From 4fcaa873ad6fbca7e87739294d5c20d161e70adc Mon Sep 17 00:00:00 2001 From: GNiendorf Date: Mon, 30 Sep 2024 11:57:55 -0400 Subject: [PATCH 1/5] DNN bug fix --- .../LSTCore/interface/alpaka/Constants.h | 4 +- .../LSTCore/src/alpaka/NeuralNetworkWeights.h | 476 +++++------ .../standalone/analysis/DNN/cut_T5_DNN.ipynb | 220 +++--- .../analysis/DNN/train_T5_DNN.ipynb | 747 ++++++++++-------- .../standalone/code/core/write_lst_ntuple.cc | 9 +- 5 files changed, 773 insertions(+), 683 deletions(-) diff --git a/RecoTracker/LSTCore/interface/alpaka/Constants.h b/RecoTracker/LSTCore/interface/alpaka/Constants.h index 47023e810cefd..ebb9f61ec9109 100644 --- a/RecoTracker/LSTCore/interface/alpaka/Constants.h +++ b/RecoTracker/LSTCore/interface/alpaka/Constants.h @@ -100,8 +100,8 @@ namespace lst { constexpr unsigned int kPtBins = 2; constexpr unsigned int kEtaBins = 10; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp[kPtBins][kEtaBins] = { - {0.4301, 0.4419, 0.4762, 0.5147, 0.347, 0.3538, 0.4061, 0.5284, 0.7186, 0.7806}, - {0.2504, 0.3244, 0.3069, 0.4128, 0.2115, 0.2105, 0.2568, 0.2435, 0.3587, 0.2614}}; + {0.4386, 0.435, 0.4638, 0.5505, 0.4071, 0.4107, 0.4946, 0.5259, 0.6527, 0.6379}, + {0.2185, 0.2754, 0.2794, 0.3838, 0.2719, 0.2004, 0.2749, 0.1378, 0.2626, 0.1656}}; } // namespace t5dnn } //namespace lst diff --git a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h index c1a5b17e8ecb0..60cbe80e17637 100644 --- a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h +++ b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h @@ -6,255 +6,255 @@ namespace lst::t5dnn { ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_0[32] = { - -0.8883630f, 0.2722728f, 0.1016291f, -1.1676310f, 1.1620101f, 0.1504889f, -1.5435610f, -0.0692244f, - -4.2153363f, -4.2260766f, -0.1551204f, 0.1450144f, -0.1899760f, -0.0228148f, -0.3506508f, -0.2040648f, - 0.1757486f, -0.1738663f, -0.2319748f, 0.0379042f, 0.0239017f, 0.0139185f, -0.1204398f, -0.1538555f, - -0.1220760f, 0.0291483f, 0.1148766f, 0.3259082f, 1.3246336f, 0.6350448f, -0.2796395f, 1.6437438f}; + 0.0838128f, -0.0951467f, -0.4538043f, -0.1200482f, 0.1697024f, 0.0734245f, 0.0477040f, -0.1409838f, + -0.1598700f, 0.1343284f, 0.7158028f, 0.5663804f, -0.0202696f, -0.0769930f, -0.1565632f, -0.4897312f, + -0.1904009f, 0.4263237f, 0.1309257f, 0.1497203f, 0.1124899f, 0.3384814f, -0.2008730f, 0.2663862f, + -0.0109372f, -0.2767799f, 0.1633506f, 0.6183366f, 2.0648596f, -0.0196532f, -0.1557027f, -0.3387919f}; ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[24][32] = { - {0.0108952f, -0.0558019f, 0.0074057f, 0.0770754f, 0.0638310f, -0.1886167f, 0.0244425f, 0.0255687f, - -0.0839688f, -0.1685015f, -0.2186795f, -0.0174607f, -0.1015779f, -0.0892610f, -0.2556714f, 0.0740566f, - 0.1856497f, -0.0237876f, 0.1624987f, -0.0158742f, -0.0523282f, 0.1812486f, 0.0906745f, -0.1455424f, - 0.1383075f, -0.1130149f, 0.0858218f, -0.0791841f, 0.0118138f, -1.0824271f, 0.0614133f, 0.0247215f}, - {-0.0615002f, 0.2489090f, -0.0765989f, -0.6762177f, -0.3041955f, 0.1424502f, 0.0689160f, -0.0613275f, - -0.7172357f, 1.3835012f, -0.1517068f, -0.0571197f, 0.0906804f, 0.0345163f, 0.1861660f, -0.2219956f, - -1.1799500f, -0.1914543f, -0.1125221f, 0.2518745f, -0.0189657f, 0.0231417f, 0.1691398f, 0.1866181f, - 0.1034780f, 0.7616542f, 0.0739528f, 0.1423306f, 0.0655985f, 2.1955757f, -0.6869692f, -0.1286531f}, - {0.0188954f, 0.0589353f, 0.0441611f, 0.0124622f, -0.0217846f, -0.0808924f, 1.5422715f, -0.0281864f, - 0.7299182f, 0.5917343f, -0.1385602f, 0.8845096f, 0.1514033f, 0.0940068f, 0.0920146f, -0.2601782f, - 2.6173670f, 0.1569604f, -0.0973214f, 0.7541708f, -0.0897320f, 0.0660505f, 0.1606622f, -0.2816422f, - 0.0126745f, 1.0140301f, -0.1677477f, 0.2257937f, -0.2216167f, 0.2004709f, 0.2658010f, 0.0230178f}, - {0.0037102f, -0.0040856f, -0.0033745f, 0.0247645f, -0.0453197f, 0.0380025f, 0.0650211f, 0.0057784f, - -0.2155162f, -0.1491260f, -0.0617472f, -0.0399091f, 0.1454746f, -0.2295472f, 0.2150548f, -0.0577407f, - 0.0278272f, 0.1403611f, 0.1048822f, 0.0695582f, 0.0677317f, 0.0190138f, 0.1438236f, -0.0218275f, - -0.0575167f, 0.0713076f, -0.2096365f, -0.0655335f, -0.0217136f, -1.4598478f, -0.0326691f, -0.0170971f}, - {0.1484707f, 0.1109349f, -0.0158722f, -0.0016825f, -0.0876299f, 0.0082290f, 0.0888775f, -0.0429260f, - 0.1259853f, 0.0993768f, -0.2736045f, 0.0806121f, -0.2051383f, 0.1111838f, 0.0865957f, -0.0863023f, - -0.1114034f, -0.1990993f, -0.0517070f, -0.0286788f, 0.1611179f, -0.1906383f, 0.0625047f, 0.1423383f, - -0.0163717f, 0.0210820f, 0.0967393f, 0.1236721f, -0.0379066f, -1.0055737f, -0.0120382f, -0.1614379f}, - {-0.1824946f, -0.0596116f, -0.0058219f, 0.0385287f, 0.0783886f, 0.1863528f, -0.1460936f, 0.0363999f, - 0.3298342f, -0.3955438f, -0.2872388f, -0.1528842f, -0.1089015f, -0.0284061f, 0.1655824f, -0.0019395f, - -0.0265265f, -0.0447154f, -0.0471948f, -0.0565421f, 0.1435290f, -0.1054043f, 0.1134707f, 0.0460618f, - -0.0419306f, -0.2606788f, -0.0930803f, 0.3043489f, -0.2224051f, 0.8101336f, 0.2280529f, 0.0905042f}, - {-0.4062535f, 0.0011132f, 0.0715293f, -0.1631242f, 0.5045123f, 0.1158295f, 1.2672585f, 0.0128002f, - 0.9749886f, 0.6644008f, 0.0588511f, 0.2597645f, 0.0826617f, 0.0304201f, 0.0491877f, -0.0417421f, - 0.6058450f, 0.1410300f, -0.0579113f, -0.1810425f, 0.0258041f, 0.1037378f, 0.1504700f, -0.5815220f, - 0.1082699f, 0.4114107f, -0.1669260f, -0.4001442f, 0.1995721f, 0.6033067f, 0.8375392f, -0.1642019f}, - {0.0135287f, -0.0026332f, 0.0017897f, -0.0059313f, 0.0168435f, -0.0612094f, 0.0148235f, 0.0056259f, - 0.0206365f, 0.0519135f, -0.2091481f, -0.0090781f, 0.0521728f, -0.1646452f, -0.0266643f, 0.0488272f, - 0.0317255f, -0.1375127f, -0.0111143f, -0.0024644f, -0.1410064f, -0.2038226f, -0.0889475f, -0.0603759f, - -0.2140266f, 0.0108557f, -0.1110831f, 0.0018316f, 0.0024465f, -0.1297473f, 0.0206129f, 0.0001504f}, - {-0.0446734f, -0.0481827f, -0.0000644f, -0.0907802f, 0.0408866f, 0.1254969f, -0.0884442f, 0.0287216f, - -0.0958106f, -0.1114444f, 0.0276019f, 0.0449458f, 0.1203778f, 0.0582529f, 0.0249243f, 0.1277847f, - 0.2518346f, 0.0713529f, 0.2172089f, -0.0349968f, 0.0299025f, -0.1614932f, 0.0635330f, 0.0560481f, - 0.0858282f, 0.1200854f, -0.1613888f, -0.0018909f, 0.0315459f, -0.8451451f, -0.0247189f, 0.1165422f}, - {-0.1108246f, -0.0979881f, 0.0290352f, 0.2774546f, 0.0573995f, 0.0363884f, -0.0892184f, -0.0465878f, - 0.1318289f, 0.3215536f, -0.1393095f, -0.3692756f, 0.0177766f, -0.1872795f, 0.2121594f, -0.2196127f, - -0.3401198f, -0.1351956f, 0.1633667f, 0.0753848f, 0.1283861f, -0.0134915f, -0.1865077f, -0.1091357f, - -0.2115083f, -0.4082200f, 0.0900386f, -0.4249704f, -0.0070692f, 0.3359871f, 0.0714354f, 0.0159194f}, - {0.2919563f, -0.0745509f, 0.0424533f, -0.0963534f, 0.3891644f, -0.0150635f, 0.9402236f, -0.0098148f, - 0.3807541f, 0.8199884f, 0.0752552f, -0.5736273f, 0.0302238f, -0.1761383f, 0.2639126f, 0.1240020f, - 0.6304978f, 0.1753901f, -0.0709855f, -0.1886917f, -0.0873328f, 0.1717738f, -0.0133103f, -0.3342271f, - 0.1545929f, 0.3053446f, -0.1997259f, -0.1582794f, 0.3504843f, 0.7467179f, 0.7306523f, 0.1273271f}, - {-0.0171152f, 0.0037321f, 0.0007980f, 0.0105450f, -0.0094624f, -0.1140742f, -0.0027083f, 0.0002720f, - -0.0100214f, -0.0124391f, -0.0861224f, 0.0035716f, -0.1691382f, 0.0821961f, 0.0151221f, -0.1814647f, - -0.0077462f, -0.0031899f, -0.0401796f, 0.0146227f, -0.1222276f, -0.0134909f, -0.0949516f, -0.2664480f, - 0.0361210f, 0.0106418f, -0.0439440f, -0.0002205f, -0.0013272f, -0.1245471f, -0.0185805f, -0.0079442f}, - {-0.0941981f, -0.0266157f, -0.0123596f, 0.0407734f, -0.0843467f, -0.1228521f, 0.1704207f, -0.0488337f, - 0.2791730f, -0.3645222f, -0.0745453f, 0.2423015f, 0.0148485f, 0.0235182f, 0.0624801f, 0.1068047f, - 0.2901192f, 0.0886823f, -0.1723743f, -0.0275929f, 0.0391243f, 0.0411268f, 0.1290681f, -0.1759204f, - -0.0721900f, 0.0455728f, 0.0093415f, -0.0929914f, 0.0484384f, 0.9907030f, 0.0455684f, 0.2849787f}, - {0.3737446f, 0.1316393f, 0.0330682f, -0.0257838f, 0.0703354f, 0.0162504f, -0.2599144f, 0.0736318f, - -0.5492234f, 0.4737330f, -0.2430853f, 0.4650336f, -0.0366798f, 0.1821429f, -0.1932727f, -0.2288319f, - 0.1105397f, 0.1029650f, 0.1065301f, -0.2186829f, -0.1311773f, 0.1034066f, 0.0922285f, 0.0942560f, - 0.0544397f, -0.4668275f, -0.0540184f, -0.3832760f, -0.2080186f, -1.4240936f, -0.0563135f, -0.3117954f}, - {0.1209704f, -0.0354431f, 0.0288724f, 0.2723322f, -0.0833552f, -0.0524306f, 0.0089383f, -0.0555679f, - 0.1248595f, 0.7426314f, 0.1564322f, 0.3485141f, 0.0658262f, -0.0407445f, -0.0981009f, 0.0143021f, - 0.2530443f, 0.1838160f, 0.0743984f, 0.1152445f, -0.0031844f, -0.1463480f, 0.1664018f, -0.2607590f, - -0.0438564f, 0.1520345f, -0.1405820f, 0.2680057f, -0.2130355f, -0.4303148f, 0.0778274f, 0.1622616f}, - {0.0102702f, 0.0005508f, -0.0009177f, -0.0178576f, 0.0051054f, -0.1311221f, 0.0187474f, 0.0006246f, - 0.0010550f, -0.0436288f, 0.1104593f, -0.0139150f, -0.1418129f, -0.1410428f, -0.4624029f, 0.1383150f, - 0.0043218f, -0.1101324f, -0.2180584f, -0.0608347f, -0.0528114f, -0.1915043f, -0.1299185f, -0.1127178f, - -0.1164140f, -0.0288585f, -0.2728926f, -0.0200188f, 0.0231223f, -0.2411360f, 0.0238826f, -0.0116007f}, - {0.1252177f, 0.0160918f, 0.0276896f, 0.0092796f, 0.1117284f, 0.0461815f, -0.0554672f, 0.0586559f, - -0.0575632f, -0.2811837f, 0.0614442f, -0.1068717f, 0.0848562f, -0.0010311f, 0.2069277f, -0.0625501f, - 0.2068450f, 0.0528085f, -0.1365632f, 0.0180558f, -0.0058707f, 0.1548912f, -0.0274235f, -0.3043585f, - -0.0762955f, 0.0406882f, 0.0827914f, 0.1381989f, 0.0067541f, -0.0184989f, 0.0291110f, -0.2224790f}, - {-0.2511629f, -0.0577511f, -0.0222883f, -0.1516615f, -0.0952450f, 0.1831719f, -0.0612934f, -0.0818583f, - -0.1823683f, 1.1805087f, -0.1216580f, -0.6484518f, 0.1801348f, -0.0065358f, -0.0541730f, -0.1760385f, - -1.0961853f, -0.0336082f, -0.1164270f, 0.2635235f, -0.0410220f, 0.1796006f, 0.1884784f, -0.1140166f, - -0.0868309f, 0.1071715f, 0.0846578f, 0.1802344f, 0.0107550f, -1.2832935f, -0.1682176f, 0.1164440f}, - {0.0241218f, -0.0160074f, 0.0077948f, -0.0055053f, -0.0227365f, -0.2026503f, -0.3016312f, 0.0311761f, - 0.0324427f, -0.4580960f, 0.0068335f, 0.1166081f, 0.0086222f, -0.0117984f, -0.1932269f, 0.0281393f, - 0.1829579f, -0.0784786f, -0.2037079f, 0.1112324f, -0.0123131f, 0.0356899f, -0.2060954f, -0.3733692f, - -0.2182784f, 0.2019520f, -0.0718310f, -0.0507200f, -0.0208256f, 0.2542212f, -0.0301813f, 0.0976803f}, - {-0.0168891f, 0.0053319f, -0.0002709f, 0.0033042f, 0.0107181f, 0.0784936f, 0.0236050f, 0.0005397f, - -0.0097595f, -0.0189279f, -0.1585627f, -0.0036594f, -0.0759674f, -0.2327807f, 0.0058472f, -0.2092704f, - -0.0004650f, -0.1816168f, -0.0522020f, 0.0012085f, -0.0055791f, -0.1190655f, -0.0682932f, -0.1688104f, - -0.0059374f, -0.0308726f, 0.1095462f, -0.0170852f, 0.0191665f, -0.2184187f, 0.0008373f, -0.0051565f}, - {1.6327580f, -0.0094266f, -0.0089329f, -0.4228083f, -0.0011535f, 0.0204122f, 0.1037322f, 0.0271053f, - 3.7967594f, -4.0711894f, 0.2936499f, 1.1713691f, 0.0700194f, 0.0209545f, -0.4363373f, 0.5554706f, - -0.5227916f, -0.0976525f, -0.2268672f, -0.0027372f, 0.1269471f, 0.0435933f, -0.1629809f, 0.1474882f, - 0.0194251f, 0.2221913f, -0.0553151f, -0.5447453f, 0.0217051f, 0.8906290f, -0.0360627f, -0.0672795f}, - {1.7066834f, -3.8333359f, -0.0933308f, 2.3427331f, 2.0429947f, -0.1213537f, 2.9638360f, 3.2452257f, - -0.8862939f, -0.3055574f, -0.1940480f, -1.3249305f, 0.0650838f, 0.1342920f, -0.6594582f, 0.1254523f, - -1.1303719f, 0.1124259f, -0.3136787f, -2.8443773f, -0.2119850f, -0.1128373f, -0.0644380f, -0.0582628f, - -0.0378422f, 1.3442760f, 0.0251862f, -0.8775268f, -2.1283085f, -2.0021296f, -3.0828383f, 3.0811334f}, - {-1.3902975f, 1.9841975f, 3.1912220f, -2.7655022f, -3.5196962f, 0.0271728f, -1.4541447f, 0.5155549f, - 0.9575055f, 0.8931092f, -0.0134220f, 1.5635871f, -0.1737274f, -0.0654143f, -0.1803205f, -0.3723404f, - 0.0832400f, 0.0450963f, 0.0165798f, 2.8168371f, 0.1440067f, 0.1102833f, -0.0606193f, 0.4459202f, - -0.1559901f, -2.9070737f, -0.2404985f, 3.2376831f, 3.0134432f, 2.2161815f, 2.5410390f, -1.8319596f}, - {0.0761088f, 1.8049548f, -3.1359518f, 1.2784467f, 0.9275237f, -0.0013598f, -1.2583277f, -3.6751618f, - -0.0887910f, -0.6849454f, -0.0667926f, 0.3843031f, -0.0160612f, 0.0486224f, -0.1632665f, -0.1474205f, - 0.1861823f, -0.1488994f, -0.3063696f, 0.2119907f, -0.0619995f, 0.0003783f, -0.0552438f, -0.7914191f, - -0.0211309f, 1.4954969f, 0.0894019f, -2.0494809f, -1.5124065f, -0.9326919f, 0.5190022f, -1.9568784f}, + {-0.0482477f, 0.0348692f, -0.6279378f, 0.2042783f, -0.0943811f, -0.0019772f, 1.1357815f, 0.1307896f, + 0.1233541f, -0.0307808f, 0.4762143f, 2.3105178f, -0.1138477f, -0.1789541f, 0.0728833f, 0.0368915f, + 0.1061521f, -0.0356611f, -0.0947667f, -1.7470065f, -0.0434104f, 0.3295992f, -0.6358198f, -0.2336755f, + 1.8262464f, 2.1567848f, -0.4115292f, 0.0250860f, -1.4501210f, -0.5921007f, 0.0049636f, 0.1055770f}, + {-0.0124859f, -0.1331200f, 0.7014464f, -0.1568218f, 0.0331185f, -0.1542812f, -0.1333411f, 0.0127694f, + 0.2013001f, 0.0947020f, -1.4170154f, -0.1189228f, -0.0019860f, 0.1502178f, 0.1740465f, 0.0477101f, + 0.1527060f, -1.3259488f, -0.1455328f, 0.1777444f, 0.1768609f, -0.1144335f, 1.7819276f, -2.2551980f, + 1.8525611f, 1.0002149f, 0.7666884f, 0.5857834f, 0.0189218f, 0.0130888f, 0.0520591f, 0.1602217f}, + {0.0089779f, -0.0046051f, 5.9403129f, -0.0760328f, 0.0868518f, -0.1175489f, 2.4591918f, -0.2272298f, + -0.0336123f, -0.1102009f, 0.4388122f, -0.1027233f, 0.1439395f, 0.0260359f, -0.1548838f, 0.5144961f, + -0.0036697f, -0.0587723f, 0.1452203f, -0.7863321f, -0.0143598f, -0.7969503f, -1.4715855f, 1.8720422f, + -0.5365977f, 1.2278979f, 0.1856042f, 0.0087406f, -0.4033080f, 0.5311748f, -0.0624044f, -0.0473539f}, + {0.0122028f, -0.2384637f, -0.0065890f, -0.1581197f, -0.2024812f, -0.0350186f, -0.1173539f, -0.2257327f, + -0.1062854f, -0.2007362f, -0.0033018f, 0.0028686f, 0.0887160f, -0.0825604f, -0.1360626f, 0.0014523f, + -0.1611109f, 0.0363360f, -0.1258196f, 0.0118614f, 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0.1805567f, 1.5299331f, 0.1614436f, + 0.1030123f, -0.1750580f, -0.0790422f, 0.1303435f, -0.0649011f, -0.1773880f, -0.0186087f, -0.4794982f, + -6.5970545f, 0.9689565f, -0.0698152f, 0.2821720f, -0.0943349f, 0.6448916f, -0.5019226f, -2.0137179f, + -1.9753901f, 2.3883131f, -0.6336402f, -0.2053137f, -0.1029351f, -3.6074843f, -0.0803197f, -0.3316223f}, + {-0.1005511f, -0.0575567f, -0.2696874f, -0.1879897f, -0.1549900f, -0.1585334f, -0.2431465f, 0.0235179f, + -0.0953478f, -0.0292804f, -0.0737122f, 0.1184362f, -0.1744826f, -0.1343563f, -0.1822797f, 0.3344489f, + 0.1855060f, -0.0245743f, 0.0700775f, 0.3525808f, -0.1741875f, 0.0696346f, 0.0178112f, 0.5294384f, + -0.0518728f, 0.0456072f, -0.0179709f, -0.3133727f, -0.0621307f, 0.0000746f, -0.1714160f, 0.0660782f}, + {0.1368161f, 0.1452735f, -1.0996736f, -0.1370980f, 0.0454142f, -0.2010279f, -0.3923539f, 0.0751393f, + 0.1552261f, -0.1690396f, -0.7361290f, 2.2066221f, 0.1860844f, -0.0471949f, -0.1001091f, -0.0785898f, + 0.3381744f, 0.2242116f, 0.0059051f, -1.1038984f, -0.0776049f, -0.9301085f, -0.2579322f, 1.0752188f, + 0.0572336f, -0.4376161f, 0.4391319f, -0.0920684f, -0.3661392f, -0.0926421f, 0.1151504f, 0.0568435f}, + {0.1527036f, 0.0719917f, 1.6668177f, 0.1541860f, 0.1094950f, -0.1874002f, 0.2204740f, 0.0663509f, + 0.0846452f, 0.0338055f, 2.6957841f, 2.2497787f, -0.1595391f, 0.1440347f, -0.0457316f, 3.2653592f, + 0.3555845f, 0.0221110f, 0.1668054f, 1.9366297f, 0.0111978f, -3.0021029f, 2.4726274f, -1.7558607f, + -1.1594486f, 0.0734959f, -2.3178720f, -0.0162731f, 1.3418909f, 0.5486869f, -0.1103047f, -0.0437041f}, + {0.0302621f, -0.2280054f, -1.1536891f, 0.0624857f, -0.1923327f, 0.0903575f, 2.9342484f, -0.1453349f, + -0.1462534f, 0.1626090f, -2.3898613f, -2.0969083f, 0.1033970f, -0.0779580f, 0.1544486f, 0.0079555f, + -0.4402241f, 2.7067254f, -0.0814941f, -2.0344024f, -0.1009802f, 2.3716676f, -2.8162944f, 1.7359691f, + 1.1204399f, -0.0530971f, 3.1334810f, 2.6716938f, -1.5494148f, -0.1560390f, 0.1592183f, -0.5042306f}, + {-0.1881559f, 0.1079059f, 0.8104222f, 0.0100862f, 0.0803289f, -0.1001732f, -3.0945404f, -0.1863656f, + 0.1092684f, -0.0122846f, 0.0211765f, 0.0882070f, 0.0887852f, 0.1788233f, 0.1035344f, -3.2046664f, + 0.1207470f, -2.7201638f, -0.1267492f, 0.3226216f, 0.0849743f, 0.7428753f, 0.0196231f, 0.0904721f, + 0.3495182f, -0.2576671f, -0.4849421f, -2.6337876f, 0.4418600f, -0.0151254f, 0.1613163f, -0.0374872f}, }; ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_2[32] = { - 0.5574176f, -0.0158378f, 0.6081951f, -0.2482200f, 1.4885056f, -0.9065233f, -0.8430861f, -0.2685009f, - 1.5197985f, 0.2150985f, 3.3223093f, -0.2417838f, 1.1797755f, -1.0702871f, 0.2581681f, 0.2042585f, - 1.6163963f, -0.2037005f, 1.4874136f, -1.5563414f, 0.6079807f, -0.2871149f, -1.0653205f, -0.2456795f, - 0.6591544f, -0.2727272f, -1.3100427f, 0.2261839f, -0.2026590f, -0.1331568f, 1.0972227f, -1.3647238f}; + -0.1445483f, -0.7468296f, 2.0087461f, 0.9193060f, 0.1838548f, -0.2429122f, -0.3490699f, 0.0500740f, + -2.4078236f, -0.1702676f, 0.2733943f, 0.2180888f, -0.2089010f, 1.3755207f, 0.5235071f, -0.4656681f, + -0.3255171f, 0.0164221f, 1.8814882f, 0.3419669f, -0.1090209f, 1.3948971f, 0.0137390f, -1.0124286f, + -0.5950485f, 0.8902460f, 0.9638857f, -3.0627401f, 1.3535937f, 0.7002685f, -0.3063155f, 0.1972668f}; ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_2[32][32] = { - {0.0451835f, -0.2068697f, -0.1455017f, -0.2000958f, 0.1953720f, -0.0868650f, 0.0185502f, -0.1359519f, - -0.0893761f, -0.2565004f, -0.5655026f, 0.0635366f, -0.3550099f, 0.1558128f, 0.0306805f, 0.3792097f, - 1.8771213f, -0.2383017f, -0.2512103f, -0.8434259f, 0.1358105f, -0.1323305f, -0.0145602f, -0.1016658f, - 1.3442085f, 0.1507007f, 0.9711186f, -0.0945338f, 0.2191976f, -0.1611731f, -1.2007302f, 1.1104904f}, - {-1.3594574f, -0.0378055f, 1.5503316f, -0.1488620f, -1.4691522f, -2.3224826f, 1.1786927f, -0.1508618f, - -4.2288308f, 1.2377062f, 0.2050966f, -0.0741773f, -1.5114043f, -2.2442327f, 2.2897842f, -1.3733587f, - 0.5636392f, -0.1783939f, 2.4285316f, 0.1796620f, -0.6498010f, -0.2876171f, -1.3547685f, -0.0461334f, - -0.9012266f, -1.3740121f, -0.0379446f, -1.0507129f, -1.4431050f, 0.0294942f, -0.3823238f, -2.1220925f}, - {-1.1411303f, -0.2431736f, -9.9841690f, -0.2129669f, 0.6981345f, -2.1666744f, -10.6869678f, 0.0611885f, - 3.2361872f, -0.6304339f, -0.5848778f, 0.1157968f, 0.2787877f, 0.9803044f, -10.3797789f, -0.9443294f, - 2.5538800f, -0.0633187f, -10.9630623f, -1.1149275f, -3.8756664f, -0.1332296f, 1.7392989f, 0.0613354f, - 0.6508241f, -1.1899891f, 1.0534809f, -0.2019602f, -0.1229644f, 0.1375276f, -4.7887559f, 0.5126888f}, - {0.7176909f, -0.0214850f, 0.5826513f, -0.1142717f, 0.9390327f, 1.1849338f, 0.7845815f, -0.1581640f, - 0.2477809f, -1.3536477f, -0.6782305f, -0.1947700f, 1.1930919f, 0.5593164f, 0.7056133f, 1.2611432f, - 1.3779073f, -0.1165161f, 0.4806055f, 0.1155640f, 0.0619001f, -0.2589326f, 0.7354722f, 0.0066148f, - 2.0458858f, 0.8164052f, -0.3876709f, 0.5323008f, 1.1789955f, -0.0268815f, 0.3633842f, 0.4662197f}, - {1.9750929f, -0.1468043f, 1.7405676f, -0.1992684f, 2.3235869f, 1.7946367f, 2.0761003f, 0.0511989f, - 1.1833738f, 2.0538511f, 0.6360483f, -0.0645854f, 2.5635352f, 1.0803665f, 1.4006170f, 1.9379770f, - 0.2737490f, 0.0391885f, 1.1377318f, 1.6247250f, 0.5832859f, -0.0003641f, 2.5831740f, -0.0072090f, - -0.7454425f, 1.8515724f, 0.1098597f, 1.7219243f, 1.6892309f, -0.0324973f, -0.4168116f, 0.6863616f}, - {0.0752462f, -0.1662275f, 0.1713349f, 0.0999224f, 0.0412468f, 0.0073274f, 0.0941098f, -0.1631816f, - -0.0020100f, 0.1488188f, -0.0753984f, -0.0390763f, -0.0592567f, -0.0363634f, 0.0577131f, 0.0113215f, - 0.1331281f, 0.0892927f, 0.0323133f, 0.1725931f, -0.0647239f, 0.0597323f, -0.0323511f, 0.0390394f, - -0.0969601f, 0.0892076f, 0.1507631f, 0.0258195f, 0.1564926f, 0.0089697f, 0.1074804f, 0.0107034f}, - {0.4456752f, -0.0011744f, -0.2483061f, -0.0932769f, 0.6201064f, 0.7728632f, -0.5952806f, -0.3104276f, - 0.3310406f, -1.1115030f, 0.6661707f, -0.0648445f, 0.7863559f, 0.9962517f, -0.3727600f, 0.7995458f, - -2.2710021f, -0.1607994f, -0.1370830f, -0.7545819f, 1.3058670f, -0.2174886f, 1.0568154f, -0.0152477f, - 0.6835587f, -0.2345589f, 1.3725461f, 0.6000165f, 0.4146734f, 0.0093098f, -0.5216421f, 0.5323782f}, - {1.1226186f, -0.2139718f, -1.6810553f, 0.0577140f, -0.6599731f, -0.0516810f, -1.8051751f, 0.0743359f, - 0.6035778f, -0.0772341f, -1.3107933f, -0.1289809f, -0.2254264f, 1.0665301f, -1.7961371f, 0.6447040f, - -1.7851267f, 0.0311611f, -1.2731884f, 2.4128528f, 2.7963874f, -0.0794195f, 0.4108718f, -0.0243468f, - 1.0090519f, 2.2392919f, 1.3879771f, 0.5621879f, 1.1172789f, -0.1387018f, -17.4072647f, -1.8168215f}, - {-0.2358715f, 0.0266361f, 1.0553420f, -0.1551773f, -0.2215100f, 0.0349971f, 1.5580076f, -0.2738504f, - 0.6694541f, 1.6789085f, -1.1405956f, -0.2484834f, 0.3041122f, -1.2395017f, -0.2135831f, -0.0818596f, - -0.7755322f, -0.1064595f, -0.8498001f, 0.4382845f, 0.7620107f, -0.1416551f, -0.8304774f, -0.0960256f, - -0.6896841f, 1.4835662f, -0.4668809f, 0.4376302f, 0.4361478f, -0.0508427f, -0.8557618f, -0.2787146f}, - {-0.1752244f, 0.0344409f, 0.9591160f, -0.0430410f, 0.8798326f, 0.1316557f, 1.5167421f, -0.0329250f, - -1.0232353f, 0.9973637f, -1.3765392f, -0.0724326f, -0.4358305f, 0.5937796f, -0.3354949f, -0.1040113f, - 0.1141699f, -0.0444658f, -0.9240037f, 1.5555140f, 0.0304396f, -0.1770578f, -0.2080073f, 0.0466823f, - -0.2886449f, -0.9460831f, -0.8555308f, -0.3045022f, 1.1774287f, -0.1700008f, -0.0553386f, -1.2852336f}, - {-0.0790898f, -0.1598882f, 0.0326521f, -0.1253484f, -0.0047360f, 0.0447811f, 0.0268714f, 0.0004109f, - -0.0080649f, -0.0259730f, -0.0404877f, -0.0908946f, -0.0501967f, 0.0480490f, -0.1847470f, -0.1234237f, - 0.0289896f, 0.1667374f, -0.0792361f, -0.1580346f, -0.1543660f, 0.0649026f, 0.1114447f, -0.0286203f, - -0.2190961f, -0.0279833f, 0.2067135f, 0.1703904f, 0.0839379f, -0.0782984f, 0.1504731f, 0.2650901f}, - {-0.1022112f, -0.1583978f, -0.1569724f, 0.0443657f, -0.0407184f, 0.2730022f, -0.2490653f, 0.1519162f, - -0.3305249f, -0.2914465f, 0.3938576f, -0.2624281f, 0.4522434f, 0.2708573f, -0.0813838f, -0.0865780f, - 0.0955285f, 0.0542366f, 0.1400105f, -0.2534869f, -1.0018730f, -0.0617124f, -0.0646572f, -0.0932494f, - -1.5292253f, -0.4963901f, 0.6442720f, -0.1657045f, 0.1778805f, -0.2607354f, 0.7873000f, 0.9417700f}, - {-0.0454490f, 0.1027239f, -0.1431503f, 0.1526977f, 0.1726041f, 0.0994884f, -0.1142145f, -0.0938273f, - 0.1132145f, 0.1006262f, -0.1241813f, 0.0608377f, 0.0825679f, -0.0577918f, 0.1231958f, 0.0046255f, - -0.0308543f, 0.1097951f, -0.1023807f, 0.1021969f, -0.0113969f, 0.1492857f, -0.0903977f, -0.0387435f, - 0.0006103f, -0.0726079f, -0.0771527f, 0.0286305f, -0.1010148f, 0.0110340f, 0.0899370f, 0.1413733f}, - {0.0709853f, -0.1947442f, 0.0601056f, 0.0789893f, -0.0056179f, 0.0905663f, -0.1644098f, 0.0984766f, - -0.0773362f, -0.0968683f, -0.1514594f, 0.0603267f, 0.1234915f, 0.0052009f, 0.0034624f, 0.0539703f, - 0.1124334f, -0.1365515f, 0.0879912f, -0.0336592f, 0.1013220f, -0.1413363f, 0.0456143f, 0.0221163f, - -0.0859804f, 0.1385853f, 0.0171008f, 0.0690550f, -0.0762036f, -0.0711688f, -0.1310947f, 0.1505480f}, - {-0.1408519f, -0.0985669f, -0.4713727f, -0.0836342f, 0.3837579f, -0.0890013f, -0.5001497f, -0.0737510f, - -0.0267395f, 0.4284769f, 0.0583974f, -0.1051440f, 0.0650211f, 0.0168218f, 0.1549876f, -0.1499830f, - 0.3692394f, -0.0137580f, 0.0588887f, -0.2984504f, -0.2102986f, -0.0082258f, -0.0110932f, 0.0881037f, - -0.1576584f, -0.4554820f, 0.1673794f, 0.0751528f, 0.0076324f, -0.0081963f, -0.0437056f, -0.0525045f}, - {0.1140012f, -0.0686564f, 0.1224050f, -0.1471748f, 0.2762693f, 0.3053007f, 0.3006799f, -0.2275705f, - 0.0599227f, 0.0048827f, 0.0994446f, 0.0026369f, 0.1686888f, 0.0432014f, 0.1081433f, 0.0237865f, - 0.0692518f, -0.0516704f, 0.1152125f, 0.3506622f, -0.0661865f, 0.1081390f, 0.1834167f, 0.0531238f, - 0.2800074f, 0.1734550f, 0.1220211f, 0.0661175f, 0.0758285f, -0.0893979f, 0.0395964f, 0.0939213f}, - {0.1420665f, -0.3169957f, 0.0839309f, -0.0168372f, 0.0778442f, -0.1946857f, 0.0558012f, -0.0876020f, - 0.0863991f, 1.0993744f, -0.2597762f, -0.0869371f, -0.0839031f, -0.1715296f, 0.0025011f, -0.1712964f, - 0.3512362f, 0.0189788f, -0.0573796f, -0.0347028f, -0.1767404f, -0.0070027f, -0.6443253f, -0.2219438f, - 0.3929335f, 0.5295364f, -0.8351922f, -0.3400496f, 0.1396646f, -0.2626049f, -0.1126374f, 0.0275972f}, - {-0.0864766f, 0.0741733f, -0.0912781f, 0.1333112f, -0.1264973f, 0.0065670f, -0.0481090f, 0.1516521f, - -0.0807727f, 0.0814792f, -0.0216370f, 0.0258632f, 0.0509399f, -0.0091230f, -0.0751342f, 0.1567677f, - -0.0265132f, 0.1476690f, 0.1261382f, -0.1349490f, -0.0232035f, 0.0112255f, 0.1082215f, -0.1584813f, - 0.1069531f, 0.1387598f, -0.0159275f, 0.1145359f, 0.1729458f, 0.0530649f, -0.1047219f, 0.0008151f}, - {0.0713862f, 0.0920260f, -0.1202323f, 0.0459048f, -0.0392854f, -0.3180378f, -0.0698610f, -0.0604497f, - 0.0532774f, -0.1501577f, -0.1968597f, -0.1322312f, 0.1708560f, 0.1024350f, -0.0169092f, -0.1166248f, - 0.0559283f, 0.1314427f, -0.0450807f, 0.1691803f, -0.0998406f, -0.0646984f, 0.0894593f, -0.0912665f, - 0.0098662f, -0.1937838f, 0.0295222f, 0.0188647f, -0.1653765f, -0.0547333f, -0.0908089f, -0.0021439f}, - {-1.8159485f, -0.3067101f, -0.3765004f, -0.1251000f, -1.3317541f, -2.3440614f, -0.1617921f, -0.0444327f, - -0.4739833f, 2.2853401f, 0.1629837f, -0.2932331f, -1.3232881f, -1.2355529f, -0.1309181f, -1.3150527f, - -2.1903090f, 0.0195323f, -0.1407724f, 0.0458571f, 0.2070698f, -0.1987318f, -2.2738180f, -0.0896101f, - -0.9469178f, -0.7119282f, -1.6207448f, -1.0214741f, -1.1536974f, -0.2514080f, -0.1167398f, 0.9429289f}, - {-0.1542182f, 0.0659505f, 0.1109212f, 0.1563802f, -0.1307439f, -0.0357025f, 0.0073372f, 0.1561993f, - 0.0392061f, 0.0190965f, -0.1377096f, 0.1082494f, 0.0649651f, 0.1462047f, -0.1334721f, 0.0880377f, - 0.0459550f, -0.0122504f, 0.1307036f, -0.0226880f, -0.0663698f, -0.1284503f, -0.0198257f, -0.0188580f, - -0.0746744f, -0.0565124f, -0.1445807f, -0.1606328f, -0.0947341f, 0.0305629f, -0.1206766f, -0.0899025f}, - {-0.0935756f, 0.1458915f, 0.0972818f, -0.0044439f, -0.1255520f, -0.0616250f, -0.1680417f, 0.1506450f, - -0.0892932f, -0.0312009f, 0.0805274f, 0.1754372f, -0.0100930f, -0.0462486f, -0.0982281f, 0.0987456f, - -0.1486668f, 0.1727460f, 0.1047921f, 0.0270238f, -0.1810919f, -0.1225601f, -0.1430596f, 0.0426471f, - 0.0944300f, 0.0663908f, -0.0355847f, -0.0608823f, -0.1049382f, 0.1155818f, -0.0703957f, -0.0667467f}, - {0.0014743f, 0.0625537f, -0.0153991f, 0.0894012f, -0.0495224f, -0.1219010f, 0.0508541f, -0.0006137f, - -0.1596925f, -0.0519368f, 0.1667078f, 0.1695615f, -0.1505568f, -0.0576535f, -0.0569532f, -0.1574844f, - 0.0967731f, 0.1184849f, 0.0714454f, -0.1401346f, -0.0571446f, -0.0803483f, 0.0991009f, -0.1015021f, - -0.1301081f, -0.1472718f, 0.0836138f, 0.0394287f, 0.0867134f, -0.0392487f, 0.0052088f, 0.0203310f}, - {-0.0070683f, -0.0920968f, 0.0115485f, -0.1001143f, 0.0974420f, -0.1220813f, -0.1084694f, -0.0494320f, - 0.0978433f, 0.2186576f, -0.0817377f, -0.2244482f, 0.3404714f, 0.2718672f, 0.1289811f, 0.0542469f, - 0.4501916f, -0.1821622f, 0.0854033f, 0.0899855f, 0.0104517f, -0.0723236f, -0.1611435f, 0.0209299f, - -0.2831168f, 0.0325928f, 0.3347049f, 0.0242399f, 0.1174182f, 0.0740212f, -0.0167192f, -0.4196944f}, - {-0.1464287f, 0.1311725f, 0.0786641f, 0.0676314f, -0.0618948f, -0.0469531f, -0.1069913f, -0.0100426f, - 0.1921959f, 0.0084023f, -0.1202803f, -0.0467913f, 0.0910331f, 0.0736183f, -0.0494701f, -0.1338117f, - -0.1109077f, 0.1619087f, -0.1852058f, 0.1145653f, -0.1820068f, 0.1701601f, -0.0044400f, -0.0957667f, - -0.0446280f, 0.0796393f, 0.1332078f, 0.1505477f, 0.0210109f, 0.1668117f, 0.0451417f, 0.0552435f}, - {0.8828899f, -0.1591612f, 0.7553288f, 0.0638267f, 0.5764531f, 1.0427947f, 0.9936816f, 0.0407240f, - -0.0183244f, -0.0583328f, -0.7137405f, 0.0469051f, 0.2914166f, 0.4258071f, 0.6765603f, 0.6176807f, - -2.0113719f, -0.2027455f, 0.4470194f, 0.9834553f, -0.1033722f, 0.0387962f, 0.8811535f, -0.0423020f, - -0.5021724f, 0.3953238f, 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-2.6154006f, 0.0234051f, 1.2700831f, 7.4880581f, 0.0808564f, -0.2905704f, 2.5056512f, 0.6301501f, + -0.1501096f, -0.3806727f, 6.1768942f, -0.2920033f, -0.1472883f, -4.7388387f, 5.9521456f, -2.7825656f, + 6.7927275f, 4.6885090f, -2.2919989f, -14.2969074f, 8.4207087f, -6.5338097f, 10.7289143f, -0.5509484f}, + {-0.1605100f, -2.7664142f, 0.1855993f, -1.0547638f, -0.1559302f, 2.3516369f, -0.0384686f, -0.0421699f, + 1.2288936f, -0.1374861f, -0.8669472f, 0.4701964f, -0.2579978f, -1.8223869f, 1.9952940f, -0.8765189f, + 0.0131214f, -2.6571078f, 0.8116180f, 1.8054768f, -0.0726431f, 0.5150636f, 0.9614642f, -1.3712974f, + -0.4931213f, 1.2799782f, -1.6611663f, 0.4448277f, 0.0846980f, 0.5529866f, -0.0449053f, -0.4308737f}, + {0.1551290f, 0.1396936f, -0.0968593f, -0.6554329f, -0.3199727f, -0.3910193f, -1.2765325f, -0.2355604f, + -2.7272804f, -0.2628681f, -8.6636467f, 0.3586271f, -0.1602254f, 0.3281886f, 0.0922947f, -5.1625209f, + 0.0448225f, 0.4242346f, 2.4615154f, -0.0818966f, 0.0419294f, -15.5337925f, 0.5842085f, -10.8753138f, + 0.9091803f, -1.7685370f, 2.0125909f, 0.6737279f, -1.4667115f, -21.1957169f, 1.2548140f, -4.0338879f}, + {-0.0536758f, 0.0545643f, -1.1698856f, 1.8836620f, -0.1045039f, -0.6019166f, 1.6155248f, -0.2697649f, + 1.7763392f, -0.0670235f, 0.2441206f, 0.9861935f, -0.1286846f, -1.2111757f, 1.0097101f, 0.7566754f, + -0.0151789f, 0.4013853f, -0.7427294f, -0.2104458f, -0.2336174f, 0.9817276f, 0.6893633f, 0.6530916f, + 0.3608560f, -0.2619474f, -0.7111229f, 0.4748580f, 0.4240529f, 0.5841066f, 1.6432278f, 1.4601723f}, + {0.0447264f, -0.2559440f, -1.3964423f, 0.9887335f, 0.0025529f, -0.3780882f, 1.8015873f, -0.0102492f, + 0.6349514f, -0.0652549f, -11.5391188f, 0.2737688f, -0.0360017f, -1.0004791f, -0.1981463f, 1.8440535f, + 0.0371480f, -0.2467426f, -0.6359706f, -0.3593787f, -0.2423154f, -0.3130093f, 0.1481221f, 0.9980756f, + 0.4402424f, -0.3383209f, 0.1110670f, 0.1886230f, 0.0224243f, -0.5134141f, 0.2146088f, 1.0987345f}, + {-0.0964645f, -0.1176648f, -0.1362459f, -0.0702352f, -0.1269714f, 0.0788451f, 0.1349706f, -0.0130557f, + -0.1754480f, -0.0714703f, -0.1319758f, 0.1684060f, -0.0253212f, 0.0238707f, 0.1894423f, 0.0064412f, + 0.0870870f, 0.1185605f, 0.0594264f, -0.0779627f, 0.1756235f, -0.0078578f, -0.0954016f, 0.0852176f, + 0.0516563f, 0.0918788f, -0.0913408f, -0.1448558f, -0.0154965f, -0.0207913f, -0.0115856f, 0.0777761f}, + {-0.1909802f, -0.2315316f, -0.1041895f, -0.1264509f, -0.0980112f, -0.0968891f, 0.1441685f, -0.1048653f, + -0.0836623f, 0.0766368f, -0.3555300f, 0.1044188f, 0.0242796f, 0.2485393f, 0.1461953f, 0.0435378f, + 0.0990156f, 0.0944424f, 0.0022342f, -0.4283264f, -0.1376556f, 0.0390784f, -0.1326635f, -0.0682079f, + 0.0576475f, -0.0730707f, 0.0102653f, -0.0201001f, 0.0372333f, 0.0019655f, -0.0474310f, -0.0362500f}, }; - ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {0.7004032f}; + ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {-1.0653121f}; ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_4[32][1] = { - {-1.1784149f}, {0.0064484f}, {-1.7084764f}, {-0.0371976f}, {1.1174239f}, {0.5888167f}, {1.7407018f}, - {0.0329500f}, {-0.5985520f}, {-0.2985888f}, {-0.7626860f}, {-0.0314763f}, {1.0361871f}, {-0.6576214f}, - {-1.8324782f}, {-1.2968894f}, {-0.3412971f}, {-0.0388680f}, {1.8274761f}, {0.3623946f}, {0.4834338f}, - {-0.0507033f}, {0.5347728f}, {0.0183969f}, {0.5529681f}, {0.5601515f}, {0.5157045f}, {-1.1073836f}, - {-0.9019504f}, {-0.0337475f}, {-0.6909413f}, {0.2963411f}, + {0.0599457f}, {2.4589522f}, {0.5886621f}, {0.5058215f}, {-1.1172724f}, {1.8007317f}, {0.5281580f}, + {0.5044137f}, {0.5756003f}, {0.1070907f}, {0.2529266f}, {-1.0334966f}, {0.0026251f}, {-0.9314435f}, + {-1.6016932f}, {0.3742798f}, {-0.0195212f}, {-2.0840223f}, {0.9543520f}, {-3.1566474f}, {0.1217308f}, + {1.1695908f}, {0.8822579f}, {-1.2197880f}, {-2.0250206f}, {0.7175051f}, {0.2349268f}, {0.5229219f}, + {0.5292190f}, {-1.7123013f}, {0.8434106f}, {-1.0713193f}, }; } //namespace lst::t5dnn diff --git a/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb b/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb index e2e946d23ad74..e67564cee946c 100644 --- a/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb +++ b/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb @@ -4,22 +4,12 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_70812/4151003746.py:4: DeprecationWarning: \n", + "/tmp/ipykernel_904678/3231266229.py:5: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", @@ -28,57 +18,72 @@ " \n", " import pandas as pd\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z max: 267.2349853515625, R max: 110.10993957519531\n" - ] } ], "source": [ "import os\n", "import uproot\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import awkward as ak\n", "\n", - "# Input ROOT file\n", - "file_path = \"PU200RelVal_Choose_p95_1000.root\"\n", - "with uproot.open(file_path) as file:\n", - " tree = file[\"tree\"]\n", - "\n", - " t5_innerRadius = np.concatenate(tree[\"t5_innerRadius\"].array(library=\"np\"))\n", - " t5_bridgeRadius = np.concatenate(tree[\"t5_bridgeRadius\"].array(library=\"np\"))\n", - " t5_outerRadius = np.concatenate(tree[\"t5_outerRadius\"].array(library=\"np\"))\n", - " t5_eta = np.concatenate(tree[\"t5_eta\"].array(library=\"np\"))\n", - " t5_pt = np.concatenate(tree[\"t5_pt\"].array(library=\"np\"))\n", - " t5_isFake = np.concatenate(tree[\"t5_isFake\"].array(library=\"np\"))\n", - "\n", - " # Use these indices to extract two from each below\n", - " t5_t3_idx0 = np.concatenate(tree[\"t5_t3_idx0\"].array(library=\"np\"))\n", - " t5_t3_idx1 = np.concatenate(tree[\"t5_t3_idx1\"].array(library=\"np\"))\n", + "def load_root_file(file_path, branches=None, print_branches=False):\n", + " all_branches = {}\n", + " with uproot.open(file_path) as file:\n", + " tree = file[\"tree\"]\n", + " # Load all ROOT branches into array if not specified\n", + " if branches is None:\n", + " branches = tree.keys()\n", + " # Option to print the branch names\n", + " if print_branches:\n", + " print(\"Branches:\", tree.keys())\n", + " # Each branch is added to the dictionary\n", + " for branch in branches:\n", + " try:\n", + " all_branches[branch] = (tree[branch].array(library=\"np\"))\n", + " except uproot.KeyInFileError as e:\n", + " print(f\"KeyInFileError: {e}\")\n", + " # Number of events in file\n", + " all_branches['event'] = tree.num_entries\n", + " return all_branches\n", "\n", - " # Each of these is two features below this line, index with above.\n", - " t3_0_r = np.concatenate(tree[\"t5_t3_0_r\"].array(library=\"np\"))\n", - " t3_0_z = np.concatenate(tree[\"t5_t3_0_z\"].array(library=\"np\"))\n", - " t3_0_eta = np.concatenate(tree[\"t5_t3_0_eta\"].array(library=\"np\"))\n", - " t3_0_layer = np.concatenate(tree[\"t5_t3_0_layer\"].array(library=\"np\"))\n", - "\n", - " t3_2_r = np.concatenate(tree[\"t5_t3_2_r\"].array(library=\"np\"))\n", - " t3_2_z = np.concatenate(tree[\"t5_t3_2_z\"].array(library=\"np\"))\n", - " t3_2_eta = np.concatenate(tree[\"t5_t3_2_eta\"].array(library=\"np\"))\n", - " t3_2_layer = np.concatenate(tree[\"t5_t3_2_layer\"].array(library=\"np\"))\n", + "branches_list = [\n", + " 't5_innerRadius',\n", + " 't5_bridgeRadius',\n", + " 't5_outerRadius',\n", + " 't5_pt',\n", + " 't5_eta',\n", + " 't5_phi',\n", + " 't5_isFake',\n", + " 't5_t3_idx0',\n", + " 't5_t3_idx1',\n", + "]\n", "\n", - " t3_4_r = np.concatenate(tree[\"t5_t3_4_r\"].array(library=\"np\"))\n", - " t3_4_z = np.concatenate(tree[\"t5_t3_4_z\"].array(library=\"np\"))\n", - " t3_4_eta = np.concatenate(tree[\"t5_t3_4_eta\"].array(library=\"np\"))\n", - " t3_4_layer = np.concatenate(tree[\"t5_t3_4_layer\"].array(library=\"np\"))\n", + "# Hit-dependent branches\n", + "suffixes = ['r', 'z', 'eta', 'phi', 'layer']\n", + "branches_list += [f't5_t3_{i}_{suffix}' for i in [0, 2, 4] for suffix in suffixes]\n", "\n", - "# Max value to normalize the z, r variables by.\n", - "z_max = np.max([t3_4_z, t3_2_z, t3_0_z])\n", - "r_max = np.max([t3_4_r, t3_2_r, t3_0_r])\n", + "file_path = \"1000_p95_final.root\"\n", + "branches = load_root_file(file_path, branches_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z max: 267.2349853515625, R max: 110.10993957519531\n" + ] + } + ], + "source": [ + "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n", + "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n", "\n", "print(f'Z max: {z_max}, R max: {r_max}')" ] @@ -89,29 +94,68 @@ "metadata": {}, "outputs": [], "source": [ - "import torch\n", - "\n", - "# Function to fetch T3 features for given T5 indices\n", - "def fetch_t3_features_for_t5(t3_feature, t5_t3_idx):\n", - " return t3_feature[t5_t3_idx]\n", + "features_list = []\n", "\n", - "# Initialize the features list without the T5 standalone variables at the beginning\n", - "features = []\n", + "for event in range(branches['event']):\n", + " # Determine the number of elements in this event\n", + " num_elements = len(branches['t5_t3_idx0'][event])\n", "\n", - "# Now, extract T3 features indexed by t5_t3_idx0 and t5_t3_idx1\n", - "for t3_feature in [t3_0_eta, t3_0_z/z_max, t3_0_r/r_max, t3_0_layer,\n", - " t3_2_eta, t3_2_z/z_max, t3_2_r/r_max, t3_2_layer,\n", - " t3_4_eta, t3_4_z/z_max, t3_4_r/r_max, t3_4_layer]:\n", - " features.append(fetch_t3_features_for_t5(t3_feature, t5_t3_idx0))\n", + " for i in range(num_elements):\n", + " features_iter = []\n", + " \n", + " idx0 = branches['t5_t3_idx0'][event][i]\n", + " idx1 = branches['t5_t3_idx1'][event][i]\n", "\n", - "for t3_feature in [t3_2_eta, t3_2_z/z_max, t3_2_r/r_max, t3_2_layer,\n", - " t3_4_eta, t3_4_z/z_max, t3_4_r/r_max, t3_4_layer]:\n", - " features.append(fetch_t3_features_for_t5(t3_feature, t5_t3_idx1))\n", + " # Collect features using idx0\n", + " features_iter.extend([\n", + " branches['t5_t3_0_eta'][event][idx0],\n", + " branches['t5_t3_0_z'][event][idx0] / z_max,\n", + " branches['t5_t3_0_r'][event][idx0] / r_max,\n", + " branches['t5_t3_0_layer'][event][idx0],\n", + " branches['t5_t3_2_eta'][event][idx0],\n", + " branches['t5_t3_2_z'][event][idx0] / z_max,\n", + " branches['t5_t3_2_r'][event][idx0] / r_max,\n", + " branches['t5_t3_2_layer'][event][idx0],\n", + " branches['t5_t3_4_eta'][event][idx0],\n", + " branches['t5_t3_4_z'][event][idx0] / z_max,\n", + " branches['t5_t3_4_r'][event][idx0] / r_max,\n", + " branches['t5_t3_4_layer'][event][idx0],\n", + " ])\n", + " \n", + " # Collect features using idx1\n", + " features_iter.extend([\n", + " branches['t5_t3_2_eta'][event][idx1],\n", + " branches['t5_t3_2_z'][event][idx1] / z_max,\n", + " branches['t5_t3_2_r'][event][idx1] / r_max,\n", + " branches['t5_t3_2_layer'][event][idx1],\n", + " branches['t5_t3_4_eta'][event][idx1],\n", + " branches['t5_t3_4_z'][event][idx1] / z_max,\n", + " branches['t5_t3_4_r'][event][idx1] / r_max,\n", + " branches['t5_t3_4_layer'][event][idx1],\n", + " ])\n", + " \n", + " # Add remaining features\n", + " features_iter.extend([\n", + " branches['t5_eta'][event][i],\n", + " np.log10(branches['t5_innerRadius'][event][i]),\n", + " np.log10(branches['t5_bridgeRadius'][event][i]),\n", + " np.log10(branches['t5_outerRadius'][event][i]),\n", + " ])\n", + " \n", + " # Append the feature vector to the list\n", + " features_list.append(features_iter)\n", "\n", - "# Append T5 standalone variables at the end of the features list\n", - "features += [\n", - " t5_eta, np.log10(t5_innerRadius), np.log10(t5_bridgeRadius), np.log10(t5_outerRadius)\n", - "]\n", + "# Convert the list of features to a NumPy array\n", + "features = np.array(features_list).T" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", "\n", "# Stack features along a new axis to form a single array suitable for NN input\n", "input_features_numpy = np.stack(features, axis=-1)\n", @@ -121,7 +165,7 @@ "\n", "# Apply mask across all columns: retain a row only if all its entries are neither NaN nor Inf\n", "filtered_input_features_numpy = input_features_numpy[np.all(mask, axis=1)]\n", - "t5_isFake_filtered = t5_isFake[np.all(mask, axis=1)]\n", + "t5_isFake_filtered = np.concatenate(branches['t5_isFake'])[np.all(mask, axis=1)]\n", "\n", "# Convert to PyTorch tensor when ready to use with NN\n", "input_features_tensor = torch.tensor(filtered_input_features_numpy, dtype=torch.float32)" @@ -129,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -177,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -193,12 +237,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -210,7 +254,7 @@ "source": [ "from matplotlib.colors import LogNorm\n", "\n", - "plt.hist2d(t5_eta[t5_isFake == 0], predictions[t5_isFake == 0], bins=[50,50], norm=LogNorm())\n", + "plt.hist2d(np.concatenate(branches['t5_eta'])[np.concatenate(branches['t5_isFake']) == 0], predictions[np.concatenate(branches['t5_isFake']) == 0], bins=[50,50], norm=LogNorm())\n", "plt.xlabel(\"T5 Eta\")\n", "plt.ylabel(\"T5 DNN Prediction Score\")\n", "plt.title(\"DNN Score vs. Eta for 100% Matched tracks\")\n", @@ -219,12 +263,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -237,15 +281,15 @@ "output_type": "stream", "text": [ "pt: 0 to 5\n", - "96% Retention Cut: [0.4301 0.4419 0.4762 0.5147 0.347 0.3538 0.4061 0.5284 0.7186 0.7806] Mean: 0.4997\n", - "99% Retention Cut: [0.1089 0.1222 0.1648 0.2305 0.1935 0.2305 0.2725 0.3658 0.514 0.5102] Mean: 0.2713\n", - "99.9% Retention Cut: [0.0338 0.0384 0.0475 0.058 0.0674 0.0992 0.1346 0.1875 0.1646 0.1129] Mean: 0.0944\n", - "99.5% Retention Cut: [0.0656 0.0691 0.0986 0.1437 0.131 0.1737 0.2268 0.3001 0.3909 0.3345] Mean: 0.1934\n" + "96% Retention Cut: [0.4386 0.435 0.4638 0.5505 0.4071 0.4107 0.4946 0.5259 0.6527 0.6379] Mean: 0.5017\n", + "99% Retention Cut: [0.1294 0.1237 0.1594 0.2274 0.2029 0.2416 0.3168 0.3414 0.4438 0.3951] Mean: 0.2582\n", + "99.9% Retention Cut: [0.0323 0.0298 0.0422 0.0607 0.0595 0.0654 0.1282 0.1102 0.1015 0.0903] Mean: 0.072\n", + "99.5% Retention Cut: [0.077 0.074 0.0998 0.1392 0.1352 0.1574 0.2531 0.2601 0.322 0.2704] Mean: 0.1788\n" ] }, { "data": { - "image/png": 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sz+nY5s2bC29vb6trwP0yrjH3v54zZswQgKX5PSIiQuh0OjFixAir/eLi4kSxYsUsX9gZ148nnnjC6jNz4cIFodfr7ZbohIeHW5oZs5Pd+z3jcz5+/Pgcj8+4XicmJopOnToJLy8vsXnzZqt9hg8fLry9vfP0mIQQIioqSgBi2rRpme7L+NxkvP8yDBw4UGg0GnHx4kUhxIObrrRarWjevPkDYzl48KB45ZVXxMqVK8X27dvFokWLRIUKFYRWq81Vs1p2TVcZidiMGTOsti9fvlwAYsGCBTmWm12i4+zsbHkOhBAiKSlJ+Pj4iMGDB1u2ZbxPZs6cmWXZL774oggICHjgY3sU2X14uchh5IWvry9jxozhp59+Ys+ePbkq780338THx4cxY8bkKQ4nJydWrlzJsWPH+Pjjj+nWrRs3btzg/fffp0KFCpbq8p07dxIbG8vQoUOz7Ux58uRJrl69Ss+ePa2aEtzd3enUqRO7d+8mMTHR6phOnTpZ/f3rr7+iKAovvfQSRqPRcitWrBjVqlV7YOe5fv36kZSUZNV5c/HixTg5OdGjRw8AfHx8CA8PZ+bMmcyaNYtDhw5hNptz/ZxlJaMTckbzSkZV8MWLF7NsSmvRogUBAQGWv7VaLS+88AJnzpyxNOPUqVOHdevWMXbsWLZu3UpSUlKeYpo+fTr79u3LdLv3vDlZsWIFDRo0wN3dHZ1Oh16vZ+HChRw/fjxPcWT4448/ADJVkdepU4cKFSpkep6KFClC8+bN83WuB8nuPXz/9pw6Dt9735AhQ7h16xbHjx8nKiqKDh068O2333LkyBFmzpzJrVu3ePHFFylatCjh4eHMnz8/T/H27duXqKgo1q1bZ+l03aVLl2z3nz9/Pk888QTOzs6W127Lli25eu0SExPZtm0bXbt2tTRx5KRDhw5Wf1etWhXA0my7YcMGjEYjvXr1svpMOzs706RJE8tnOuP60aNHD6vnNiQkhPr16z8wjtzq0KGDpZnxXrl5v69btw5IH0H3INHR0TRv3py9e/fy559/0qJFC6v769Spw507d+jevTurV6/m5s2buYo/o1nQ398/y/s9PDwyvSY9evTAbDazffv2XJ3DaDRmed26X40aNZg9ezbPPfccjRo1om/fvuzcuZPAwEDefPPNXJ0rK7///juQ+VrRpUsX3NzcchVbVqpXr07JkiUtfzs7O1O2bNk8dfvw9/fn+vXrGI3GfMWgZnZNdBISEoiOjqZ48eLZ7vPqq69SvHjxXL9ZPD09efvtt1m/fr3lCyUvKlSowKuvvsp3331HREQEs2bNIjo6mgkTJgDp7f6Apf0yK9HR0QAEBgZmuq948eKYzWZu375ttf3+faOiohBCEBAQgF6vt7rt3r37gReDSpUqUbt2bRYvXgyAyWTiu+++49lnn8XHxwfA0o+ndevWzJgxgyeeeIKiRYsycuTILIf2P0hcXBwrVqygTp06FC1alDt37lhG4iiKYkmC7lWsWLFst2U8j3PmzGHMmDGsWrWKZs2a4ePjw3PPPcfp06dzFVepUqWoVatWpltWF/n7/fzzz3Tt2pWgoCC+++47du3axb59++jXr5+l70lePej9kXF/hqz2s5Wvr69VLPe6deuW5T2SsW9W+yUkJJCammq1L4Cbm5ul71F0dDSvv/46s2fPpkiRIrzyyivcunWLM2fO8MMPPzB69Og8fU5DQkJo0aIFixYtYtGiRXTr1g1XV9cs9501axZDhgyhbt26/PTTT+zevZt9+/bRpk2bXCXLt2/fxmQy5fhZv1fGc5rByckJwHKuqKgoAGrXrp3pM718+XLLZzrjuc7ps2EPWb2vcvt+v3HjBlqtNlfxnDp1ij179tC2bdssRyD17NmTRYsWcfHiRTp16oS/vz9169Zl06ZNOZab8bw6OztneX9WP2Tuv7YUJG9vb9q3b8+RI0fy/OMsQ3R0NDqdLlOirSgKxYoVy/fjuP+9Cunv17zE6ezsjBAi39dBNdM9eJfc++233zCZTDRt2jTbfVxcXJg0aRKDBg3it99+y1W5Q4YM4ZNPPmHMmDEMGTIk3/EpisJrr73Gu+++y7///gtgecPd32n0XhlvosjIyEz3Xb16FY1GQ5EiRTKd615+fn4oisKOHTssF8x7ZbXtfn379mXo0KEcP36cc+fOERkZmanTZkhIiCUBOXXqFP/73/+YNGkSqampef61vWzZMhITE9m7d2+mxwewcuVKbt++bXXftWvXMu2XsS3jeXRzc2Py5MlMnjzZ8mt+7NixPPPMM5w4cSJPMebVd999R1hYGMuXL7d6jbKbGyg37n1/3P8levXqVfz8/Ky22TIPRnYyvnD++eefTMPQ//nnH6svpCpVqvDDDz9w7do1qy+2f/75x6qsrLz++uvUrFmT7t27A+k1AYsXL8bLy4vatWvTqlUr1q5dS7NmzXIde79+/XjppZcwm818/vnn2e733Xff0bRp00z75DaJ9/HxQavV5vhZz4uM1/XHH38kJCQk2/0y3h85fTbsIav3VW7f70WLFsVkMnHt2rUHJuL16tWjS5cu9O/fH0jvQHx/p/m+ffvSt29fEhIS2L59OxMnTqR9+/acOnUq2+cq4/nMbuqPjMTyXvdfWwpaRotFfj/Dvr6+GI1Gbty4YZXsCCG4du0atWvXtkuc+XHr1i2cnJyyHcDzKLNbjU5ERASjR4/Gy8uLwYMH57hvv379qFChAmPHjs1V04rBYGDKlCns27ePFStW5CqerJISSP/iiY2NtdQ61a9fHy8vL+bPn59ts1u5cuUICgri+++/t9onISGBn376yTISKyft27dHCMGVK1eyrI2oUqXKAx9T9+7dcXZ2ZsmSJSxZsoSgoCBatWqV7f5ly5bl7bffpkqVKhw8ePCB5d9v4cKFeHh4sGXLFv744w+r28yZM0lJSWHp0qVWx2zZssXqgmQymVi+fDnh4eFZ/pIOCAigT58+dO/enZMnT2ZqAsyv7H7NKIqCwWCwulBdu3YtV6OuspPRDPXdd99Zbd+3bx/Hjx/PVLVfEIKCgqhTpw7fffddpnluTp48yfPPP2/Z9uyzz6IoCl9//bVVGUuWLMHFxYU2bdpkeY4//viDFStWMG/ePMs2IYTVRGPx8fG5njgwQ8eOHenYsSP9+vXjySefzHY/RVEy/SA4cuRIppGP99e8ZHBxcaFJkyasWLEi180pOWndujU6nY6zZ89m+ZmuVasWkH79CAwMZNmyZVbPzcWLF9m5c6fNceQkt+/3tm3bAuSYaN6rd+/e/PDDDyxevJhevXpZvefu5ebmRtu2bRk/fjypqakcPXo02zJDQkJwcXHh7NmzWd4fFxeXaQTY999/j0ajoXHjxkD2r7093L59m19//ZXq1atnW+uUIbs4Mq4F918rfvrpJxISEh7KtSI7586do2LFig47f0HKV43Ov//+a2mPvn79Ojt27GDx4sVotVpWrlz5wPZvrVbLBx98QMeOHYH/2r5z0r17dz788ENLW/KDDBo0iDt37tCpUycqV66MVqvlxIkTfPzxx2g0GkufH3d3dz766CMGDBjAU089xcCBAwkICODMmTP8/ffffPbZZ2g0GmbMmMGLL75I+/btGTx4MCkpKcycOZM7d+5kOYPz/Ro0aMCgQYPo27cv+/fvp3Hjxri5uREZGcmff/5JlSpVHlhb5e3tTceOHVmyZAl37txh9OjRVr+kjhw5wvDhw+nSpQtlypTBYDDw+++/c+TIEcaOHWvZr3///nz99decPXs2219X//77L3v37mXIkCFZ9idp0KABH330EQsXLrSaDdnPz4/mzZszYcIE3NzcmDdvHidOnLAatli3bl3at29P1apVKVKkCMePH+fbb7/NVcIIcPr06SxnJy1RooQlmcqotVi+fLllZuwqVarQvn17fv75Z4YOHUrnzp25dOkS7733HoGBgbluOrtfuXLlGDRoEJ9++ikajYa2bdty4cIFJkyYQHBwMK+99lq+ys3w448/AlimIdi/f7/lV1fnzp0t+02fPp2WLVvSpUsXhg4dyvXr1xk7diyVK1e2qvmrVKkS/fv3Z+LEiWi1WmrXrs3GjRtZsGABU6ZMydR0Bek1AIMHD2bSpEmEhYVZtrdu3Zp3330XT09PTp8+zZYtW/Lch8HZ2dnyGHPSvn173nvvPSZOnEiTJk04efIk7777LmFhYVb9Cjw8PAgJCWH16tW0aNECHx8f/Pz8CA0NZdasWTRs2JC6desyduxYSpcuTVRUFGvWrOGLL77I00SFoaGhvPvuu4wfP55z587Rpk0bihQpQlRUFHv37rXUXGo0Gt577z0GDBhAx44dGThwIHfu3GHSpEl2bbrKSm7f740aNaJnz55MmTKFqKgo2rdvj5OTE4cOHcLV1ZURI0ZkKrtz5864urrSuXNnkpKSWLZsGQaDgYEDB+Li4kKDBg0IDAzk2rVrTJ061VLrlx2DwUC9evWynXnY19eXIUOGEBERQdmyZVm7di1ffvklQ4YMsfRPyem1B9DpdDRp0uSBfWF69OhByZIlqVWrFn5+fpw+fZqPPvqIqKioXE1EmvHDdfr06bRt2xatVkvVqlVp2bIlrVu3ZsyYMcTGxtKgQQOOHDnCxIkTqVGjBj179nxg2QXBbDazd+9eSy3dYycvPZczRiNk3AwGg/D39xdNmjQRH3zwQZYjGbIapZKhfv36Ashx1NW9Nm7caDn3g0ZdbdiwQfTr109UrFhReHl5CZ1OJwIDA8Xzzz+f5ciUtWvXiiZNmgg3Nzfh6uoqKlasmGlipVWrVom6desKZ2dn4ebmJlq0aCH++uuvXD9eIYRYtGiRqFu3rnBzcxMuLi4iPDxc9OrVS+zfvz/Hx5PVc3Dq1Cmr+6KiokSfPn1E+fLlhZubm3B3dxdVq1YVH3/8sTAajZb9evfuLQBx/vz5bM/z6quvCkAcPnw4233Gjh0rAHHgwAEhxH+v27x580R4eLjQ6/WifPnyYunSpZmOq1WrlihSpIhwcnISpUqVEq+99pq4efNmjo/9QaOu7h0xcuHCBdGqVSvh4eFhGe6fYdq0aSI0NFQ4OTmJChUqiC+//DLL0QxZye71NZlMYvr06aJs2bJCr9cLPz8/8dJLL1mNBBMiffRIpUqVHniee+X0mO+3ceNG8eSTTwpnZ2fh4+MjevXqleXkhqmpqWLixImiZMmSwmAwiLJly4o5c+ZkG8Pbb78tqlWrZjWMXwghrl+/Ljp37iy8vLxEcHBwrkb83TvqKjtZjZ5JSUkRo0ePFkFBQcLZ2Vk88cQTYtWqVVlOurd582ZRo0YN4eTkJADRu3dvy33Hjh0TXbp0Eb6+vsJgMIiSJUuKPn36WIb2ZjeyM+P9d/9UDatWrRLNmjUTnp6ewsnJSYSEhIjOnTtnGo301VdfiTJlylie70WLFtllwsAHjabJ7fvdZDKJjz/+WFSuXFkYDAbh5eUl6tWrJ3755RfLPlmNkv3jjz+Eu7u7aNOmjUhMTBRff/21aNasmQgICBAGg0EUL15cdO3aVRw5cuSBj23hwoVCq9WKq1evWm3P+Nxs3bpV1KpVSzg5OYnAwEDx1ltvZXpP5vTak8vh5VOnThXVq1cXXl5eQqvViqJFi4qOHTuKvXv3PvBYIdLfqwMGDBBFixYViqJYXW+TkpLEmDFjREhIiNDr9SIwMFAMGTJE3L59+4HlZjfqKqtJHJs0aWL1WHN6n2zZssXqWv64UYTIYz2zJEmSJBWA5ORkSpYsyeuvv2410rZp06bcvHnT0rdSsq+ePXty7tw5/vrrL0eHUiDk6uWSJEmSKjg7OzN58mRmzZr1WC8yqSZnz55l+fLlTJ8+3dGhFBi7jrqSJEmSJFtk9K88d+5crgZpSLaJiIjgs88+o2HDho4OpcDIpitJkiRJkh5bsulKkiRJkqTHlkx0JEmSJEl6bMlER5IkSZKkx5bsjEz6ZElXr17Fw8OjQKbnlyRJkh4fQgji4uIoXrx4puUv7CU5OZnU1FS7lGUwGB44m/PjTCY6pC8LERwc7OgwJEmSpEfIpUuXcr1IbF4kJycTFuLOtetZL62RV8WKFeP8+fOFNtmRiQ5Ypn0fvKEdTm4PXgHbEaq42mchwoLQyEW9sQEkq3xg4ZiLzzk6hGz9r/5MR4eQo45Npzo6hBydGWJwdAjZGlj9T0eHkKME04MXOnaUlIQ0Pn5qc56WDMmL1NRUrl03cfFAKJ4ettUYxcaZCal5gdTUVJnoFGYZzVVObnqc3NWZ6Li6aR0dQrY8XNTd1Uuv8kRH76beL0NPT09Hh5AjnVbdF26Ni3pfW2d3dV/+jSZ1XovvVdBdHdw9FNw9bDuHGdkdQ93vdEmSJEkqpEzCjMnG32kmYbZPMI8wmehIkiRJkgqZEZixLdOx9fjHgbrbHCRJkiRJkmwga3QkScqWyWQiLS3N0WFkq2iAm6NDyFG8Qb39TFzNRR0dQs7MjunfJBAkK/GYFPsM7baFGTO2NjzZXsKjTyY6kiRlogCNdFU5deqUo0PJ0YBRDRwdQo6MNnYkLUgepmqODiFHZuG4585kNnKRQ5zW7sCRfXlNQmCycTCFrcc/DmSic4/J/v/g6aHO0U1JIsXRIWRLg3qHgQIccfwPsxxV8Lzm6BAyqWiuTwVtRYxmJ7Ra9dZKFCnv7ugQclTENdHRIWTLRaPuD0aM0cUh5xVCYEoxobnhwp1kF/azP9M+qUnqfu4kazLRkSTJik4YKKVUw8fPB1OCq6PDyZGi3hwMAJ2TOn84ARgKaEZfe9FqHffc6Zx1+OBDxaiK/G38mzQc03wrOyPbh0x0JEmy4owbWkWLzlmLKcHR0UiSY2idtOgUHW64cYc7DonBjMAkEx2bqTullyTpoVMy/lNv9xJJKnBy3cPHh6zRkSRJkiQVkk1X9iETHUmSJAmAIQPGExMTx/cr5jg6FAk56speZNOVJEkFwmQ2sTdqJ79dWMneqJ2YzPZZiTk7CfHxzHh7Am2eqEWdkmH0avcM/x46nGm/c6dOMbJnbxqEl6VeWGleavs0kZf/W5h25oSJNCpbgdY1arJu5SqrYzesXsOIl3o9MJbVPyynmn8gQa5lCHItQ7XQJ+ndaRAnj53O02OqW74pX362OE/H5Mali5fxdq7Mkb9PWG2f9tFY5n05xe7ny8r2rXvp8uwQwoo3ILBILepW78D4MTO5eiUq12W8MfgNBncbXIBRSo8DWaNzj//FF8FFpU+Js0a9k7Y5K+qNDeBQYqijQ8jR0ZhAR4dgxU/xpLG7lmSzjlSv/JWx+dxvzPhzAlEJkZZtAW6BvNnwPZ4q9bSdIgWDk9Hy7zGDX+P08VNMXzAb/2IB/PK/lQzu0pVf92whoHj6cxxx7gJ9nnmWTj27MXL8a3h4enD21BncPXUYnIz8sW4T61euZOHK77h47jzjh71K41YNKOJThNg7MXw2dSqL1/xgdd6s6HQm3D09+OvwOoQQRF6NYsrbM+n1/ED+OrIegyH3k+EpCDR2nvQto7wUs44k839D1wwePgAkFfAcc9989QPjXp1M1xef46vvPyU4JIjLlyL5cekqZs/+lsnTx5FmfvCoK7NQMAslV/vmVXptioYbSR7cuK9WxJj0cKb7MN+92VpGYSdrdCRJsqvN535j9IaBVkkOwPWEa4zeMJDN536z+zmTk5LYtGYdo999i9oNniQkPIzh40ZRIiSYZQu/tew3+70ZNG7VnDfeG0/FapUJDguhaesW+Bb1A+DsyTPUbvgklZ+oxtOdn8Pdw4PL5y8C8OE7H9B9QC+KBwflKiZFUfAvVpSAQH+q16zCoOF9uBxxhbOnzlv22bf7IM+1fJEwn6rULNOEt1+fQmJC+tw7z7fuyeWIK0x8cyqBruUIdC2Xq+MAapdvzicz5vPa4HGU9q9BzbJN+Xbhcsv9dSq0AKBlvecIdC3H8617AvDKoLH06TrUsl9KSipvvz6FyiH1CC1ShQ4tunN4/xHL/Tu37yHQtRw7/thF6wbPE+ZbjWeadePMqXPZPi9XL19jwugp9B/ak4+/mEr9xnUJDilBvYa1+ejz9xk1bhgAn37wMc82aGt17JK5C2leuYHl/pXf/8iW3zZSzjOEcp4h7NmxK1evzaPCdHfUla23wk4mOpIk5UgIQWJaYq5ucSlxTP/zbUQWF1dxd+v0PycQlxKXq/JELvsXmIwmTCYTTk7Wk1c6OTtzcPc+AMxmM9s2/k5o6TAGdHyRBuHVeaH5M2z+db1l//KVK3D00BFibt/h6KEjJCcnU7JUKAd27eXY3//Q8+V++XoOY+7EsvJ/vwKg06fXGh//9yTdO/Sn3bMt2bJ3DfO//Zg9Ow/w1mvvAbBw2acUDyrGGxNG8ve5P/n73J+5Oi7DF3MWU+2JymzatYo+g3ow9pVJnD55FoB121cA8L/flvD3uT9ZuOzTLON+b/wMflu1gU8WTGPDzpWEhYfQ/dkB3L51x2q/aZM+ZuK0saz/8ye0Oi2jXn4r2+fil5XrSU1NY9hrA7K838vbM6en0qLfyEG0fb49jZ5qwp+n9/Hn6X3UqFszV8c+KkzCPrfCTp3tNJIkqUaSMYl6X4XbqTTB9YRIGi4qm6u9dw04i6v+wZMWunm4U71OTT6f+Qnh5Urj61+U335czZH9hwgJDwMg+sZNEuMT+OrjeYx8+w1en/wWf27eysiXBrHk1+XUaViPhk815ZkXnqdrs/Y4uTgz9fNZuLi5MnnUW0ydN4sfFn7Ld18spoivD5M/mUaZCuWyjSkuJpbwojUQQpCUmARA66ebU6Zc+nM5b/ZCOnZ9hkHD+wBQqnQoUz4az/OtejJtziSK+Hij0Wpx93DDv9h/61I96Dhn5/Rkr3nrxvQZ/CIAw18fyILPlrBz+17KlAvHt2h6E1URH2+rsu+VmJDIN1/+wOwFU2nRugkAH859jzpb/mLZ1z8y9J5EZeyk16jfqA4AI14fxEvPDyI5OcUSy73On7mAh6c7AYH+2T53ueHm7oazszOpKakUDbCtLOnxJhMdSZIeC9O/mM344aNpUr42Wq2WitUq077Lcxz7+18AhDm9t0Lzdq3oM2wgABWqVuLQ3v0sX/QddRrWA2D4uFEMHzfKUu5nU2dRr2lDdHo982fOYfWuTWxdv5mxg1/jp+1rs43HzcOdTTt/xmg0smvHPj6fvZDpcyZb7j9y6CgXzl7k5+W/WLYJITCbzURcuEzZ8lknl7k9rmLl/5IwRVHwD/Aj+kZ07p5M4MK5CNLS0qhd7wnLNr1eT/VaVTl94qzVvhXuOVdG4nTzRjQlgotnKlcIIeeoySXZR8c+ZKIjSVKOXHQu7Bpw9sE7Agev7mbY2hcfuN/cdkt5oviTuTp3bpUsFcq3a38kMSGR+Lg4/IsF8FqfIQSFBAPg7euDTqcjvHwZq+NKlS1jad6637lTZ/jlfyv5ecd6fv5uObXq18XHz5c2HZ9h/LDRxMfG4e7pkeWxGo2GsPAQAMqUC+dG1E0G93qNVZuWAulNaT37d6P/0J6Zjg0Kzr6Dem6Py2giy6CgYDbnvh0jo9Xw/qQkq0RFf8+5Mu7LSCzvV6pMGLExcURFXs+xVkfRaDI1XRqN6h74YG9mFEw2ripqvnt87drpPwCGDRvGsGHD7BHeI0MmOpIk5UhRlFw1HwHUC25CgFsg1xOuZdlPR0HB3z2QesFN0GoKZi0jVzdXXN1cibl9h79+387oyen9RQwGA5WfqMb509YdZS+cPZdlB2MhBO+8MoYx70/Azd0Nk8lE2t0vWmNa+v/N2XyZZ2XQiD588eli1q7eRLtnW1KlekVOHj9tSYayYjDoMZusz5Gb4x5Er08faWUyZT/kPyy8JAaDnr07D1DihfSambS0NI4c/JeBw3vn+9ztO7bmgwkfMvfjr3h3Rua+PDF3YvHy9sTHz4ebUTesEqvjR45ZPw6DHnMOj0H6z759+/D0zF3/p8eNTHTucSCuFAahzlUCbc3qC5JeUfeF5kKCr6NDyJFRqGtMgAkNAhCARp+3im8NCmObvMuotYNQUKySHeXue3hs48nonRTsUanuov1vFeltm7cjhCC8TCkunLvIB+OnUapMGD16P4f+7n4vv9qfEb1foX7DmtRr/CTbNm1n67rN/LDue6uyAL5ftAz/oj48/UxTIJV69aszb9osjh/Yw9aN2yhTvgwBvi5A5pWs9Roj6d2v//vcunt60KNPFz58/1PadGjJsFGDaN+0K2NfncyLfbvi6ubK6RNn2f77X7w/6x0ASoQEsevP/XTo/DQGJwO+fj65Oi7jGb/3/MJyU/D198PZxZnNG//CN7AETs5OeHp5YBIKZhRSzDq0Lp68OOBF3n1rJm7ePgSVKM782QtITEqmU89upJh1pN4d1p1i1pFiTv86SRWZt93Lr3gwE6a9zTuvTyImNpHnu3ekREgJrl2J5KfvV+Lm7sbbU9+idsN63Lo5gQUfz6f1s+3YsWUbOzZtxc3TA7NIf1zFg4PZsWU7Z0+dw9unCO6eHpYkzlZCpD9/ySbrIfgARuPDaRAyi/SbrWUUduq6wkqS9Mh7qnQ7ZrVbgL97MavtAe6BzGq3gKdKtyuQ88bFxPHOqEm0eKIVowaOpna9Wny7+murL742HVrz/ifv8cXsBbSu244fvv4fny+dS+36tazKuhF1k7kffs6kD/9LHKrXqsaAEQPo13kAv/28lpnzp+c5xv7DenP6xFl++XkdFauU5+cN33H+7EU6tnyRVvWeY+Z7swm4p3PwGxNe4XLEZepXfooqJdOb+nJz3IPodDomzXyH7xcto26Zegx8IetJ98a8+yZtn23NqAGjebphBy6eu8g3q5bgVSSfEyzd1XPQS3y75muuXb3G4B5DaPFES8YMewsPT3cGvZLeyTm8XBne+WgKy776ho4N2/DPgcP0HWEdZ5c+3QkrXYrOTdtTv1R1Du3eb1NcamO623Rl662wU0Rux28+xmJjY/Hy8mLQti4Y3GWNTl7JGh3bJJvUVbFaVPFkgEdrAkoGYtI457sck9nEwat7uJFwnaJu/jxRvK7dm6s8DMl2Lc/eDBr1fjZSC2CSPXtKMjn2WmxONRJ1KZK5dzZz3RxndZ8xIYVdz35KTExMgTQHZXwn7TlaDHcP2+oj4uPM1K10rcBifRSo6worSdJjQ6vRUrtEfUeHIUmPLHvUyKj5R/LDIhMdSZIkSVKhjCUubC2jsJN9dCRJkiRJemzJGh1JkiRJUiHZdGUfMtGRJEmSJBUyocFkY8OLervDPzwy0bnHidii6EyZ12ZRAzW3s7rrM88joiYRMd6ODiFHLnqjo0Ow4qx1Tu8bYNag0al7Anmdou741DyyKU2oN7Z0jr7mKSDAZNZgMlsnG/f/XVCEHfroCBV/dzwsso+OJEmSJEmPLVmjI0mSJEkqJPvo2IdMdCRJkiRJhUxCg8nGJWJMhX5KYNl0JUmSJN315uDXGdJtoKPDkCS7komOJEkFw2TCdccuPH9cheuOXVDAq0zHx8Uz8Y0p1C3fhHDfyjzbvCuHDxyx2udG1E1eG/QmNcMbUNqvCi8+249zZy5Y7TN5zAdUKlGLOuUas3rFr1b3/fLTWvp0HvTAWP737U+UcCtDqHs4oe7h1AqrQ/8uAzl17FSeHlODio1ZOHdxno7JjUsXL1PGI5RjR45abZ8wYyLT539o9/NlZff2nQzo1IfaJatTxb88bWo9xdRxU7h29Vquyxg35DWG9+hfgFE6lhkFMxobb7LpSiY6kiTZnceadZSpUo/QZ7pSYsAIQp/pSpkq9fBYs67AzvnGsPHs+OMvPvlqJpv3/kbjFg3p3r43kXe/OIUQ9O82hIgLl1j4v8/ZsHM1JUoWp3v73iQmJAKwae0WVv3vF75fs5i33nuDUS+P5Xb0bQBi7sQyffIs3v94Uq7i8fB0Z+/Z3ew9s4tFP31FYkIifTsPIDVVvaMUPbw88fS2bcHO3Fi2aCm9n3kJP/+ifLb0c9bt28S7s98nLjaORXO+LPDzPyrkop72Ifvo3OPqHS+0qflfxLAgKYp6G1pdndV74QaIi3NxdAg5MnjHOzoEKyYl/cIoAF0+3nfua9ZRvPfLcN96wbrIa5ToPZir38wnvkNbe4Rq+bWanJTM2lUb+Op/86nTsC4Ar41/hQ2/bOabL5fxxsRRnDtzgYN7D7Np31rKVSwLwJTZ71IjtC4rV/xK9z4vcOrEOZ5sXJcqT1SlyhNVmfTm+1y4cBkvXx+mvD2DXgNfJDA4iAcNahcoKIqCr38AAL4Bxeg7rD+DXhjImVMXKFepPAAHdh9g5sTpHDl4hCK+PrR6phVvTHoTVzdXerTtxpWIK7w3ZgrvjZkCwNm48w88DqBxpYZ069Odi+cusm7VWjy9vRj2xjC69+sBQKNKTQB4tsHTANRpWJel65bz5uDXiYuJ5fMf0pONlJQUpr89ld9+/IX4uDiq1KjKW9MmULVmNQD27NjFS+268/UvS5n5zjTOnDhNhSoVmfb5TEqVDc/yuYm8Esl7b0ym15A+jJ/23+rwJUKCqdOwLrF3YgCYO/UjNv+2kdV//ZccL5m7kG8+X8Tv//7Fpx98zKrvfwSgglcwAN/89gN1G9V7wKuTO0IxoygCN30q7iLF6j5jWko2R0lqJGt0JEnKmRAoCYm5u8XG4f/mxPRj7itGuZv4+I+ZiBIbl6vy7k+WsmM0GjGZTDg5Wc+D5ezixL5d+wFITUlPyJ2c/9tHq9Wi1+vZt/MAABWrlOfIwX+4czuGI4f+JTk5mZBSIezduZ9/Dx+l79De+XkGib0Ty5oVawDQ6dJX5T559AR9O/amdYc2/LZrHXOWfMqBXfuZ9PpEAOYtnU+xoEBeffs1dp/Zy+4ze3N1XIaFn35FlSeqsObPX3lpwEu889oEzp48C8DKrasA+PqXpew8s5e5S7/IMu4ZE6aycfU6ZnzxIav+/I2SpULo17EXd27dsdpv1uSZjP1gPD9v/wWtTse4oW9m+1ysX/kbaampDHz15Szvz22NUr+Rg2j7fHsaPdWEP0/v48/T+6hRt2aujn1UZHRGtvVW2MkaHUmScqQkJlG2eHn7lCUE+qvXKBtcKVf7n7p6AnG3liIn7h7u1KxbgznTP6N0+XCK+vux+n+/cGjf34SVDgUgvFwpSpQMYvrED5k6Zwqubi58OWcRN6JucP3adQCatGxMx27P8kzjjjg7OzNrwUxc3VwY/8o7fLRgBt9+uZQl87+liG8Rpn06xVIzlJXYmDiqFKuEEIKkxCQAnmr3FOHl0ms6FnyygGe6dKDvsH4AhJUO450ZE+nethvvzZ6Ct483Wq0Gd3d3igYUtZT7oOMyErmmrZvy0sCeAAwe9TKL5i5kz5+7CS8Xjo+fLwDePt4UDfDPMv7EhESWfbWUafM/pEmrZgC8/9k0mlVqyIpvljPw1cGWfUdNfIO6DZ+8e64hDOzcl5TkZJycM9eQXzh7AXdPD/yLZX3e3HJzd8PZ2ZnUlNRsH8OjLr2Pjo2LesqmK5noSJL0ePj4qw95Y8hY6pRugFarpXL1SjzX9Rn++Tu9w61er2f+93N5c8g4qpaoiVarpWGz+jRr1cSqnFHjX2HU+Fcsf896/xMaNquPXqfj0+nz2Lj3N7as+4PXBr7B2r9WZxuPu4cbq3f8isloYs9fe/jykwW898n7lvuPHvqXi+cusuZ//5UhhMBsNnPpwiVKly+dZbm5PS6jeQxAURSKBhQl+kZ0bp5KACLOXyQtLY2aT/5XS6LX66lasxpnT56x2vfecxUtlp6URd+IpnhwUKZyhRAo8rtXeohkoiNJUo6Eqwunrp7I1b4uO/cQ3PnBzTuXfvyapPp1c3Xu3AotFcKKDctITEgkLjaegEB/hvYaScmQYMs+VWtUZv3uX4iNiSMtNRXfor50aNKJqk9UzrLMMyfPsmr5GtbtXMPyb36kTsPa+Bb1pX2ndoweMpa42Dg8PD2yPFaj0RAaHgpAeLlwbkbdYGTv4fyw4X8AmM1muvXrTu+X+2Q6tnhw8WwfZ26P0+v1VvcpioLZnPslM8TdZkPlvqwkPVGx3qbX//dVknFfducKKx1GXEwc169dz7FWR9FoLDFkMBrTch3/48Bsh7WuzKi3f+fDIhvvJEnKmaIg3FxzdUts3pi04oGIbH6yC0UhLSiQxOaNc1Vefn76u7q5EhDoz53bMWzfvIOW7Z/KtI+nlwe+RX05f+YCRw7+Q6unM+8jhGDsiLd5e+o43NzdMJtMGNPS1yVLu/t/szn3XyJ9h/Xj+L8n2LBmAwCVqlfm9PHThIaHZroZDAYgPVkx3TcsPzfHPYjekJ4EmU3ZJz4hpULRGwzsv9vHKf1xp/HPoX8IL5d1bVNutHmuHXqDgS9nz8/y/ozOyD5+PtyMumGV7Bw/cizT4zAX8LQFjiT76NiHfAYkSbIfrZbr0ycBZEp2Mv6+Pm0SaO2/oOS2TdvZunEbERcusX3Ln3Rr+xKlypSia89Oln1+/Xktu7bv5uL5CDb+uokXn+lN62da0vipRpnK+37xcnyL+liSoFr1arJz2y4O7j3EV58tokyF0nh5e+Y6Pg9PD17o1ZVPPvgYIQSDXxvMob0HmThqAseOHOP8mfNs/m0Tk0b/16m4REgJ9v21l2tXr3Hr5i2AXB33IL5FfXF2cWb75m3cvH6DuJjYTPu4urnSY8CLzHj7A7Zv2srpE6cZP3wsyUlJdOn1Qq7Pdb/AEsUZP20CX89bzLihb7Lnz91cibjMgV37eXvkOOZO/xSAuo2e5NbNaL6cPZ+IcxdZuuBrdmzaalVWUMkSnDx6gnOnz3Ir+hZpaY9XjY/tc+ik3wo72XR1D2OaFnOaOlf0NcbrH7yTg6R5qPM5y2COzd2vXEdJ81TX82dUNICCEAqZx049WFyHdlz55gsCxkxEfzXyv3KLBxI1bZLdhpYDVis7x8TEM2PSh1y7cg2vIl60fbYNoye+jlZnIKPiJSryBu+N/YCb16PxL1aU57t3ZMTY4ZlWiL4RdZPPZn7Oz1tWWO6rWrM6A0YMoE+ngfj6+fLRgpnZrixtJn14/v3PX++h/fh6/tf8tnItTz/fnu/XLeejdz+kW+uuCCEoGVaSds8/YznulfGjmPDKWzSr2oTUlFTOxF2gXOWKDzyObM6fsU2r0zNhxiQ+mz6HT6bMolb92ixdtzzT43hj8hjMZsHogaNIiI+nSo2qLFr5DV5FbJtr58WBPQktHcbCOQsY1mMwyUnJBJUsQbM2Leg3YgAAoWXKMeGj91kw6zPmzZhDq2fa0WfEYFYs+d6yeninXi+yZ8duOjV5hsT4BJb88j/q2Gl4udmswSwUYlJcuG0yWt1nSlLXZ1bKmSLubwQthGJjY/Hy8iJ00dtoXNU5j46aEx29h7rn0THeVudrmsG7RIyjQ7BSTOvOm/6NKVqiOHpnGy7oJhOuO/eijYrCFBBAYv06dq/JcdGq/L0n1PuFqPYLf5rJsc+dOdVI1KWrfBy9jShTnNV9poQUDnaeRUxMDJ6eua/Vy62M76RvD1XB1cYfkolxJnrW+KfAYn0UyBodSZIKhlZLop1+XUtSYWSyQ2dkk+pT2oInG+8kSZIkSXpsyRodSZIkSVIhs9BgtnHUlFn2TpE1OpIkSZKkRhlNV7beHra4uDhq165N9erVqVKlCl9+6diFWmWNjiRJkiRJduPq6sq2bdtwdXUlMTGRypUr8/zzz+Pr6+uQeGSiI0mSJEkqZAZM2UxhkJcyHjatVoura/oadcnJyZhMpkyzXD9MMtG5h+mOEyLF6cE7OoLBEW/X3DHGqPQ5u0t/W71DfAE8Syc7OgQr7ho9iiLQasxoVb4okdpnfbX1S6ogCRXHBvmaFNvu51cAncaEHuvZlxXtw5mN2R4T/uXn+O3btzNz5kwOHDhAZGQkK1eu5LnnnrPaZ968ecycOZPIyEgqVarE7NmzadTov4k379y5Q5MmTTh9+jQzZ87Ez8/PpsdhC3VfJSRJkiRJeqgSEhKoVq0an332WZb3L1++nFdffZXx48dz6NAhGjVqRNu2bYmIiLDs4+3tzd9//8358+f5/vvviYqKeljhZyITHUmSJElSIXuudRUbG2t1S0lJyfa8bdu2ZcqUKTz//PNZ3j9r1iz69+/PgAEDqFChArNnzyY4OJjPP/88074BAQFUrVqV7du32+dJyQeZ6EiSJEn59vPSFdQumfXq75JtzCh2uQEEBwfj5eVluU2dOjVfMaWmpnLgwAFatWpltb1Vq1bs3LkTgKioKGJj09dPi42NZfv27ZQrV86GZ8I2MtGRJKlAmEywZ4cTv65wZc8OJwp6ken4uHjeG/MujSo2oGLR8nRu0YkjB/622ufm9Ru8MXg09crUpZJ/Bfp07M35M+dzLDctLY1Pp82hWdUmVPArx9P12rJt0zarfVYvX0WD8vV5omR1po7/wOq+yxcv0bpGU+JjrZcRuN/li5co5xliudUsUZmuzZ/j93Wbc/8kAGNffp2h3Qfm6Zjcal6lPl/P+8pqW7vnn2H9ga0Fcr77XTx7nreGjqJphdpULRrOU1Xq83q/Yfx78O8HH3zXyqX/o07JSgUYpf3Ys0bn0qVLxMTEWG7jxo3LV0w3b97EZDIREBBgtT0gIIBr164BcPnyZRo3bky1atVo2LAhw4cPp2rVqrY9GTaQnZElSbK7DWtceP/NIly7+t8lplhxI+Nn3KZ1h6QCOee44WM5fewUHy2YhX9gAKuXr6Jnh55s2LeRYsWLIYTg5W6D0el1fPHDAtw93Fn42UJ6dXiJDfs24ermmmW5s979iNXLV/H+p1MJLxvOji3bGdJjMCs2/0SlapW4dfMW44aPZcb8DykZGkz/zv15stGTNGvTHIBJr43n9cljcPf0yNXjWLJmKaUrlCU2JpZlX37LyJde5ucdv1G2ouN+EefE2cUZZ5eCX0/u34N/0/fZ7pSpUI5Js6dRqkw4CfEJ/L52I9Pffo9v1/5Y4DE8yjw9Pe261pVyX29xIYRlW82aNTl8+LDdzmUrWaMjSZJdbVjjwsiefly7aj3aLSpSy8iefmxY42L3cyYnJbNh9XrGvDeWOg3rEhoeyitvvUpwSAmWfvUdABfOnOfQvkO8O3sKVWtWo1TZcN79+D0S4xP5ZcWabMte9cNKhoweSrPWzSgZVpIXB7xEoxaNWfhp+iRoly5E4OHpQftO7alasxpPNn6S0ydOA/DL/1ahNxholYcV2719ilA0wJ/wsqV59Z03SEtLY8/2nZb7o65e49U+w6hdsgp1Q6oxpNsALl+8BMCnH3zMyu9/ZMtvGy01Q3t27MryuKHd+1uOAxg7ZBTDegxg4ZwvaFS2JnVDq/Lu62+TlpYGQM+nu3I14jJTx71Lea+SlPcqCWTddLXsq29pWa0hVfzCaVOzKat/+Mnq/vJeJVnx9TKGvziQ6sXK0rpGY35fuzHb50QIwbihowgpFcp363+iaesWlCwVSoWqlRg29jXmfr8QgL07dlHBK5jYO/8tlHv8yFEqeAVz5eIl9u7YxVtDXycuJpYKXsFU8Arms6mzcv3aPGxqnDDQz88PrVZrqb3JcP369Uy1PGoha3TuoaQpKDp1DrnUpKj3pTJ7Gh0dQo7MOnVPga5V1DV1gEYRKEDGJ0EISErM3efCZIIpbxQhfcqM+3/xKSiKYMqbRajfNDlXC5m7uIochxlnVMunpJkxmUzonJythpw7OTuzf9cBTEJDUkr6+1RvcPlvH40GvUHPvl0H6Ny7R5bnSE1JRZ+pXBdLucGlwklKSuafw8coHlyCIweP0OmlF4iOjmXO+7NY8uv/cjWNv7i7T8a0/2lpafxvyQ8AaHUGzEJDUmISPZ/uRs16dfh27Y9odTrmz5zDgOd7s2rnRvqMeJkzJ8+QEBfP+/M+AsCriDcJCSlZHjewUy9W7dyIwWAAYM+OXfgF+LPk1/9x8dwFXu87lHJVKtG1Tw/mfLuAjg1b06VPD7rcfa7EPa9xxr83/bKOD8ZOYuzUidRr1oht6zfz1tDRBBQvTt3G9S37z50+m9HvvsXod8ezdMESRg98hS3/7MLbp8jd5+O/5+bY30c5c/wUM7/6FEXRcP+ULB5eXgjx3zH3/zvj/9Xq1GTc1El8OvUjftu3FQBXN7dM5VnKAIxmbaaV1E0PaWV1s1Aw2zqPjp2nETAYDNSsWZNNmzbRsWNHy/ZNmzbx7LPP2vVc9qLeb09JklQhKVGhemCwXcoSQiHqqo6aJXJX3uHIS7i6PThRdfdwp0adJ5g341PCy5XBz9+PX1es5u/9hwkNDwOgVNlwgkqW4KPJ03l39lRc3FxY/NlX3Ii6wY1r17Mtu2GLxiz+7Ctq169LyVIh7Nr6F1vWbsRkSk9QvYp4MX3+R7w5eBTJSck8160TjZ5qwriho3lxcF8uX4xgWLd+pBnTGD52FK2fezrHx9Kj1XNoNBqSk5Ixm80ElQymTcf2AKz9aTUajYYpn820NBO8P+8j6pasxL4du2jQognOLs6kpaZSNMDfUuZPy3944HEAnt5eTPhwClqtllJlS9OkVQt2b/uTrn164O1TBI1Wi5u7u1XZ91v86QKe69GFHgN7AxA2fBB/7zvEok+/sEp0nuvRhac7PwfAq++M4bsvFvPPwcM0eqpZpjIvnkvvRxVWpnSOz92DGAwG3D09UFByfAyPo9q1a6PVahk2bBjDhg3Lcd/4+HjOnDlj+fv8+fMcPnwYHx8fSpYsyahRo+jZsye1atWiXr16LFiwgIiICF5++eWCfhj5IhMdSZIeCzMXzGbcsDdoVK4OWq2WitUq80yXZzn6978A6PV6Pv12Pm8Nf5PaIVXRarXUb9qQxi2b5lju2zMmMX7EWNrUao6iKJQMC+H5F7vw89IVln1aPdOGVs+0sfy9Z8cuTh49yfiZ79O6RkM+XPgZRQP86dr8GWo1qItv0ewnT5u1eB5hZUtz4cw5po6dxKSPp1pqOY4e/oeIcxeoFVTe6piU5BQizl+kQTZl5va40uXLor2nqq1oMX9OHT2R4/Nzv3MnT9O1j3XtWI0na/Ht54ustpWrVMHyb1c3V9w83Im+EZ11oXerXO7vF/K4M9uh6SljwsB9+/bluo/O/v37adbsv4Rz1KhRAPTu3ZslS5bwwgsvEB0dzbvvvktkZCSVK1dm7dq1hISE2BRrQZGJjiRJOXJxFRyOvPTgHYF9O50Y2OnBv5S//Ok6tetnP4/HvefOrZKlQli67n8kJiQSHxeHf7EAXukzjBIh/9UeVa5RhTV/rSMuJpa0tDR8/Hzp3OxZKteokm25Pn6+fL7sS1KSk7l96w4BgQF8OHGaVbn3Sk1JYdKot/nwy9lEnLuAyWiiTsN6AISGh3Fk/yGatW2Z7fmKBRUnNDyM0PAwXN1ceaXnYH7d+zu+Rf0wm81Uql6FGV/OyTLO7OT2OL3+vq8ERcEs8tG0mmVHVetddPedS1EUhDnrc4WElwLg3KnTVKia/YgpjSb9JIL/3jdGY1quw1Yb+6xenvfjmzZt+sAlG4YOHcrQoUPzG9ZDJTsjS5KUI0UBVzeRq1vD5skUK25EUbK+SCqKIDDISMPmybkqLz8/4F3dXPEvFkDM7Rj+3LKdFk+3yrSPh5cnPn6+XDhznn8PHeGpLPa5n5OzM8WKF8NoNLJh9bosywWYO30OjVs2pVL1KphMJkzG//qwGY1GTHkYZ1+nYT3KVCzHFx9+CkDFalW4ePY8vkX9CAkPs7p5eKX/WtfrDZZmtQy5OS439Ho95gfEX6pcGQ7u2me17fCeA5QqVybX57lfhaqVCC9fliWfLcCcRTKU0fm4yN2k7d6myBP/HLPaV2/QYzI/nCUcJHWQiY4kSXaj1cL4GbcBMiU7GX+/Nf12rjoi59WOzdvYvmkrly5E8NfvO+jZvhthpUvR6aUuln3WrfyNPTt2EXE+gs2/baTvcy/xVPtWNGzR2LLPG4Ne48NJ0y1//73vEBvWrCPifAT7du6l//O9MAszA18ZnCmG08dPsfbnX3ll/OtAer8gRaPhx29+YOuGLZw7dZYqT1TL0+PqO3wQyxcvJepqJM907Yi3rw/DevRn/849XL4Qwd4/d/HBmIlcuxIJQFDJEpw8epzzp89yO/oWaWlpuTouN4JKlmD/zj1EXY3kdvStLPfpN3Iwq75fwQ8Lv+XC2fMs+WwBm35ZR78RmZ+v3FIUhffnfsiFs+fp1a4z2zb+zqULFzn573HmfziH4T36A1CyVCjFShRn7rSPuXDmHNs2bGHJZwvuewzBJMYnsGvbn9yOvkVSYsFMd2APJhS73Ao7mehIkmRXrTskMefbmwQEWv9qLlbcxJxvbxbYPDpxsXFMfn0CbWq14M3Br1HzyVosXvUter3ess+Na9d5Y9BrtK3VnClvTuLZFzoya9GnVuVEXr5qVSOQkpLC7Pc+pF2dpxjWYxABgcVYtuEnPL29rI4TQjBh5FjemvqOZU4eZxcXPvh8FvNmzGbC8Dd4e+Z7BBQPzNPjatrmKYJKlmD+h5/i4urCt+t+JLBEECNfGsTTdZrz9rDRJCcl4+7hDkCXPj0IK12Kzk2fpn6pahzavT9Xx+XGiPGjuRJxmVbVG1G/VNYJ21Pt2zBu2iQWzZlPh7otWL54Ke/P+4g6jerl6XHfr2rNGqz44zeCQ0OY+MqbtK/TnGHd+3H2xCnGTp0EpNc4ffjVZ5w/dYaODVvx1ex5jHz7DatyatStxQv9XuL1vkNpEF6NhZ9kXrZALTKarmy9FXaKcOTa6SoRGxuLl5cXJadPQfMQJr7KD8Ws3qxc7cPLtdHq7opW8okrjg7BSlGNJ0O9WhIQHIjOKf9VLyYT7N/pxI1rWooWM1GrfkqB1OSomdpXV1czR38zmVONRF26ysfR24gyWc9qbUpI4WDnWcTExNh1Er4MGd9Jk/c8hbO7bdev5HgjE+tupmzZsrkedfW4Ufc3wEOmSVPQaNWZUKj6epmk5uBAm6LO1zSDJpv+LI6SHk96TLZEptFCnUbWHY7t/UiFnecIsTezul5aK2p/7hzNLBSEUEhJ05Fk1FvdZ0p7OHNfmcDmpqeMetW8jLp63MhER5IkSZJUyFGjrh43qnwG5s2bR1hYGM7OztSsWZMdO3bkuP/SpUupVq0arq6uBAYG0rdvX6Kjs5mPQZIkSZIeAfZc1LMwU90zsHz5cl599VXGjx/PoUOHaNSoEW3btiUiIiLL/f/880969epF//79OXr0KCtWrGDfvn0MGDDgIUcuSZIkSZLaqC7RmTVrFv3792fAgAFUqFCB2bNnExwczOefZ90zfvfu3YSGhjJy5EjCwsJo2LAhgwcPZv/+/Q85ckl6PIi70605ujOoJDmUSP8cmO3esywPIaBgtvEm5PBydSU6qampHDhwgFatrCfiatWqFTt37szymPr163P58mXWrl2LEIKoqCh+/PFHnn46+/VkUlJSiI2NtbpJkpQuzpyE0WzCnPLozigrSbYSqUaMZhMxpmSHxSCbruxDVZ2Rb968iclkyrTUe0BAQKYl4TPUr1+fpUuX8sILL5CcnIzRaKRDhw58+umnWe4PMHXqVCZPnmzX2CXpcZGCkb3JZ2h804CP4oPGSXf/QuTqofKRQ/ZeOdqe5KirbAiBSDVy+2Y02+Mukiwej1mU87Ko5+NGVYlOhvsXbktfJyXrD+WxY8cYOXIk77zzDq1btyYyMpI33niDl19+mYULF2Z5zLhx4yyLlEH6nAXBwcFoUhXLWilqY4hxdATZS/EpZJOj2FmKSX0fw/VJJzAJDU+aSqFXtJnWLlINlTevqbnZQO1Nk45KxIQQpJnN/H77EitvXUTglGkfc+LDefLMQrE5Wc44Xg4vVwk/Pz+0Wm2m2pvr169nquXJMHXqVBo0aMAbb6TPflm1alXc3Nxo1KgRU6ZMITAw8yykTk5OODllfvNKkpROABuTj/FXygk8NC7qauO+R5pZ3Ul2Upr+wTs5SJpR3c9dSrJjnjshBLeNKaqoyTHZYfVyW49/HKgq0TEYDNSsWZNNmzbRsWNHy/ZNmzbx7LPPZnlMYmIiOp31w9DenX5VTvosSbZJwUiKOe7BOzqI2hOdBJPB0SFkK1XliU5yqnqfO+nRoqpEB2DUqFH07NmTWrVqUa9ePRYsWEBERAQvv/wykN7sdOXKFb755hsAnnnmGQYOHMjnn39uabp69dVXqVOnDsWLF3fkQ5EkSZKkfLNn01VhprpE54UXXiA6Opp3332XyMhIKleuzNq1awkJCQEgMjLSak6dPn36EBcXx2effcbrr7+Ot7c3zZs3Z/r06dmdQpIkSZJUz4wGs41NT7Ye/zhQXaIDMHToUIYOHZrlfUuWLMm0bcSIEYwYMaKAo5IkSZIk6VGjykRHkiRJkgo7k1Aw2dj0ZOvxjwOZ6NxDmwxalfZfVvOcT9qUB+/jSELdfS4xmtQbYKpQ9yXCaFbxBwMwmdQbn0nlz505Vb2fC3Paw4nNnn105Dw6kiRJkiSpirDD6uXi7vGFeR4ddaf0kiRJkiRJNpA1OpIkSZKkQiYUTDbOrm3r8Y8DmehIkiRJkgqZhe3z4JhV2u/0YZJNV5IkSZIkPbZkjY4kSZIkqZDZDp2RbT3+cSATHUmSJElSITMKZhv72Nh6/ONAJjr3UMzpNzXSpDk6guyl+Ki7Edjpjro/6Dqt41dJzk58ipOjQ8iRVqPSD+xdKWnqvcQaVTxPDYAwqfdzq+bYpMxknZYkSZIkqVDGzMi23iB9wsCKFSsyd+5cBz+qh0+9PzckSZIkqRCzZx8dOWGgJEmSJEnSY0jW6EiSJEmSCpmxw1pXsjOyTHQkSZIkSY2EHUZdCZnoyERHkiRJktTInquXF2Yy0bmHPg60KY6O4tHjfFPdHyS1ThmQQafiAFNS1X2JMOiNjg4hR2YVD0MWZvXGBqDo1DtthZpjkzJT91VMkiRJkgopOTOyfchER5IkSZJUSDZd2YdM9SRJkiRJemzJGh1JkiRJUiG51pV9yBodSZIkSVKhjKYrW28gl4CQJEmSJOkxVpiXgJCJjiRJkiSpkOyMbB8y0bmXgmob89JcHR1B9oTW0RHkTB/v6AhylpBqcHQI2VIUdc8XkmZU95tPqPhLRsToHR1CjjQpKr0YAySbHsppZKJjHyp+J0mSJEmSJNlG1uhIkiRJkgrJGh37kImOJEmSJKmQwPbh4epufH44ZKIjSZIkSSoka3TsQ/bRkSRJkiTpsSVrdCRJkiRJhWSNjn3IROdeAtU2aKp5iHR8qEqftLucb6n7g66oOzxVM6p9eLlZvrj5pU1R73OnPKTYZKJjH3Zpujp79ixvv/023bt35/r16wCsX7+eo0eP2qN4SZIkSZKkfLE50dm2bRtVqlRhz549/Pzzz8THp1c9HDlyhIkTJ9ocoCRJkiQVRvZc66owsznRGTt2LFOmTGHTpk0YDP/N8NqsWTN27dpla/GSJEmSVCgJodjlVtjZnOj8888/dOzYMdP2okWLEh0dbWvxkiRJkiRJ+WZzouPt7U1kZGSm7YcOHSIoKMjW4iVJkiSpUDKj2OUGULt2bSpWrMjcuXMd/KgePptHXfXo0YMxY8awYsUKFEXBbDbz119/MXr0aHr16mWPGCVJkiSp0LHnqKt9+/bh6elpj7AeOTYnOu+//z59+vQhKCgIIQQVK1bEZDLRo0cP3n77bXvE+HCpe6S0KhnuqLsNOM3N0RHkTJjUO0Q6JVm9K6sDoPLV1U1J6p3BQ1F53w1tkqMjyEGKowOQ8sKmT6EQgqtXr/Lll1/y3nvvcfDgQcxmMzVq1KBMmTL2ilGSJEmSCh17dCaWnZHtkOiUKVOGo0ePUqZMGUqVKmWvuCRJkiSpUJMTBtqHTZ2RNRoNZcqUkaOrJEmSJMnO5PBy+7B51NWMGTN44403+Pfff+0RjyRJkiRJkt3Y3FPupZdeIjExkWrVqmEwGHBxcbG6/9atW7aeQpIkSZIKHWGHpitZo2OHRGf27Nl2CEOSJEmSpHsJQNg4sFDd4xIfDpsTnd69e9sjDkmSJEmSJLuzyyQPJpOJVatWcfz4cRRFoWLFinTo0AGtVr3zg2TFEC/QGmT+m2eKuqtGjS4P3seRtBqzo0N4dKm9Wt6s3vg0qeqNDbBDD9IC9JCeOjMKio0nMz+sYFXM5kTnzJkztGvXjitXrlCuXDmEEJw6dYrg4GB+++03wsPD7RGnJEmSJBUqch4d+7A5Zx45ciTh4eFcunSJgwcPcujQISIiIggLC2PkyJH2iFGSJEmSJClfbK7R2bZtG7t378bHx8eyzdfXl2nTptGgQQNbi5ckSZKkQsksFJuX6pATBtoh0XFyciIuLi7T9vj4eAwGla+TI0mSJEkqJYQdRl3Jbqe2N121b9+eQYMGsWfPHoQQCCHYvXs3L7/8Mh06dLBHjJIkSZIkSflic6IzZ84cwsPDqVevHs7Ozjg7O9OgQQNKly7NJ598Yo8YJUmSJKnQkUtA2IfNTVfe3t6sXr2aM2fOcPz4cYQQVKxYkdKlS9sjvofK5KSAQZ1vCkOciusfVRwagFnv6Ahylpyq8gBVzJSq7iksFBXPHKAxOjqCnDnddnQE2TOlPJzzyFFX9mGXeXQASpcu/UgmN5IkSZKkRrIzsn3Y3HTVuXNnpk2blmn7zJkz6dKli63FS5IkSZIk5ZvNic62bdt4+umnM21v06YN27dvt7V4SZIkSSqUMkZd2XoDqF27NhUrVmTu3LmOfVAOYHPTVXbDyPV6PbGxsbYWL0mSJEmFUnqiYmsfnfT/79u3D09PTztE9eixuUancuXKLF++PNP2H374gYoVK9pavCRJkiRJUr7ZXKMzYcIEOnXqxNmzZ2nevDkAW7ZsYdmyZaxYscLmACVJkiSpMJKjruzD5kSnQ4cOrFq1ig8++IAff/wRFxcXqlatyubNm2nSpIk9YpQkSZKkQkdg++wdKp/946Gwy/Dyp59+OssOyZL9mORUK/mmS3J0BDlLM9ncglxgtHqTo0PIkTHRbjNkFAyter9mtMnq/qWf5uboCLJnUvnbTrJm15crOTmZ5cuXk5CQQMuWLSlTpow9i5ckSZKkQkM2XdlHvn9KvvHGG7zyyiuWv1NTU3nyyScZOHAgb731FjVq1GDXrl35KnvevHmEhYXh7OxMzZo12bFjR477p6SkMH78eEJCQnByciI8PJxFixbl69ySJEmSpArCTrdCLt+Jzrp162jRooXl76VLlxIREcHp06e5ffs2Xbp0YcqUKXkud/ny5bz66quMHz+eQ4cO0ahRI9q2bUtERES2x3Tt2pUtW7awcOFCTp48ybJlyyhfvny+HpckSZIkqYI91rmSNTr5b7qKiIiwGj6+ceNGOnfuTEhICACvvPIK7dq1y3O5s2bNon///gwYMACA2bNns2HDBj7//HOmTp2aaf/169ezbds2zp07h4+PDwChoaH5eESSJEmSJD1u8l2jo9FoEOK/OrHdu3fz5JNPWv729vbm9u28rcqWmprKgQMHaNWqldX2Vq1asXPnziyPWbNmDbVq1WLGjBkEBQVRtmxZRo8eTVJS9j1QU1JSiI2NtbpJkiRJkprYc2bkwizfiU758uX55ZdfADh69CgRERE0a9bMcv/FixcJCAjIU5k3b97EZDJlOi4gIIBr165lecy5c+f4888/+ffff1m5ciWzZ8/mxx9/ZNiwYdmeZ+rUqXh5eVluwcHBeYpTkiRJkgqarc1W9ujM/DjId9PVG2+8Qffu3fntt984evQo7dq1IywszHL/2rVrqVOnTr7KVhTrF0YIkWlbBrPZjKIoLF26FC8vLyC9+atz587MnTsXFxeXTMeMGzeOUaNGWf6OjY0lODgYt6tGdHpjvmIuaAnF1DueUWgdHUHO9AmOjiBniakqfm1V/mtQ66LOz2sGc2zm5XHUQvXff2qOT82xSZnk+wrbqVMn1q5dy2+//UarVq0YMWKE1f2urq4MHTo0T2X6+fmh1Woz1d5cv34929qhwMBAgoKCLEkOQIUKFRBCcPny5SyHuDs5OeHk5JSn2CRJkiTpobJHZ2LVZ7QFz6afkk899RRPPfVUlvdNnDgxz+UZDAZq1qzJpk2b6Nixo2X7pk2bePbZZ7M8pkGDBqxYsYL4+Hjc3d0BOHXqFBqNhhIlSuQ5BkmSJElSA3v0sVF7rezDoLopWUeNGsVXX33FokWLOH78OK+99hoRERG8/PLLQHqzU69evSz79+jRA19fX/r27cuxY8fYvn07b7zxBv369cuy2UqSJEmSpMJDdZ0DXnjhBaKjo3n33XeJjIykcuXKrF271jJsPTIy0mpOHXd3dzZt2sSIESOoVasWvr6+dO3aNV9z+EiSJEmSasjFruxCdYkOwNChQ7Pt37NkyZJM28qXL8+mTZsKOCpJkiRJenjkEhD2obqmK0mSJEmSJHtRZY2Oo8SG6NAa5FOSV5o0R0eQM5N6R/gCYDap9xeXMKs3NgCRou65DRQVL/6uS3Z0BDnTxzs6guxpUh7iyWTTk81srtGJioqiZ8+eFC9eHJ1Oh1artbpJkiRJkpR3csJA+7C5+qJPnz5EREQwYcIEAgMDs53YT5IkSZKkPJCdke3C5kTnzz//ZMeOHVSvXt0O4UiSJEmSJNmPzYlOcHCw1eKekiRJkiTZg4Lt603IVhab++jMnj2bsWPHcuHCBTuEI0mSJEkS8F/Tla23Qs7mGp0XXniBxMREwsPDcXV1Ra/XW91/69YtW08hSZIkSZKULzYnOrNnz7ZDGJIkSZIkWZGdke3C5kSnd+/e9ohDFQzxAq1Bne+KVHf1trNqVDxXCIDhjjpf0wwxKp5HB5XPo6No1f3aItQ7J6vR2dER5Eyb6OgIsmd+WC9rIVy9/ODBg+j1eqpUqQLA6tWrWbx4MRUrVmTSpEkYDHmfGM0uL5fJZOKnn35iypQpvP/++6xcuRKTSeXffpIkSZIkqcrgwYM5deoUAOfOnaNbt264urqyYsUK3nzzzXyVaXONzpkzZ2jXrh1XrlyhXLlyCCE4deoUwcHB/Pbbb4SHh9t6CkmSJEkqdIRIv9laxqPk1KlTlulqVqxYQePGjfn+++/566+/6NatW766y9hcozNy5EjCw8O5dOkSBw8e5NChQ0RERBAWFsbIkSNtLV6SJEmSCqdHdNTVpUuXaNq0KRUrVqRq1aqsWLEi18cKITCbzQBs3ryZdu3aAelT2dy8eTNf8dhco7Nt2zZ2796Nj4+PZZuvry/Tpk2jQYMGthYvSZIkSdIjRKfTMXv2bKpXr87169d54oknaNeuHW5ubg88tlatWkyZMoWnnnqKbdu28fnnnwNw/vx5AgIC8hdPvo66h5OTE3FxcZm2x8fH56vTkCRJkiRJPLKdkQMDAwkMDATA398fHx8fbt26latE5+OPP+all15i1apVjB8/ntKlSwPw448/Ur9+/XzFY3PTVfv27Rk0aBB79uxBCIEQgt27d/Pyyy/ToUMHW4uXJEmSpEJJEfa55dX27dt55plnKF68OIqisGrVqkz7zJs3j7CwMJydnalZsyY7duzIsqz9+/djNpsJDg7O1bmrVavGP//8Q0xMDBMnTrRsnzlzJt98803eHwx2qNGZM2cOvXv3pl69epbJAo1GIx06dOCTTz6xtfiHSpMGql1vXb2jVEnxcnQEOdOpeJgqgEhT8Yur8pGpikblPS31ZkdHkC1FxUPfAYRqL8YPMTYHzaOTkJBAtWrV6Nu3L506dcp0//Lly3n11VeZN28eDRo04IsvvqBt27YcO3aMkiVLWvaLjo6mV69efPXVV7k+d6lSpdi3bx++vr5W25OTk3niiSc4d+5cnh+PzYmOt7c3q1ev5vTp05w4cQIhBBUrVrRUN0mSJEmS5FixsbFWfzs5OeHk5JTlvm3btqVt27bZljVr1iz69+/PgAEDgPSJgzds2MDnn3/O1KlTAUhJSaFjx46MGzcuT01OFy5cyHJ6mpSUFC5fvpzrcu5lc6KToUyZMpQpU8ZexUmSJElS4WbHPjr3Nx1NnDiRSZMm5bm41NRUDhw4wNixY622t2rVip07d6afUgj69OlD8+bN6dmzZ67KXbNmjeXfGzZswMvrv6YCk8nEli1bCAsLy3O8kM9EZ9SoUbz33nu4ubkxatSoHPedNWtWvgKTJEmSpELNjk1Xly5dwtPT07I5u9qcB7l58yYmkynTCKiAgACuXbsGwF9//cXy5cupWrWqpX/Pt99+a5ntOCvPPfccAIqiZFpxQa/XExoaykcffZSvmPOV6Bw6dIi0tDTLvyVJkiRJUi9PT0+rRMdWimJd0ySEsGxr2LChZS6c3MrYPywsjH379uHn52efQMlnovPHH39k+W9JkiRJkuxEhYt6+vn5odVqLbU3Ga5fv57veW7udf78eZvLuJ/NfXT69evHJ598goeHh9X2hIQERowYwaJFi2w9hSRJkiQVPipMdAwGAzVr1mTTpk107NjRsn3Tpk08++yzdjnHli1b2LJlC9evX89UM5SfnMLm8YVff/01SUlJmbYnJSXle8y7JEmSJEn2U7t2bSpWrMjcuXMfuG98fDyHDx/m8OHDQHoty+HDh4mIiADS++l+9dVXLFq0iOPHj/Paa68RERHByy+/bHOckydPplWrVmzZsoWbN29y+/Ztq1t+5LtGJzY21jJBYFxcHM7Ozpb7TCYTa9euxd/fP7/FO0SKl4LRoNKJQ1Q8XYhrlIqDA0xOKn1N71LMKo5PxfPAAJCo4slWQNXzX6meij8WDy02O4662rdvX6776Ozfv59mzZpZ/s4YdNS7d2+WLFnCCy+8QHR0NO+++y6RkZFUrlyZtWvXEhISYluswPz581myZEmuR2vlRr4THW9vbxRFQVEUypYtm+l+RVGYPHmyTcFJkiRJUmGV35mN7y8jr5o2bYp4wLLnQ4cOZejQofmMKnupqan5XuohO/lOdP744w+EEDRv3pyffvrJalFPg8FASEgIxYsXt0uQkiRJkiQ9/gYMGMD333/PhAkT7FZmvhOdJk2aAOltdyVLlsw01EySJEmSJBuosDNyQUtOTmbBggVs3ryZqlWrWpaWypCfuflsHnX1+++/4+7uTpcuXay2r1ixgsTExEwT/0iSJEmSJGXlyJEjVK9eHYB///3X6r78VqjYnOhMmzaN+fPnZ9ru7+/PoEGDZKIjSZIkSfmgYIc+Onf/X7t2bbRaLcOGDWPYsGG2hlZgCmJuPpsTnYsXL2a5/kRISIhlKJokSZIkSY6Tl1FXjxubEx1/f3+OHDlCaGio1fa///470zLraucUI9Dp1dmgmeqh3j5Qif7qjQ1Al+LoCB5AnW85AISah76j7hHIAGjU++IqRkdHkDNtsqMjyEHqQzqPHYeXPyqaNWuWYxPV77//nucybU50unXrxsiRI/Hw8KBx48YAbNu2jVdeeYVu3brZWrwkSZIkFU6FsDNyRv+cDGlpaRw+fJh///03311hbE50pkyZwsWLF2nRogU6XXpxZrOZXr168cEHH9havCRJkiRJhcTHH3+c5fZJkyYRHx+frzJtTnQMBgPLly/nvffe4++//8bFxYUqVarYZYZESZIkSSq0CmGNTnZeeukl6tSpw4cffpjnY21OdDKULVs2yxmSJUmSJEnKO0fNjKxGu3btslpqKi/yleiMGjWK9957Dzc3N8saGNnJz+Q+kiRJkiTZz6MyvPz555+3+lsIQWRkJPv378/3bMn5SnQOHTpEWlqa5d/ZkbMlS5IkSVI+2bHp6lEZXu7l5WX1t0ajoVy5crz77ru0atUqX2XmK9G5d0Kfgpjcx1HS3BTMKl293Ji/GruHQpPm6AhyZohVed1tmnqXuFaMKn/u1L54ebJ6X1ujq6MjyJlbpKMjyJ7y0IaXU+j66CxevNjuZdqtj44kSZIkSZI9HDhwgOPHj6MoChUrVqRGjRr5Litfic79bWg5+fnnn/NzCkmSJEkq1ApjZ+Tr16/TrVs3tm7dire3N0IIYmJiaNasGT/88ANFixbNc5n5qlf18vKy3Dw9PdmyZQv79++33H/gwAG2bNmSqa1NkiRJkqRcypgZ2dbbI2TEiBHExsZy9OhRbt26xe3bt/n333+JjY1l5MiR+SozXzU697ahjRkzhq5duzJ//ny02vQGc5PJxNChQx+Jjk+SJEmSpEqFsI/O+vXr2bx5MxUqVLBsq1ixInPnzs13Z2Sbe8otWrSI0aNHW5IcAK1Wy6hRo1i0aJGtxUuSJEmSZKPatWtbEgY1M5vN6PX6TNv1ej1mszlfZdrcGdloNHL8+HHKlStntf348eP5DkqSJEmSCjt79tF5VIaXN2/enFdeeYVly5ZRvHhxAK5cucJrr71GixYt8lWmzYlO37596devH2fOnOHJJ58EYPfu3UybNo2+ffvaWrwkSZIkFU6FsOnqs88+49lnnyU0NJTg4GAURSEiIoIqVarw3Xff5atMmxOdDz/8kGLFivHxxx8TGZk+8UFgYCBvvvkmr7/+uq3FP1RCm35TI6OboyPInjbJ0RHkTJ+g8k+6TsXxuZgcHUGOlBh1z5Ch5n6gah+No+a5w0zqnR7pkRccHMzBgwfZtGkTJ06cQAhBxYoVeeqpp/Jdps1XCY1Gw5tvvsmbb75JbGwswCNRPSZJkiRJqmaHpqtHpUbn999/Z/jw4ezevRtPT09atmxJy5YtAYiJiaFSpUrMnz+fRo0a5blsu+SlRqORzZs3s2zZMsuyD1evXs33kuqSJEmSVOgJO90eAbNnz2bgwIFZVpR4eXkxePDgfK+daXOic/HiRapUqcKzzz7LsGHDuHHjBgAzZsxg9OjRthYvSZIkSdJj7u+//6ZNmzbZ3t+qVSsOHDiQr7JtTnReeeUVatWqxe3bt3FxcbFs79ixI1u2bLG1eEmSJEkqnApRjU5UVFSWw8oz6HQ6S0VKXtncR+fPP//kr7/+wmAwWG0PCQnhypUrthYvSZIkSYVSYVoCIigoiH/++YfSpUtnef+RI0cIDAzMV9k21+iYzWZMpswjMy5fvoyHh4etxUuSJEmS9Jhr164d77zzDsnJyZnuS0pKYuLEibRv3z5fZdtco9OyZUtmz57NggULAFAUhfj4eCZOnEi7du1sLf6h8riQhk6vzvHlcebsq/QcLc3V0RHkLNVDxWN8ASXN0RFkT5jV+XnIIAzq/rmqpKr3vadNcXQEOTO6PHgfR3kUh5fXrl0brVbLsGHDGDZsmKPDyeTtt9/m559/pmzZsgwfPpxy5cqhKArHjx9n7ty5mEwmxo8fn6+ybU50Zs2aRfPmzalYsSLJycn06NGD06dP4+fnx7Jly2wtXpIkSZIKJztOGKj2mZEDAgLYuXMnQ4YMYdy4cQiRHriiKLRu3Zp58+YREBCQr7JtTnSCgoI4fPgwP/zwAwcOHMBsNtO/f39efPFFq87JkiRJkiTlXmHqowPpfXvXrl3L7du3OXPmDEIIypQpQ5EiRWwq16ZEJy0tjXLlyvHrr7/St29fueSDJEmSJEk2KVKkCLVr17ZbeTYlOnq9npSUFMskgZIkSZIk2dEjVCOjVjZ3qRoxYgTTp0/HaDTaIx5JUi2BmTinM9xyPUic0xkEZkeHJEnS46wQzaNTkGzuo7Nnzx62bNnCxo0bqVKlCm5u1qtP/vzzz7aeQpIc7rbLES55ryJNF2PZpjd6EXznOYokVXVgZJIkSVJObE50vL296dSpkz1icbg0Dy1CpcPLnW+pt/Yg1V3dYy1dr9u2AvdN7384V+LbTNvTtDGc8/2aCud64nenSv7L16i46VfFoQFoktT93lPz8+cU7egIHl1K6kM6TyHrjFxQbE50Fi9ebI84JEmVBGbOBq9J/+P+Ly0FEHAueA2+dyqh2GeNXEmSpHR2HF5emOX7ymw2m5k5cyYNGjSgTp06vPXWW1nOaChJj7IY9/OkGmKy/2WuQIohhhj38w81LkmSJCl38p3oTJ8+nbFjx+Lm5kZgYCCzZs1i5MiR9oxNkhwuVR9r1/0kSZJyK6PpytZbYZfvRGfJkiV8+umnbNy4kdWrV7Nq1Sq++eYby2yGtpg3bx5hYWE4OztTs2ZNduzYkavj/vrrL3Q6HdWrV7c5BkkCMKTlbibR3O4nSZKUa3YcdVW7dm0qVqzI3LlzH+pDUIN899G5ePGi1QJbrVu3RgjB1atXCQoKyndAy5cv59VXX2XevHk0aNCAL774grZt23Ls2DFKliyZ7XExMTH06tWLFi1aEBUVle/zS9K9FLM2/UKRXdOVAKc0L7ziwx5mWJIkSXmi9iUgClK+a3RSU1OtlnhQFAWDwUBKim0rxc2aNYv+/fszYMAAKlSowOzZswkODubzzz/P8bjBgwfTo0cP6tWrZ9P5JSlDrNtFjpZdaOl0nKlT392/S13qIDsiS5Jkf3IeHbuwadTVhAkTcHX9b+nq1NRU3n//fby8vCzbZs2alevyUlNTOXDgAGPHjrXa3qpVK3bu3JntcYsXL+bs2bN89913TJky5YHnSUlJsUrIYmNl/wrJWqzbRf4t8xUmbQpecaUodqMO50usS++YnEGBkMutbRpaLkmSlB05vNw+8p3oNG7cmJMnT1ptq1+/PufOnbP8ndelIW7evInJZMq0QmlAQADXrl3L8pjTp08zduxYduzYgU6Xu4czdepUJk+enGl7or8GrZM6f5lrH9K8DfmhTXN0BDlL9czb3Ehxzuc5EfQVZk0KHonhlInqj1Y4Uf1iTeJczpGqjSXa4yB33I9zy/cUAUktUWyYMEWj4tfW7Kze+ZsAdIkqnqgGSPVS7/OX5qnu506r4kG85oeVPMjh5XaR70Rn69atdgzD2v0JkhAiy6TJZDLRo0cPJk+eTNmyZXNd/rhx4xg1apTl79jYWIKDg/MfsPTYiHM+Z0lyPBNLU/Zqf7TCAICCBs+k0gB4JoVz2PUD4lzOE+N6Au/ECo4MW5IkScqGzRMG2pOfnx9arTZT7c3169cz1fIAxMXFsX//fg4dOsTw4cOB9Pl9hBDodDo2btxI8+bNMx3n5OSEk5NTwTwI6ZEV63yOk0FfYtakZkpy7mcweVHsTkMifbZyyW8tXhHlZD8dSZLsS9bo2IWqrswGg4GaNWuyadMmq+2bNm2ifv36mfb39PTkn3/+4fDhw5bbyy+/TLly5Th8+DB169Z9WKFLj7hY57P3JDllckxyMgTebo7W5Eyi01Wi3Q8/nEAlSSo05Dw69qGqGh2AUaNG0bNnT2rVqkW9evVYsGABERERvPzyy0B6s9OVK1f45ptv0Gg0VK5c2ep4f39/nJ2dM22XpOzEupzlZPGv0pOchLKUi+yHRugfeJze7Ebg7WZc9lvHZd/1+MRXQ4M610qTJEkqrFSX6LzwwgtER0fz7rvvEhkZSeXKlVm7di0hISEAREZGEhER4eAopcdFrMsZThZfiFmTildCWcrmMsnJUOxOI6557yDFEM0Nrz0ExGSueZQkScoX2XRlF6pqusowdOhQLly4QEpKCgcOHKBx48aW+5YsWZJjR+hJkyZx+PDhgg9SeuTFuJy21OR4JZTPc5IDoBVOBN1qCcAVn02YHtayxpIkPfZk05V95LtGJ7e1KjnNZqw2hjiBNkWd7wp9ojrjAkj1UPcwVU1a5ucuxu00p4ovwqxJwzuuPOUu90YjdOTn50+xm3W55r2VFMNtrnvsICg6cwf4nChm9T5/Spp6YwMwq65O+j4a9X5u1c45Wr3PnSlVvbFJmeX7MhEaGprlkO97h4IrioLRaMx/dJJUAO64neJE8CKExnhPkpO3mpx7adARfKMNZ4KWccXvDwJuP4nO7PrgAyVJknIim67sIt+JzqFDh7LcLoTghx9+YM6cObi7u+c7MEkqCNZJToV7anJs4xdTgyu+v5PkHMVVv62UvN7ODtFKklSoyUTHLvJ9ha9WrVqmbZs3b2bs2LGcOnWKN998k9GjR9sUnCTZ0x23k5wIXozQGCkSV5Gyl3vZJcmB9MkES15vx8mSi4n02UGxWw0xGAvnAnpS4SIwk6icw0gsOjxxFaXknFKSqtjlKp+xPtWOHTsYMGAAa9euxd/f3x5FS5JdWCc5lSh7uafdkpwMReIr4p4YQrzrRS77babUteftWr4kqU2scoQo7UqMyn9rwOmEFwGmjniKqg6M7PGg3L3ZWgZA7dq10Wq1DBs2jGHDhtlY6qPFprT7zJkzvPDCC9StW5eiRYty7NgxPvvsM5nkSKpy2/34f0lObCXKXrJ/kgOgoFiarK4X2U2y/qbdzyFJahGrHOGKdglGYqy2G4nhinYJscoRB0X2GLHj6uX79u3j2LFjhS7JARsSnaFDh1KpUiViYmLYv38/33//PaVKlbJnbJJksxjn45wssQShMeITWzm9JqcAp4/ySgzHO74cQjFzqejGAjuPJDmSwEyUdmX6H/dXOdz9O0q7CoF6FzV9FMjh5faR7yv+/PnzcXZ25vr16/Tr1y/b/Q4ePJjfU0iSTWKcj3HWbwlCMeETW4Uyl196KDMXl7zejjvuJ7npdYji0U1xSyle4OeUpIcpUTln1VyViQJG7pConMNNlH54gUlSFvKd6EycONGecaiCyUkBgzrnDdGmOTqC7KW5qu85i9Uf5aJHepLjd6sy5c90vzulianAz+0dV4yiXlW54XuEyz7rqHyqT477m/XqXTZCm6TuTqUalc9eoernz4Zf+kYlNnf7EZvv86R4q++6ksGU8pBik6Ou7EImOtJj594kxyulGuXPvIBGPNxkIvRSa274/MutIseJcb+AV3zoQz2/JBUkncjdiMLc7iflQCYqNlPxzw1JyrsY/b/3JDnVKRn/0kNPcgBcUvwodr0WAOeD1yHk1Up6jLiKUmiES/Y7CNAKd1yF7LcpOV6+a3SaNWuW5czI91IUhS1btuT3FJKUJzH6f7jo8TUoZrxSalAyvgcKWnBQh8iQK09xvehBYj0vcNvrJD4x5R0ShyTZW7xyDDPJ6X8IrDsk3/3bJJJJUE7jLso5IMLHgz06E8vOyDYkOtWrV8/2vtjYWJYtW0ZKSkp+i5ekPIkxHOGi+zegmPFOqUGwJclxHKc0L4pfa8Dl4ts4H7yeIjFl5URq0iMvQTnDFd03oAhcTeGkam5aDTHX4YXG7EqqJpJLuq8IMvXA01zDgRE/wmQfHbvId6Lz8ccfZ9pmNBqZO3cu77//PkFBQbz33ns2BSdJuRFj+JuL7t/eTXKeIDi+u8OTnAzBkU2I9N9NglskN3yP4B9d3dEhSVK+JSmXuaxbiFCMuJsrUcLUB0zK3VFYsehE+szIAjNXtd8Tpz3MFe13GInHx9zI0eFLhZTdJhRZunQp77zzDklJSUyaNIlBgwah06l9aWHpUXfH8DcRliSn5t0kRz21JnqjG8GRTbkQvIELJTbid6uKQ/oMSZKtUrjOJd0CzEoKruZwgoy9LD8o3ERpq5oDBQ1BppeIwo3b2r+I0q3EZErAz9Qaxea5fgsP2XRlHzZnIuvXr2fs2LGcP3+e0aNHM2rUKNzc3OwR20Nn1oGi0twsxUu9FwdDvGM+Sbdc/ybC/TtQzPjG1yQ0utvdi6i6PtlB1xpwpdhfJDtHc63oPopff9LqfsWk3tdWKfjR+DYxOavrtb6fyVm9E+bp43OfcKdqbnPJ4wtMSjwuxhKExfZHi/4BR2kowfMYnN2Jct3ATe1GzGnxBCU+n6sfI1oV93xQUh/SiWTTlV3k+6fv3r17adasGR07dqRZs2acPXuWCRMmPLJJTmEgMBOnP8Ntp4PE6c880rOW3nI9xDm/jCSn1t0kRz01OffSmp0oeaUFABFBmzFpHtZVUpJsZ1TiOe/xBWna2xhMRQmLG4gW51wdq6AQkNyaoIROIBRuOe8kwv1bzKh8AiTpsZLv+osnn3wSFxcXhgwZQmhoKN9//32W+40cOTLfwUn2c8fpCFfcV5Km/a/ToN7kRVB8R7xTHq3F99KTnKWgCHzjaxMa3VW1SU6GwOt1uFxsOynOt7kasJPgyKaODkmSHshEMuc9viRFex29yZtScS+jEx55Lsc3pQFasxuX3JcSY/gbk0ciIXF9c50wFVay6co+8p3olCxZEkVRWLlyZbb7KIoiEx0VuON0hAueSzJtT9PEcMFzCaGxfR6ZZCfa9SDn/b4HReAXX4eQ6C6qT3IANEJH6JVWnAxfzqXiWyl2vQ56k6ujw5KkbJlJ46LHYpJ0l9Ca3QiLG4zBXCTf5XmnVUcb58pFj8XE609zznMeoXED0ecjcSo0ZNOVXeQ70blw4YIdw5AKisDMFfccFt8TcMV9FV4plVWfMES7HeC877L0JCeuLiG3Oqs+5nv536zOpcCtJLpGcTlwO2GX2zg6JEnKksBEhPt3xOtPoxFOhMUNwtkcYHO5HsaylIodynmPL0nSXeas56eUihuMwexrh6gfQzLRsYtH51tCypd4/bn05qrs+rsqkKa9Q7TzLkyKenv/WSc5Tz5ySQ6kj0QJu5Se3Fwp9icp+tytFyRJD5NAcNltBbGGf1CEltC4friagu1WvqspmNKxI9CbfEjV3uSM5xyStFftVr4k3c+mMUZms5klS5bw888/c+HCBRRFISwsjM6dO9OzZ88HzpwsFTyjJndfppc9f+IyP6E3FcHZGICzqVj6/40BOJsC0OY03XsBu+m2nwu+P9yT5HR65JKcDD53KuAZV5JYjwgign6nzIXnHB2SJFkIBJEuv3DbaS8IhZLxvXA3lrH7eZzMRSkdO4LzHgtI1kVy1uMzwuIH4GaUS0bcS/bRsY98JzpCCDp06MDatWupVq0aVapUQQjB8ePH6dOnDz///DOrVq2yY6gFT2NKv6mRNiV/71a9cAGvB++nMblg1iaRpr1NmvY2cZywLsfohUtaAM5pxe7+PwCXtAB0ZldS3Qsuob3ltI/LbulJjk9yfQJTnyfNPW9JjtZHXXMGBMW0J9ZjHtf89+Cf1BRNmp+jQ8qW0V3dI/PUPDQfUHWducgitutOv3PTZSsAQUkv4GmqkuV+9qDDi1IJw7ng9hWJuvOc85hPyYReeBorA5Cm4gG8pod1SZFNV3aR75dryZIlbN++nS1bttCsWTOr+37//Xeee+45vvnmG3r16mVzkFL+JBgucclnVc47CdCbvKl6ZTwmTRJJ+iiS9VEk6aNI0l8jWR9Fmi6WNF0MaboYYl1OWR2uN3rgJIrhdLfmx8mUXhukE+55jldgJkF3jjRNLHqzJynam1xxW5E+uiq5PsUTOj0Wk415JoXjlVCOGLeTXPbZgDcvOjokSeKWYRdRLr8BUCypAz6pdQr8nFrhQlj8YCLcviVOf5SLbosJSuqKT2rdAj+3VHjkO9FZtmwZb731VqYkB6B58+aMHTuWpUuXykTHAQQmIr1+J9JrI0IxozW5YNIkpd95/+J7QMlbz6KgQWd2wyOlFB4p1tXHRiUpPfkx/JcEJeujSNXdJk0XRxpxxOtPWx2jNbvjbLon+TEWw8kUgE54ZJmsxBiOcNXVevh7xuKAvskNKJ7w/GOR5GQIjm5HjNtJoj0O4WJuhpOmuKNDkgqxGP1hrrj8CEDR5BYUTWn60M6twUBIQh+uuPyP2077uOK6HJMSj29a88fqM58fihAowrYqmYzja9eujVarZdiwYQwbNswe4T0y8p3oHDlyhBkzZmR7f9u2bZkzZ05+i5fyKVl3g/N+35PgFAFAkYRqhNzqTJzTGSJ8VpGmu3ceHW9K3nqWIkk5Dy3XCRfcU0NxTw212m5SkknSXyfeI4oUbRTJ2ihStNdI1d7CpIknQRNPgv6s1TFaswtOpmJ3k6D05CdVcyu95uZ+d0eFuaWVfuwueG4pJfCJq84tj8PcSl1HoHN/R4ckFVJxupNcck2fl8onpR4Bye0eegwKWoKSuqEV7tx0/oNrLr+RpomnWMozj2x/PLuwY9PVvn378PT0tDWiR1K+E51bt24REJD9cMOAgABu376d3+KlPBIIbrrv5lKRNZg1qWjNzpSM7oRPYg0UFIokVcX7SmXinM6Rpo1Fb/LEI6WUTRcRrXDGPbUkhpQQq+1mUkjW3iBFey09+dFFkay9RqomGpMmiUTNeRL15+9/ANmODIt0XY1XapXH7oIXHN2GW+5HSDQfI8l0HhdtmKNDkgqZRO0FLrotRigmvFKrUzzJcc3DCgqByc+gEx5cc1lDtNM2jEo8JZK7qWaRXunRlO9Ex2Qy5bhop1arxWiU03w/DKnaWC76/I8Y1+MAeCSVJiy6GwaT9eReCho8U0oXeDwanHA1lcDVVMJqu5lUUrQ37tb8pCc/SbpLpGnvPHD4e4LuHO7Ggo/9YXJOK4p/bB2ue+3mVtpvFNcMkyMVpYcmWRPJBbcvEUoq7mnlKJHYQxU/JoqmNEVnduOy63JiDAcwKQmUTOqNBidHh/bQyVFX9mHTqKs+ffrg5JT1my8lRb1zsjxObrse4aLPCozaRBSho8Ttp/GPa6iKC9b9NBhwMQXhYgqybLttOMglj+8eeGxaLofJP2qColtxw2s/yebzJJpP4Kat4OiQpEIgzXSLK+5fYNIk4WIMISShDxrb13i2myJptdEmuRHh8jXx+hOcV74gJLE/OlQ8FKsgyFFXdpHvd3bv3r0fuI/siFxwjEoSl3xWEe2+HwDXlCDConvgklbMwZHljd6cuzbj3O73qDGYvPDSNeSOcSu3Utfi6lwORVFfkio9PkymWK7fXoBRE4uTqRihCQNUWVviYaxIWOLLXHT9iiTdBc67fUZo4mD0wtvRoUmPmHwnOosXL7ZnHKqQ6g5a9X3eAfC68d98JjFuZzld4gdSDXdAKATdaEbw9ZZohA4csSK5DRNtFCEMvZtX9rM33x3+XuR2GEo+f5qY9PkO76Hwcm5OTPxuUsVV4sVh3HVPODokC02KupvS1D6PjmJUV9JqEklcT16I0RyNwehDmejB6M3qrCVxijXjTgjuhmGcKPElKfoozjvPocLlQbik+js0NmPaw7nOyqYr+1DXp1DKkVlJ40KxXzka9gWphjs4pfhQ+dwQQqLa3k1yHj0KGkreei79j/s/kPcNf39caRU3vA3p0zTcSlmPECqdtVJ6pJlFKteSvyLVfBWt4kHpW4PRm3Mxm6iDuaYWo1LEMJxTipKqv8PR4M+Id45wdFgPh7DTrZB7fL89HjMJzlc5Ej6Hq0W3gSIIuFWX6mdewzPx0R+pUySxKuE3eqM3WV909SZvwm/0pkjio7Gyui28DI3QKu4YRTRxaXscHY70mBHCRFTKN6SYL6DBmWJOg3AyFXV0WLnmZPSh0qXhuCUFY9Qlcix4PndcTzo6rAKXUaNj662wezSrAQoRgZlo/Vauh69DaEzo09wJv9IFn7iKjg7NrookVsU7sTJxzvcMf0+2bfj7o0SjOOFtaEl0ykpup27CXV8LjWJwdFjSY0AIMzdSlpFkOo6CnmLO/XHSPnoTVOpNblS89DKngr4mxu0UJ0ssIjyyG35xNRwdmqRyMtFRsVTlFlecvidRew4An5hKlLrSGYMp78srPAoUNHgmP15DyPPCU/8kManbMIpbxKb+ibdTc0eHJD3ihBBEp64i3nQI0BDg1Btn7aO7cKZWOFHucj/OBv5AtOdhzgR+j1GbQLE7DR0dWsGQo67sonD8XH7ECAS3dXs56zKTRO05NMKJ8MtdKBfR+7FNciRQFB1FnFoDcCf1d0wi0cERSY+622kbiTX+BSj4O3XHVffoT1+gQUfpyB4E3G4AiuBCwCou+a5HPKbf6LLZynYy0VEZI/FcclrMVacfMCspuJrCCE8aTcDtOo/dMghSZu66J9BrimEmiZjUrY4OR3qExaTt4E7aRgB8Dc+pajSfrRQ0hF5/jhI32gBwxW8z5wN+Qjhi1KmkerLp6h6GONA6cJ7DGKejRHgtx6iNRxFaAuPa4J/QDAUN8UEPPt5RFJUPEipyKtXRIeQovsS9fXG0+CvtuKJdREzKdvwSG6HDcXMImZwddupc0SU5OoKcmfWO+XESw36iWQWAL63xS20E930MFJXnBIn+D/4d7kMrRII7V1x/4rr3bpJdEyiZ8BIaCnZOCVPKQ6ojECL9ZmsZhZys0VEBk5JChOf/OOezEKM2Hue0YpS9+SoBCS0KTWdc6T/uohIuIhShpHFTs8nR4UiPmHiOco0fAPCmEb60cnBEBcs3tT4hCb1QhJZYwz+cd1+AiWRHh2UXctSVfchvUQeL15/nhN+HRLvtBqFQNL4J5W6+hqtRxVU4UoFSUChqSl9B+o6yi1RuOjgi6VGRyFmu8jVgxpOa+PNsoWjy9kqrRlj8IDTCiQT9Wc56zCVNiQPSR67G685wW3+QeN0Z2bxVCMmmKwcxY+Sa+0ai3LeAItCbvAm50x2P1DKODk1SAVdK42YuR4LmJDc16ylufsnRIUkql8xlrrAQgRE3KlGMboWqRtjdWIbwuGGcd19Asu4KZz3m4JfclBsum0nTxFj205u9KJ7YEa+0R2B+Ljnqyi4Kz6dARZJ01zjlN4coj82gCIok1qL8jTdkkiNZKWp+GoBY5RDJXHVwNI83gZkE5QwxykESlEfvV38qN7jMAswk40IpitMTBa2jw3roXEwlCI8bgcHkQ6o2mquuP5GmxFjtk6bEcNFtCTH6Iw6KMvcUs31uhZ2s0XmIBGZuuP7JVc9fEYoRrdmV4JjOFEmu7ujQJBVypgQe5urEaQ5zQ7OWYPMAR4f0WIpVjhClXYnxni9EnfAiwNQRT6H+X/1p3OES8zERjxNBBNEfDYV3skknc1FKxQ3nhNeUrL/lFUDAVddVeMZULlS1XoWVfIUfklTNHc74fMEVr1UIxYhHcjkq3HhDJjlSjoqa24DQkKA5RiLnHR3OYydWOcIV7RKMWP/qNxLDFe0SYhV1/+o3kcBlvsDIbfQUpQSD0OLi6LAcLlV7M+eqDAXSNHdI0J17eEHlh1zryi5komNnAjNxhjPccj5InCG9CvyW80FOFJ1JvNNpFKGnREwnwm8PeiQW1JMcy4A/XqIOADe0vz62k6I5gsBMlHZl+h/399e9+3eUdpVqm7HMJHOZL0klCh1eBDMYHR6ODksV0pRYu+7nKHLUlX3Ipis7uuN8hMueK0nT/vfrUDHrEZo0AFxTgwm58yLOJv88l22IVe+7NdVL3aM6dAlpjg4hR4o552aGouZWxOr3k6ScJ1Ecx108Xuuc2cLomv9jE83nMJpist9BASN3uO10AHdNtfytPVZAOZJZGLlqXkwyEWhwpbhmMFrFR6UpWf64X87/BF0mN3fIxSTyntfdcU/I+3mMaQ9p8jA5j45dyETHTu44H+G895JM24UmDQR4J1cn9M6LhbKDoGQbPd4UMTfklnYr17VrcTOWl/0K7CBF5K6Dd5R5GVHmZWhxR6f4oMcHvVIEHT7olfSbjiIFtgirEGaSOIdJxKJVPHEWoUSJ70jiNAoGgjQDcVKKFci5H1WeCWEYUr1I1cdkrq0DEGBI88YzIeyhxyY9fDLRsQOBmcue2VSB35VguJD9nZL0AL6mFtzR7CZFc5VYzWG8zI/PdP4PkxCCZHGeO+btxIvc9b9R0CNIw0Q8JhFPChFZ9nvIMhG6e8tvIhQvjnDDvPK/PkQCFAwIUlHQUlzTD2clJM/lPu4UNIRFPsvJkt+kv1b3XnrvvnZhkR1U/4PBHk1PsulKJjp2EW84Z9VclYkCado7xBvO4ZFaeFfnlvJPhxu+pmbc0K3jhnYdnuZqsnYwD8zCSLw4xB3TdlK4cs89OsCY7XE6vAnVvY2ZFIzcIk2k39L/fdvybzPJuUuEMmqBuKdGKJtEKF4cIdK8JFM54u5aDl5KY1yVsvl4NgoH39gqlIvoxfnA1aQa/rs+G9K8CYvsgG9sFQdGl0tyHh27kImOHaRpctnxLZf7SVJWfMyNuSV2kKZEc0ezhyLm+o4OSfWMIo4Y805izDsxkT5TroIOD6UW3tpGpIkbRJqWZHt8Ue1zKIoGLS5oCcJJyXrGcpNIupv8RJMmbmdKisykpCdCxJMiIv478J4voXsTIZ3wJpY9OT62eHEIP/E0iqLuWglH8o2tgk9sJWLdzpOqi8Vg9MQzIUz1NTmSfclExw705twtupjb/SQpKxqc8DO1JEq3kpvajXiZaxXq+VJykiwuc8e0nXhxCEF6x1EdXnhpGuCleRKtkt5T1UkJJJA+3DCttBpirsObotrncNfkbh4drZJNImROby4zk0Qa/9UEWRIhHpAI5cDIHf7f3p2HN3XdCR//Xu22ZMn7CgaMwexrTCAEAiEbtCTQZJrpvE3JNO1MszaTTtq0mb5JM52maUkmnckybScJyfu0aTYga5sQwpYQdoMhgA3YxuDdeJEt29ruef+QLdt4t2RLts/nefRI9+rcq6Pja+mnszZTQCSylrg3ChpsjsmhzsagyKar4JCBThBYXBnovTbfNOM9dHzTq9FYXBnDnjdpdIlRl1AjduFWaqjR7CFeXRXqLIUNIbw0ihPUqXtoEe3zo5iUiURrlmFR5qAoXZv7LJo5mJVZNIsCvNjRYiVCyQhaTYmiKGiJREskMK7LZ0R3gVCTOE0TeX2e2yvssuvfaCZHXQWFDHQ6MF3yotMPbthghqv3jm8ZF28m0i6AwZ3fawrfqlal5y4OYaFuagBjkIeB1zSQ1DrixE2Uiz9zSfcZVmUJWmUI31+Yf0ZqPOCliXr2U8vneKhte4Yo5hHDMiLEhH7822mwhKxmxBcI6dsCISCCtH4FOnphRTPIkc4aT3j/cRvGh28fNK8zfPMmdRW+354jTFvHN4O78ySABnc0WcXfGRkd36QRIYoFGEhGpZlasSPU2QkZl6ikgnc4xxNU8T4eatFiJpbryODfSOXbRDAyRyRFkIGO3icU1RFNBLKWeDQbyRMGrl+/npiYGG677bbQZKADWaMTRLLjmzQcFEVDPGsoFS9Ty26ixTJ0ytjo/yWEoIk86tTdNHHav99ACjEsw8qCUdFvSUFDIuspZVOPaRJZJz9bRrsRPOrqgQce4Lvf/S6vvvpqaDLQgQx0gmwkd3yTRg4zMzExkRaKuCS2kaTcGuosDSlVOLGLw9SJ3bipbN2rYGEGMSwngkyUUdZZJYo5pHInlXTtKJ3IOqII/wVHpbFr5cqV7Ny5M9TZAGTTlSSNSIqiEK+sAaCeL3GJ6hDnaGi4RS3V6vsUqk9QJd7GTSUajEQry5mg+Slp3EUkU0ZdkNMmijlk8HPGcw8pfJvx3EMG/yaDnDEiVE1Xu3fvZu3ataSmpqIoClu3bu2S5oUXXmDSpEmYTCYWLlzInj17An/DQ0TW6EjSCBWpZBIpsmgij0vib6Qo3w51loJCCEELhdSpe2gkl7a6dz1xRCvLiFIWoVUG1IN7RFPQyCHkY5UqfLdAzzFADoeDuXPn8o//+I/cemvX2uI33niDBx98kBdeeIGlS5fy+9//ntWrV3Py5EnS09MDy+8QkIGOJI1g8crXKBZ5NHCESDUTRTGgw0oEwRseHQyXr9fUXf58sxcfpU7sxslF//4IphCtWY6Z6WH1niRpyAWxj47d3nnCWqPRiNFo7PaQ1atXs3r16h5P+cwzz3DXXXfxve99D4Bnn32Wjz/+mBdffJEnn3wywAwHnwx0OvCaNCj68PwgNdaE7xjuhjR9qLPQK3N5uK/pPPihqiZlHCbh66tTwZv+DzUdNhJYT5QSWBOH1xR4T0aHJ5dq11a8on29Jq1iI16/DrNuDh7RQIP7S+yevXhF++zFFt1CbPplGDQpQNtC4J3zE3ExvJusmpJCnYOeNceFd9npm0Kdg164Q52BgRs/fnyn7ccee4zHH398wOdxuVwcPnyYRx55pNP+G264gb179waSxSEjAx1JGsEaRC4tFHXZ76GeMrEJuDPgYCcQDk8uFc6uoy68op4K56uY3JNpUYtom+hGq1ix6pZi1bfPXixJY5VCEGZGbr2/cOECVmv76MyeanP6Ul1djdfrJSmpcxSflJREeXm5f/vGG2/kyJEjOBwOxo0bx5YtW8jOzh7UawZKBjqSNEIJoVIltvSapkpsxcKskDT5CKFS7draa5oW9RwARk06Nv0yzNq53c5eLEljUhBnRrZarZ0CnUApSucaQSFEp30ff/xx0F4rUDLQkaQRqpmCTsOOu+OhjrPiZyhC1zrnigIo/sdK6zZounmsgWbFf0zH4/yPlcv3t5/HKxrbm6t6Eaf/BjbD0kGWgiRJwyk+Ph6tVtup9gagsrKySy1PuAjLDikDGba2efNmrr/+ehISErBarSxZsiSsIklJGioe7H0nAgQuVJpaF41swIsdD3V4qMVNDW4u4aYKF5W4qMBFGU5KcXIRp3oBp1qMUz2PUy2iRS2gRT1Hi3qWZjWfZm8ezd7TNHlP0uT9iibvcRzeXBzeY/7amr5oNRGBFIMkjVrhODOywWBg4cKFbNu2rdP+bdu2cdVVVwX3xYIk7Gp0Bjpsbffu3Vx//fX86le/Ijo6mldeeYW1a9eyf/9+5s+fH4J3IEnDQ0f/qqGT+BYmJR1fd16BQHR47Lvv+Fj4h3qoeA3qZekveyx82x2PaXvsViuxe77oM3/aMTKrsyQNWIhmRm5sbOTs2bP+7cLCQo4ePUpsbCzp6ek89NBD3HHHHVxxxRUsWbKEP/zhDxQXF/ODH/wgwMwOjbALdAY6bO3ZZ5/ttP2rX/2Kd999l/fff18GOtKo1rYeUm/NVzqisSoLB91Hx6Mb/KesECoO74lem6+0SjQmjVyvSZKGWnZ2NlqtlnvvvZd7772317SHDh1i5cqV/u2HHnoIgA0bNrBp0yZuv/12Ll26xBNPPEFZWRmzZs3io48+YsKE8FxbLqwCnWAMW1NVlYaGBmJjY3tM43Q6cTqd/u3L5xeQpJFAUTQksL51dFX3EpR1IZt7RlE0xBvWdTvqqk284RY5N44k9UARAiXAzshtxx88eLDfnZFXrFiB6ON177nnHu65556A8jZcwirQ6e+wtd48/fTTOBwOvvnNb/aY5sknn+QXv/hFl/16h4pOH55zrjhSw+pP1YnWFeocjHABfI75lgK4k6pu1kNKaFsPKYDz65oDm2vFxlw0yp1UiW7yp6wjyj0noDlJRHhPBRPW/xuRlSFa7bGfwnmeH+9wvZBK2wRSgZ1jjAvLb8++hq315PXXX+fxxx/n3XffJTExscd0P/3pT/1VceCr0bl8MiVJGimilDlYxKzWUVj2sJsZOUqZg4XwzZ8kSaNbWAU6gQxbe+ONN7jrrrt46623uO6663pN29vU15I0EilKeK+HFO75kwZPo6rMqywgvtlOdYSVo4kZqBoZxAZDMJuuxrKwCnQ6Dltbv369f/+2bdu45ZZbejzu9ddf57vf/S6vv/46X/va14Yjq5IkSWPeivO5/OjgVpKa2pslKyJtPJ29jp0T5ArrAQviqKuBdEYebcIq0AH6HLb205/+lJKSEl577TXAF+R85zvf4Xe/+x2LFy/21wZFRERgs9lC9j4kSZJGsxXnc3lq16tdvocTmup5ater/OSaDTLYCVQQZ0YeSGfk0Sbs6hdvv/12nn32WZ544gnmzZvH7t27Ow1bKysro7i42J/+97//PR6Ph3vvvZeUlBT/7Yc//GGo3oIkSdKoplFVfnRwK4KuXyIafJUIDx18F40qe8JKoRd2NTrQ+7C1TZs2ddreuXPn0GdIkiRJ8ptXWdCpuepyGiC5qY55lQUcSZZ9swYrGDMbB3tm5JEoLAOdUGkYp0VrCM8FBUV4ZgsI7yG0AJFlLaHOQq+U2eZQZ6FnwzaOdnAUmb9B07UM/hswqZ9zjyXZ7eiiB/c6+gCnNhhKmuH6zAti09VYFnZNV5IkSVKYEoJ5lee4Lb/n9Qc7WlBxFqvTMcSZkqTeyRodSZIkqVda1cs1Jcf5h7xdzKi5ALQPBuqu3kW07r+lcD83Fh/hbxMW8taUqym0JQ9TjkcHRfXdAj3HWCcDHUmSJKlbJo+TrxUe5O/zd5PmqAHAqdXx4cRsCqxJ/ChnKyqdmwZUfEHOm5lLmVtdRFZdCesK9rGuYB8Hkqbw5pRlfJkyDSEnjOxbEJuu5PBySZIkSWoV09LAbWe+4Bvn9mJzNQFQZ4jkncylvJO5lDqTBYBLETYezNlKUnN7x+SqiGienX8Lu8bNBiGYW13IN8/sYXnJCRZVnGFRxRmKLfG8PeVqPpp4BU16U0je41gzloeXy0BHkiRJAiDdXsnf5+9mddEhjKoHgIuWOF6feg0fTbwCp87QKf2ucbPZkzqTudUFxLc0UG2K4lh8h5mRFYVjCRkcS8gg2VHDrWe/4OaC/aQ3VvNQzlb+6cTf+GDSIt7OXEqpJW643274C+KEgWOZDHQkSZLGuNnVhfyf0zu5uvQkmtZvxhOx6fx52gp2p87qdUkHVaMhJ7HvIeTl5lien7uWl2fcwOrzh/i7M58zoaGKv8/fzTfz97AnbSZvTrmanITJ0I+1DccCuQREcMhAR5IkaQzSqCpXl37F/8nbyexL5/3796TO5M9Z13AsftKQBBzNeiObM5eyZfISFpXnc/uZPSwuz+OakhNcU3KC/OhU3ppyNe9bF+LS6YP++tLYIwOdDtwWUMN0rU9jXahzMHK5bIa+E4WQCOMfr4YwHxnsjgp1DnpnqAt1DroyelzcfOoQ3zm8k/SGagBcGi1/nXQFf55+DedtvS+gHDxavkyfzpfp05lYX8Hf5X3OmoJDTK0r5dGDb3L38Y/YPH0Jb02/impzeC3nM2zzI8l5dIJCBjqSJEljQHRzI7cf/4K/z/2C2OZGAOoNEWyespS3spZSExG6jqpFtiR+u+hW/mfuam4+t5/b8r4gpamW7+VsY8Oxz9iWMZfXZy3nZEJ6yPIYEgLfMLZAzzHGyUBHkiRpFBtXX80dObu45dQBIjxuAEqssbwxZTnvT15Esz58qrEbjJH8acZK/jJtOUsrv+JbJ3Yzv6KQNWePsObsEY4mTeQvM5fz2aTZeDVhPF18kMg+OsEhAx1JkqRRaHb5eTYc2cGqc8f9HYxPJozj1QUr2ZY5B0Nd+M5j49Vo2Z4xl+0Zc5lWdYFvndjDjQU5zKsoYl5FEeXmaN6asZQt0xZTbwrjJVTCiJxHR5IkaQzTqCoLLxSQ0GinymLl8PiMXkcahStFqCwvOsWGIztYWFrg379nwjReXbCSg2mZHToYj4xf+qcTxvPYyn/gv678Ored2sutp/aS7Kjj/oMf8v0jn/DhlIW8Pms5hTGjcNZlQRD66Pju5Dw6kiRJY9R1p3P56bYtpDS0T3pXFmXjyevX8+m0OSHMmY9GVVlQWkB8k53qSCtHUrsGYQaPm6/lHeY7OTvJqK0EwK3R8lHWAl6bv4KzcSkhyHlwXYq08vuFN/HyvOu48VwO3zqxm2mXSrj19D5uPb2PfWlTeX3Wcr4Y33nWZY2qMr+8vfxykkdQECs7IweFDHQkSRqzrjudy7ObN3XZn9RQz7ObN/HgN+4MabCz6mwuP96zheTG9iCs3GLjN8vWsz1zDlEtTXzz+F7+IXcP8U0NADQYTLw9awl/nruMSkt0iHI+dNxaHR9MzeaDKVcwv7yAb53YzYrzJ1hcks/iknzOWxP4y6yreX/qIhZfzOPhL7eQ5Ggvvwqzjd8uWc+OSaEPYqXhIQOdDnRNoB2uYYMDpIbxX0rrDHUOeqcaw/vXm64p1Dnoma451DnonQjgT6tRVX72yRbf48ufw1fj//hf36JOH4nDGEGTwUCTwUST3kCz3tCvOWbUAGY2WHUml41/3dRlf2JjPU//dRO7J00n++I5It0uAMot0fy/hct5Z/ZiHMa+l1XQuMP7l35/FqM8mpTB0aQMUhpq+ObJz1mXv58J9ip+sncLD+z/AJPX3eWYBEc9v/l0Ez9ZtYEdEwcX7CjqMJVd28JhgZ5jjAvjr09JkqTg0qpeMqvLmV1WzLVnjneqKbmcAsQ2O3jtjRe6PKei0Kw30GQw4GgNfpoMxtZ7ky8o0htbb23PGf1pOh7TrDfiMBhp0en9wZNGVXnks96DsGsKTwGQl5DCpitW8res+Xi0o38kUnfKomL53ZU384cFN/K1M4f41le7mWCv7jatBt93/4/2vcuu9N5nfQ41OeoqOGSgI0nSqJXUUMfs0vPMKStmdtl5ZlZc9NeA9FdVZBQoEOlyYXb7qi81CMxuJ2a3kwRHQ1DyqqL4AyRVUfoMwgA2Ll/Lq1eskEsmtGrWG3l7xlIKoxP5/V//p8d0GiDZUcf8igIOp/S9fIU0sslAR5KkUSHC5WRW+QVml7UHNkmN9i7pGg1GjienU22OYu2pI32e98dr7+Bguu/LUBEqJrebSJeTSLeTSJeLSLcT82Xbka7Wfa4O+9zdbXcOniwuJxZX/9uCKy02GeR0I765f8FnfFPX6yOsyM7IQSEDnTHEP3rDYafa3P3oDalnGqEyp7qQOKedS0YrufGTUBVZfqGgUVUyasuZcc4X0MwpLWbypXK0l32oexQNZxJSOJ6STm7KBHJT0imMS0QoGjSqSvaFcyQ21ndpHgJf80ZFVDSHx2X49wlFQ7PBSLPByKV+5LM//Uz8wVOHAGlBSQE/3bG1z2OrLGNzuHBfqiP7Vy79TRcyMtAJChnojBGrzuby493djN5Y7hu9IfVueelxfpj7Lokt7eVXabLxuzm3sDt1dghzNjJoVJX5ZQUkNNmpirSSkzKwIDvBUc+sivPMrihmVsV5ZlRd9NeEdFQWFU1uygRfYJM6gVOJaTQbup/5V9Vo+PWq9Tzz7iZUOveFaYtPnrp23ZD/GOgUPLXOfZefkMo/HtzRZxB2JC2jm2elnKQMKsw2Ehw9l1+lOZqcpLFTfnLCQGlUW3U2l40fbeqyP7Gxno0fbeJf19wpg51eLC89zi8PvNZlerX4lnp+eeA1/m3Rd2Sw04trz+Xy8OdbSO4wxLfcbOO3V6/ns8ldrzuT28mMqosdAptikh11XdI59EZOpIzvUFszgeoB1nB8OnUOD91yJ49s7/wjoCIqmqeuXcenU0Pzf6FqNPz62vU8814vQdjKoQ/CRipVo2Hj4nU8tf3VHsvv6cW3hH/5BbFGR04YKAGgbwKtJ9S56J7GM7iLXaOq/GRnz6M3VODHu7byRcLMQf/T61rCu2rUaR38h5lGVXngxLsIei6/+0+8x/apswddfsO2EvIgeANc+H3V2Vx++/GmLvsTHfX89uNN/OvXNlAYm8Ts8vPMLjvP7PJiMi+VoxOd23y8isLZuGSOJ0/geHI6x5MnUBCbhKGuQ/8UB0Q6Bn4t7rXNZt26mcyrLCC+2U51hJWjib4ap8iKwK5tEcAgqC/iZvPIyg38aP9Wkpo61CSao3lm0S18ETcbU83g82esDdMPu1ZaV2AjyPZFzeLni77TpSa2KiKa/5p9M/uiZhFxaXBjrz3uYRqzLYeXB4UMdEa5eRUFnT4kL9c2+mBB+TkOpU4ZvoyNEPMqzvVdfk113Jr3BTsmzOFSRFSnWVnHsr6CbAFs/PDVbpsWKiw2f0CTm5zOycTxPTRBBSfIVjUajiSH3+ibHRPnsCt9FvMqOgRhSbJvXX/tTp3N5ykzR2zfOjm8PDhkoDOKxTXZWZ/3Zb/Sbtz+MnvHTedA6lQOpE6hNCpuiHMXvhIddWSXnyG77AxLL57s1zEPH9zKwwe34tToKLfEUGqJpdQSS1nrfUnrfb3RPCSjZDSqysKL7R3ND48b3i9DjaoS29xIQmM9SY31JDjszC0t7HOItAK0aHXtNTUpvvvROKPvYKkaDUfkEOhBUxUNRxMmhzobUgjJQGeU0apelpTkcUv+Pq6+cKpLE0BPzB4X1xcd4/qiYwBciIpjf+pUDqRO5VBKJg3GyKHMdkjZWhpZWHGO7DJfcJPe0P1EY72pNlmIcTowqh4m2KuYYK/qNp1DZ6TMHwjFUWqJpag+lhJbLBdtcf2a0fZy1+Xn8shnXTua//ra9YH3MRGCKGcziQ47iY31vkDGUU9Co50ERz2JjfUkOuzEORr6fa1d7hfXfZMPp18RWD4laTSSo66CQgY6o8Q4ezU3nznA188cJKG5fW6I3Ph0JtiriHI19zz6INLGoyu+zaKysywqzWd25XnGN1xifN6X3Jb3JV5F4VTceA6kTmF/6lSOJ07ErR25l06E28m8ygJ/YDOltgxNhyaQtvd7MHkKh5Mz+L973yS+qZfRG5HRrPvGoygIkhx1pDbW+G8pHR4nNNsxe5xk1pWTWVfefpJD7Q/rTJGU2GIpsbYHP6W2GEpscZRYY3DqO3eauS4/l2fe29QlX4mN9Tzz3iYeuvnOHoMdo9tFosNOQqPdVwvTWE9iQ70/qPEFNnYiPF2n0e+OV1GojoyiymKj0mwDBNcWfNXncRWy9kaSuqcKUAIMVIZruYowNnK/rSSMHjfXns/llvz9LCw/599fazTzYeYVvDd1EYXRyawsyuWpHT2PPnjmynXkJmWQm5TB/867gUh3CwvKC7iyJJ9Fpflk1Fcwq7qYWdXFfDd3O806A0eSMjiQOpX9qVM5H5EU1pOW6b0eZlWd54rW5qhZ1cVdah/ORidzKHkKB1MyOZI0GYchwv/cxux1PLWr+/JTgGey20dvlEbF9djsZ/S4SXLUktpYQ1qHICi5pYY0ew0xzQ6iW5qIbmliZsXFbs9RZY7yB0Gl1li+eWyvvwmoo7aO0o9te4v4RjsJTfb2IKahnkRHPbaW/i9kVWeKpMpspdJso9Jio8rS/rjSYqPKbKUm0oJX096BVKOq/O3lf+99iLRFDpGWJGloKULIei273Y7NZmPuhl+hNQy86WA4dBx1lVV9kXVn9nNjwRGiXC2A79f0vtQs3p16JXvGz8BzWY3LyqLcLqM3yltHb/S1sF2Co55FpflcWZrPorIzxF0262i1KYqDyVM4lDSFg8lTqYq0Bfp2A6JRVabWlnBFxRkWVpxlXlVhl8X9SiyxvjynZHIoeQo1EVG9nnPF+Vx+dPCy8ouM5pnsW9g5IbDmoaZEX5gS6Wohrb6GtPoaxtXXkGpve3yJtPqaAc2Y21/NOn1rsGJtDWDaH1dabFRF2Kg2R+HUDW741aqzuTz9wSag+yD7R18PbGqDQEYdDYdARl0NNcvF/tXUhYrHHL6F53G3sO/Dn1NfXz8kQ7bbvpOuy/ghOm3380D1l8fr5NOC3w1ZXkcCGegwMgIdm8PBTeeOcMuZ/WTVlPr3l1hieX/KIj7IvIIKS0yv59CoauCjN4Rgcm05V7YGPvMrCojwdF47qMCaxMFkX9CTk5hBk75/ZapRVeZWFRDf3EB1RBTHEvqZPyGYYK8ku+IMV1ScZX7FOazuzrUVl0xRHErO5GDKFA4lZw6qs7VGVbsdghyotkCnV0JgbWliXH178LPkfD5Xnc/v89ATSePITZnQJYiptNhoMJp6rY3TBGEE8qqzufxkZ+c+RGWWaH6zYl3A8zfJQGfwZKAzeMMX6DyAThNgoKM6+bTgv8Z0oCObrjpoSoQAg+egUoTKoqJz3HZ0H9efPo7R6/vWcWp1fDZpNu9mXcnBtMx+D2f2ouXghECHkCvkJ6eSn5zK/1uwAr3Xw+yqIhZfyGfxxTxmVF4kw15Bhr2C2/M/x63RcDxpAvvHTWXf+Cy+ShzfqXmjzUAnlUtpqCH74lkWXcxnUclZEi5bs6bBYOJw6mQOjJvCkZhMCqKTA25eG6ohyN5+xYEKtRFmamPMHGc8AMeK0vsV6Pzm+ps5OHFw+TZ036d6QHalzWHPt2Yxv7yA+CY71ZFWcpJ9QaKu/61n3TLVhvEkRIDXEL5Nui3x4f3xbztRG+os9MjjDX7tqjR0wvtKH6OS7HWsP3aQbxzbz/i6Gv/+vLhUtk67kr9mLsBuMocwh+3cWh2H0zI5nJbJ86zB2uIgu+Qsiy/ks+hiPun2SywoK2RBWSF3H/yYBoOJQ2mZ7Bs3lX3jp1JsS+DaguO9Tir38I13kpOaQfbFM2SXnGHRxTOk2zuvNNSi1XEsZRIH0qawf9wUTieM8wdU+sbw/tU/WIfTMyiLspHU0EsfGGs0h9ND3wdG1Wg4nCqHSEvSgMhRV0EhA50wofd6WHHmJLce3c/V5077FydsMJr4cOYC3p94Jafix4V1p18Au8nM9slz2T55LgCp9kuttT2+wMfmbGZl4QlWFp4AoMxsI9rZ3GOHWgH8ettr6NXOnYc9ioavEtM5MC6TA+OmkJs0EZdOP+TvL5yoGg1P3rieZ9/ueZmAJ2+QywRIQ0uoCvaqNNzNFvQRjVgTSlA08ss1KFRBwJNito66kmtdSSGTUV3BrUf3c0vuIeKaGv37D6RP5p15V/LJ9Dm06A1EBKEJIRRKrXFsnrmEzTOXoFFVplWXsPhCHosunmF+WQEpjp4nlANf8NMW5OTHpXBg3FQOpGVyJHUyjjDtTzWctk2bw4O33clPP95CSkOHtZqs0Tx5wzq2TZNrmElDp+ZCJkWHr8XV3N6Z3xDRwMSFnxE7/mwIcyZdTq51JQWNRlVZWFxAQqOdKouVw+ldO6xGupzcdPIotx7dz4KLRf79VZYotszJZvO8KzkfmzDMOR96qkbDycTxnEwcz8sLr8PkdvH9Q5/w3ZzP+jz2l8tv451ZVw1DLkeebdPmsH3qrD6vO0kKppoLmeR/fnOX/a5mC/mf38zUq9+TwU6ghOq7BXqOMU4GOkF0/encLr+sy6JsPHnjerZlzWZuyXluO7qf1SePYm4dKuxRNOyaMp235y1mT+a0bjvqjlYtegN706f1K9ApikkchhyNXKpGM+gOx5I0UEJVKDp8bevW5Y3OCiAoOrKSmLRzshkrELKPTlDIQCdIrj+dy7Nvb+qyP6mhnt+9vYnyKFunAKgoNoF35i3i3dnZVEWNzepEgJyUDMrNNhIdvcw8bIkmJyX0HWolSfKxV6V1aq7qSsHVZMVelYYtqfvJL6V+CGIfnbFMBjodGOygHcS8aBpV5Wd/63mVZoCUhnqatTq2TZnHlhlXciQ1w9exWAVD791UAIioDt9htI2pg6+FUrUafrN8PRv/2nOH2t8sW4dHP/hmGF1LeP+jJ+8P3/lMVF14d35XjeHdPKcJ339brNsH36x0ydG/2kP9QQfREYN7HdeM9EEdNxw8nrFT8z4ayEAnCBaUFvS6SnObh1dvYM+kmcOQo5Fle+Yc/nX1nfx4T+dJ5Sos0fx2WeCTykmSFBx29zjOONZS5erf/2S+4xYURSXZeAQl0DWbxiLZdBUUMtAJgvjLJqvridktJ5nqyfbMOezImMWC0vZJ5Y6kyg61khQOGjxpnHV8jQrn/NY9KhrcqBjo2kcHfM0tghY1jmP273FOW0qm+UOSjDky4BkIQRACnaDkZESTgU4QVEf2r49Nf9ONVapGw6FxskOtJIWLRk8yZx1fp9y5sHWPSorxEJPNH9HoSeGo/Z/wfZN2DHZ836yzozbRrCZQ1LSKRm8qR+3fx6K9SKb5I5KMR2XAIw0bGegEwZHUDMottr5XaU6VHWolSQp/jZ4kzjnWUOa8graec8nGQ2SaP8SiKwfAoqtgHn/gVOM3cart6+yZNLVMs7xFsukoABMidlDUdC3nm1fR6B3HUfs/EaW7SKb5AxINx8J9DtTQkk1XQSEDnSBQNRp+s6z3DrW/XSZnqJUkKbw5PAmca1pDacsi2j7Jkow5ZJo/JEpX0iV9sukoScZj1Lgzcao2jJp6YvVnO9XW6DXNTLF8yMRIX8BT1HwtDZ5x5NT/gCjdhdaAJ1cGPN1RVdq/RQI5x9gmA50gkR1qJUkaqZq8cZxzrKa0ZTEC34iiBEMuU8zvY9X3PjxcUQRxhjN9voZe08QUywdMiPyMoqbrON+8kgbPeHLq78aqKybT/AEJhuMy4JGCTgY6QSQ71ErSyCSEQn31ONxOM3qjA1v8xTHRh6TZG8M5x2pKWq7yBzjxhhNMMX+ATX9+SF7ToGliquU9JkZubw14VmD3pHOk/h6suiIyzR+SYDgxJK894simq6CQgU4HQgMiwOkRvFoNBycEv0OtrimMqx/DfDZnY60n1FnoVTjPVWOod4U6C0Ou8lIWeYU34nS1DxYwGuxkTfqYxLi8gM7tMYfnQrPOFguT/+5F/vhHcLX+ia+/Hn7xC1iyZBYwa9jyUl0NGzfCc8+B3TGRI/X3smgRuHb+B3EcDcsaHo0YprmvZKATFLKqQZKkMavyUha5ebfhdHWe5dfpiiI37zYqL2WFKGdDw+k0cyZvFfu++Geef94X5KxcCbt3wyefwJIlw5+n+Hj49a+hsBAefhgiI+HAATjKoxzkP7gk5srv6iDIzs5mxowZPP/886HOyrCTNTqSJI1JQijkFd7YutX9ek15hTeQEJs/4puxXK5IiouupPTCfFTVV8t09dXwxBO+QCccJCTAb34D//qvvvv/fNqJnank8G/YyCNDvEksY6zTchCXgJCrl0uS1CchFOpqx+FyWjAYG4mOGRv9OEYTIRQcTfHUN6ZRVTO1U3NVVwpOl43Ci0tJTczFaLCPuC9ZlyuCC+cXUVK8AFX1rW9jtZUwcfLn7N59e1i+n8REX1PWgafv4zy3cJEbqCeLHH6OjdNMFm8Qw4mwzHuwCaEiAlx9PNDjRwMZ6EhSP1RVTOVs3iqczg79OIx2MrO2k5CUH8Kc+QihUF/THoTZYmUQJgQ4XVbqG1OxN6RS35hGQ2MKXnVgC9oVXFhBwYUVGPSNWC2lWC1lrfelGPTNQ5T7wLjdJi6cz6akeCFerxGAKGsZEyd/TmxcAYpC2AcKRqWOqbzKBPEuRaynhOuoZxpHeIxoTpIh3iBWORnqbA4tIQJflFO2+8lAR5L6UlUxla9y13XZ73RG8VXuOmbO2RrSYKe6fApnT63C1dIehBlMdjKnbyc+ue9hv6OFx2PE3pjiC2wa06hvSMXl7rrCtlbjxGopw2BooKJ6dp/nNZlqcLZE43JbqK6dSnXt1PbnjHX+oMcXAJWh04auA7fHbeBicTYXirPxenwBjiWqgomT9xAXfy7sg5vuGJU6snilNeBZRwnXUccMjvALYsQJMniTGOVUqLMphTEZ6EhSL4RQOJN3XetW9/048k/fQERkDVqdC63Wg0bjRqv1DEuNSnX5FE7mrOuy39USxcmcdcyYv3VUBjuqqqGxKRF7Yyr1DWnYG1NxNMdz+d9IQcVirsBqKcVmKcUaVYI54hKKInxNkfYJrR2Ru1+vyWiws3T+i6iqloamJOyNqa23FJqa42lxRtPijKby0gz/MeaIan/wY06owhJViWaIlzH3eAyUFC/kwvlFeDwmAMyWSiZO/pz4hDMjMsC5nEmpYRovM1Fsba3hWUUtszjMLGLEcSbzJtHK6VBnM7hEEProyBodGeh0FFmlotMH2B6qKtir0nA3W9BHNGJNKEHRhMeFNlTNG9qWIGQOX9k1lqfhbjKjj3RgSQ5O2WncPf9NVVWD02XB2WKlxWnF6YzC6bTS0uJ73NQcg9dr6uXsCm6XmUP77ur6uooHjdaNVuNGo/W03vu22wMiN2qUikbrQaNr3a9zt293eHx5GkXxcvZ070HY2bxVRE0tHHQ5Gur7TtMfQijU2tNxuSwYDI3EWIv7fe0JAc3OmNbmJ1+g0eBI9neq7ShCU41NX4RNX0S0rhCr/gJaxe37rmhovXUw3fQ6R109r9c03fQX9Od8E+YZOU88rcmiwG02YfdMoN49gfrW+xY1DkdzAo7mBMqq5kIhKIqHKFMFtoiLWCNKsEWWYDFWDuD9K9Q6JuL0RGHUNRBjLvIf6/EaKK65kqKqq3F7zQCYjZVkJm0nyXoSRRVQ0f15Vyfd3a/XDxXtrO5HvJmBmXxOhus4hVXLuVi7kFoxm0PMJs58lsmJ24kxX/Cn7638Bkt4nTAcrWaqCkqAfWxkHx0Z6ARTzYVMig5fi6u5vbrcENHAxIWfETv+bAhzFv7NG3UFmVzcuwK3o73s9OYGxl21k+iMwZWdEOBpMlNvj8XZEtUayFhpcUb5AxuXy0L3v+YHRqtxItB0+vJVhQ7Vo8NDRO8Hlwf88j1QcDVZKTh4PebYCnSGFt/N2Ox/rNW7hvzX/kDnqXG5I/w1J221NW5PZJd0Om0zNm0h0brC1uDmPEZNQ5d0vUk2He3Xek3d0WtaiDPkEWdofw9ONcoX+Lgn+oIf7yTcXjP25jTszWn+dFrFRVREKbbWwMcacZFIQ02Xv0VF/QxOlX0Np9vm32fU15OV/DecbiuFVctweS0ARBqqyEzaQbLt+JjonxVhqGdG2vtMSthDQdU1lNQs4FJjJpcaM4mznCEzaTtOt7Xb8pue8iFJtlHev0fyU4SQ9Vp2ux2bzcYVt/0Snb63X+89q7mQSf7nN7dudf1lOPXq9wIKdgx1g6/67ty80TVvgTZvNKQHNilaXUEmhdu+3rrVNX+Trv+gS7AjBHhbTLgcUbgbo3A1RrU+tuBqjMLtiMLlsIDa92SGiuLBaGzAZLRjNLXeG+2YjA04XZHk5X+tz3PMn/snYmKKfX0HVR1erx5V1eP16lrv9XhVPapX13rv2/Z6dTgjTb5tjw7V40ujevWontbzeNq3fen0vtktg0FR2wMgQws6Y0un7UiHHb2uBb2u2X/T6VrQ61r69WXaNk9N64t1eMZ37Kypm4kw2qlvaO1X05hKc0ts12wqHqLamqCiSrBaSok01aA/13X9pcEQQul1vabBUhPjaHFHU9+cRn3TOOqb07A3p+JVu37O6LRN2CJK/TU/Lm8kJ0vWtT7bteza9kUYLpGZuIPk6Fw0A/j1r1RcGtR7Gi4iKW5A6Ztd0ZyrXEFp7Xz/LM+Xl1XHffPSXx90sOPxOtl+ciP19fVDMmS77TtpleUf0CkD6zx/OY9wsb3xz0OW15FA1ugEgVAVig5f27rVfRNC4aFVGMz1vl9sQkEIBWi973Gb1m0Nukbh+/e8PG3rfXf72tIWnVnWa97yj9+Ey2VCo1FRFIGiUVFQffeKQFHU1psA/+P255prtK1pVdC0pteo0HautmM1befsXHYX967oNX/Fu6/HUZ2AxxHVKYgRnn4EWIqK0dDYGrjYfQGNqT2QMZnq0eubeqzVEEKh6PwynM5e+nEY7URH+6rKFQW0Wg9arQfo34icppSBf5Cpqoa6sgnk7/5Gn2ltKQVotF48LhNepwmPy3dTvb6AyeOMxOPsWmPSF5222R8E6fTNnQIina4ZnbaFs+d7b1o7kf+Nbp6DSNMlrFGl2Cy+oCbKXDGk/Vz6u17TwM8LEYY6Igx1JNu+AlqHuDvjW4MeXwDU0JKMxxvpr5Fod3mTGh22VWakbiUt9uiAApzRKsJQx6xxW8lI3MW5ihWU1i2g+/9Z37V3umwNidZTYV37JVQVEeDfVg4vl4FOUNir0jo1V3Wl4G6xcOLj7wxbnvpPweOJ4OxXq4fxNVuDHsXX0U54ewtYFLxOE5U5i7t9VhfhQG9uxGCxY7A0orc0YDA3+O4tDegjHcSeGfwQYEURTMncxomvvkFP/TimZH467B+WGo1KTEoRhogGXM09Nb8JDJENTFu+tds+OqpHh8dl9Ac+HlcEng6BkMdlglo9Hk8Ebo8JtycCtyfCP1zZ443A442g2RnT5dz948uzVtNMjO1Ca21NKVZzKXp9kDp+hSFFEVhMVVhMVaTFHAXwdXZ2JmFvSqO+eRyXGifR4o6l92ZVDWZjrQxyLhNpqCUt5iildQt7SaXQ4o6m1jGRWEvhsOVNCg0Z6ASBu9nSr3RafUtrJ1IA0VpD4qsBAdq38d37nvMFAxpV9e9XoNNzne47HK8gcLZYaKhP6ylLfuaocgzGptbaIQ1CaFprhDQdbl23ERpUpXVb9T2P2v5c9xRQtQMaSxCVdh5L6oXLgplGNNqhHc0CkJiQz6yZmzlz9vou8+hMyfyUxITQDC1XNIKJCz9rbTLtPgibuGBHjx2RNToPBp0HQ6Sjx9ewnO8aJKqqBk+HwKctCPJctu1oSqCxKanP9zFt8t9ISfiqz3SjmUbjbW22KmU8Bymrm0PuhW/2eZzT09sPrLGrv+US9uUnR10FhQx0gkAf0divdFOXvYst6eKgXmOwfXTqLo0n98C3+kw3efoOouMu9JmuOz310RECf9DjC4Q0oCrtQZKqobEslfM71vT5GkkLDhCVOriyC4bEhHwS4s9QVzcep8uC0dBIdPSFkFd7x44/y9Sr3+vaCT6ygYkLdgxJJ3iNRsVgaMJgaOo1XU39BI58dUef5zMa+vf/M5YYdf3rVN3fdGPNqCk/tfVHbyBkoCMDnY5sJ+vQaY0DPs4q6ijQ34TTbaWnJgSTvp7xlSdQqgZ30SlNzkEdFyGqydOuxum19Zw3bR0p1TkolwaXt8jDg/+iEuII5ZolONXonvOnqWXyvh2DDio8VdWDzt/l4jgctHO1iU6ID+x4zjLJ8jE1xss60+YLCLCyKZCyixHHMbIaJ7F0v36wipEaYr76YNBDaMN7XXpQZ6QP6jhLbBXGUnuffcMs6VW4FfOgXkNzIrCV2YeaLqp/NeXdiVXyMGrr+vzci1XyUJoG/rmiqIP7PJZCQ65eHgSKIpie8mHr1uX/NL7taSkfheTXv6IIpse/2ykv7VrzFv9eyGomFEUw3fJmp/y0a82f5a2Q15yEu7bOtKmmQ8QZzoRFeSmKShav4PuiuTyQUQGFLF7xN91K7dr6hvl0/38Rir5hI0W4f+71mxC+eXACuoX5exwGMtAJkiTbSealv45Rb++036SvD2gYYzAkW04wL+k1jNrOs7+ZtHXMS3qNZMuJEOXMJ9l0lHnWP2DU1HXab9LUMs/6h17nMpHCW6JygDlsxEhNp/1GapjDRhKVAyHKWfhr6xtmNHZuXjEa7cyauTlkfcNGinD/3OsPoYqg3MY62XQVREm2kyRaTwV9Fs5gSLacIMn8FTUtk3B6rBh1dmJNhWGRN/AFO0nGY0Myl4kUWonKARLEIWqZhosYDNQSw2lZk9MP4do3bKQI98+9PgmVrrWhgznH2CYDnSBTFBG2wxUVRRAXURDqbPRoqOYykUJPUVRih2XO/NFHUQQxMcWhzsaIFe6fe9LQk4GOJEmSJIUhoQpEoOtyyT46MtCRJEmSpLAkm66CQgY6tEe8Hm/4DhkM6+GMqivUOeiVR7hDnYXehXH5hX3ZhTnVE74zPGvC/W8bxp95ntb/2aGuLfHgDni+QA9h/nceBnJRT6CgoIDJkyeHOhuSJEnSCHLu3DkyMjKCft6WlhYmTZpEeXl5UM5ntVpJSUlBo9Fw7733cu+99wblvCOFDHSAuro6YmJiKC4uxmazhTo7I4rdbmf8+PFcuHBhzK6MGwhZfoMny27wZNkFpr6+nvT0dGpra4mOjh6S12hpacHlCk5tr8FgwGQyBeVcI5FsugI0Gt90QjabTf7TD5LVapVlFwBZfoMny27wZNkFpu27YyiYTKYxHZwEk5wwUJIkSZKkUUsGOpIkSZIkjVoy0AGMRiOPPfYYRuPAF/Qc62TZBUaW3+DJshs8WXaBkeU3ssjOyJIkSZIkjVqyRkeSJEmSpFFLBjqSJEmSJI1aMtCRJEmSJGnUkoGOJEmSJEmj1pgJdF544QUmTZqEyWRi4cKF7Nmzp9f0u3btYuHChZhMJjIyMvif//mfYcpp+BlI2e3cuRNFUbrcTp8+PYw5Dg+7d+9m7dq1pKamoigKW7du7fMYed35DLTs5HXX7sknnyQ7O5uoqCgSExNZt24deXl5fR4nrz2fwZSfvP7C25gIdN544w0efPBBHn30UXJycli2bBmrV6+muLi42/SFhYWsWbOGZcuWkZOTw89+9jMeeOAB3nnnnWHOeegNtOza5OXlUVZW5r9NmTJlmHIcPhwOB3PnzuW5557rV3p53bUbaNm1kdedL2C599572bdvH9u2bcPj8XDDDTfgcDh6PEZee+0GU35t5PUXpsQYsGjRIvGDH/yg075p06aJRx55pNv0P/7xj8W0adM67fvnf/5nsXjx4iHLY7gaaNnt2LFDAKK2tnYYcjdyAGLLli29ppHXXff6U3byuutZZWWlAMSuXbt6TCOvvZ71p/zk9RfeRn2Njsvl4vDhw9xwww2d9t9www3s3bu322O+/PLLLulvvPFGDh06hNs9dpa8H0zZtZk/fz4pKSmsWrWKHTt2DGU2Rw153QVOXndd1dfXAxAbG9tjGnnt9aw/5ddGXn/hadQHOtXV1Xi9XpKSkjrtT0pKory8vNtjysvLu03v8Xiorq4esryGm8GUXUpKCn/4wx9455132Lx5M1lZWaxatYrdu3cPR5ZHNHndDZ687ronhOChhx7i6quvZtasWT2mk9de9/pbfvL6C29jZvVyRVE6bQshuuzrK313+8eCgZRdVlYWWVlZ/u0lS5Zw4cIFNm7cyPLly4c0n6OBvO4GR1533bvvvvvIzc3l888/7zOtvPa66m/5yesvvI36Gp34+Hi0Wm2XGojKysouv2DaJCcnd5tep9MRFxc3ZHkNN4Mpu+4sXryYM2fOBDt7o4687oJrrF93999/P++99x47duxg3LhxvaaV115XAym/7oz16y+cjPpAx2AwsHDhQrZt29Zp/7Zt27jqqqu6PWbJkiVd0n/yySdcccUV6PX6IctruBlM2XUnJyeHlJSUYGdv1JHXXXCN1etOCMF9993H5s2b+eyzz5g0aVKfx8hrr91gyq87Y/X6C0sh6wY9jP7yl78IvV4vXnrpJXHy5Enx4IMPCrPZLIqKioQQQjzyyCPijjvu8KcvKCgQkZGR4l/+5V/EyZMnxUsvvST0er14++23Q/UWQmagZfef//mfYsuWLSI/P1+cOHFCPPLIIwIQ77zzTqjeQsg0NDSInJwckZOTIwDxzDPPiJycHHH+/HkhhLzuejPQspPXXbu7775b2Gw2sXPnTlFWVua/NTU1+dPIa69ngyk/ef2FtzER6AghxPPPPy8mTJggDAaDWLBgQaehghs2bBDXXHNNp/Q7d+4U8+fPFwaDQUycOFG8+OKLw5zj8DGQsnvqqafE5MmThclkEjExMeLqq68WH374YQhyHXptQ04vv23YsEEIIa+73gy07OR11667cgPEK6+84k8jr72eDab85PUX3hQhWnucSZIkSZIkjTKjvo+OJEmSJEljlwx0JEmSJEkatWSgI0mSJEnSqCUDHUmSJEmSRi0Z6EiSJEmSNGrJQEeSJEmSpFFLBjqSJEmSJI1aMtCRpDC0c+dOFEWhrq5uyF5jxYoVPPjgg0N2fkmSpHAgAx1JCpG9e/ei1Wq56aabQp2VfikqKkJRFI4ePRrwue68804URely61gWiqKwdevWgF9LkqSxTRfqDEjSWPXyyy9z//3387//+78UFxeTnp4e6iwNq5tuuolXXnml0z6j0Rii3EiSNFrJGh1JCgGHw8Gbb77J3Xffzde//nU2bdrUbbovvviCuXPnYjKZuPLKKzl+/Lj/ufPnz7N27VpiYmIwm83MnDmTjz76yP/8rl27WLRoEUajkZSUFB555BE8Hk+PeequBiU6Otqft7ZVnOfPn4+iKKxYscKf7pVXXmH69OmYTCamTZvGCy+80GcZGI1GkpOTO91iYmIAmDhxIgDr169HURT/9rlz57jllltISkrCYrGQnZ3Np59+2udrSZI0dslAR5JC4I033iArK4usrCy+/e1v88orr9DdsnMPP/wwGzdu5ODBgyQmJnLzzTfjdrsBuPfee3E6nezevZvjx4/z1FNPYbFYACgpKWHNmjVkZ2dz7NgxXnzxRV566SV++ctfDjrPBw4cAODTTz+lrKyMzZs3A/DHP/6RRx99lP/4j//g1KlT/OpXv+LnP/85r7766qBf6+DBg4AvgCorK/NvNzY2smbNGj799FNycnK48cYbWbt2LcXFxYN+LUmSRrkQLyoqSWPSVVddJZ599lkhhBBut1vEx8eLbdu2+Z9vW737L3/5i3/fpUuXREREhHjjjTeEEELMnj1bPP74492e/2c/+5nIysoSqqr69z3//PPCYrEIr9crhBDimmuuET/84Q/9zwNiy5Ytnc5js9n8qzYXFhYKQOTk5HRKM378ePHnP/+5075///d/F0uWLOnx/W/YsEFotVphNps73Z544ole89OdGTNmiP/+7//uM50kSWOT7KMjScMsLy+PAwcO+GtEdDodt99+Oy+//DLXXXddp7RLlizxP46NjSUrK4tTp04B8MADD3D33XfzySefcN1113HrrbcyZ84cAE6dOsWSJUtQFMV//NKlS2lsbOTixYtB6w9UVVXFhQsXuOuuu/j+97/v3+/xeLDZbL0eu3LlSl588cVO+2JjY3s9xuFw8Itf/IIPPviA0tJSPB4Pzc3NskZHkqQeyUBHkobZSy+9hMfjIS0tzb9PCIFer6e2ttbfT6UnbcHL9773PW688UY+/PBDPvnkE5588kmefvpp7r//foQQnYKcttfoeHx35xWXNZ+1NZP1RFVVwNd8deWVV3Z6TqvV9nqs2WwmMzOz1zSXe/jhh/n444/ZuHEjmZmZREREcNttt+FyuQZ0HkmSxg7ZR0eShpHH4+G1117j6aef5ujRo/7bsWPHmDBhAn/60586pd+3b5//cW1tLfn5+UybNs2/b/z48fzgBz9g8+bN/OhHP+KPf/wjADNmzGDv3r2dApe9e/cSFRXVKcDqKCEhgbKyMv/2mTNnaGpq8m8bDAYAvF6vf19SUhJpaWkUFBSQmZnZ6dbWeXmw9Hp9p9cC2LNnD3feeSfr169n9uzZJCcnU1RUFNDrSJI0uskaHUkaRh988AG1tbXcddddXZp2brvtNl566SXuu+8+/74nnniCuLg4kpKSePTRR4mPj2fdunUAPPjgg6xevZqpU6dSW1vLZ599xvTp0wG45557ePbZZ7n//vu57777yMvL47HHHuOhhx5Co+n+9821117Lc889x+LFi1FVlZ/85Cfo9Xr/84mJiURERPC3v/2NcePGYTKZsNlsPP744zzwwANYrVZWr16N0+nk0KFD1NbW8tBDD/VYFk6nk/Ly8k77dDod8fHxgG/k1fbt21m6dClGo5GYmBgyMzPZvHkza9euRVEUfv7zn/trlSRJkroV0h5CkjTGfP3rXxdr1qzp9rnDhw8LQBw+fNjfGfn9998XM2fOFAaDQWRnZ4ujR4/60993331i8uTJwmg0ioSEBHHHHXeI6upq//M7d+4U2dnZwmAwiOTkZPGTn/xEuN1u//OXd0YuKSkRN9xwgzCbzWLKlCnio48+6tQZWQgh/vjHP4rx48cLjUYjrrnmGv/+P/3pT2LevHnCYDCImJgYsXz5crF58+Yey2HDhg0C6HLLysryp3nvvfdEZmam0Ol0YsKECUIIX4folStXioiICDF+/Hjx3HPPdXkfkiRJHSlCdDOmVZIkSZIkaRSQfXQkSZIkSRq1ZKAjSZIkSdKoJQMdSZIkSZJGLRnoSJIkSZI0aslAR5IkSZKkUUsGOpIkSZIkjVoy0JEkSZIkadSSgY4kSZIkSaOWDHQkSZIkSRq1ZKAjSZIkSdKoJQMdSZIkSZJGLRnoSJIkSZI0av1/IBFHsHA+XQsAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -258,10 +302,10 @@ "output_type": "stream", "text": [ "pt: 5 to inf\n", - "96% Retention Cut: [0.2504 0.3244 0.3069 0.4128 0.2115 0.2105 0.2568 0.2435 0.3587 0.2614] Mean: 0.2837\n", - "99% Retention Cut: [0.0769 0.1182 0.0896 0.1174 0.1037 0.1147 0.1376 0.1326 0.1812 0.1348] Mean: 0.1207\n", - "99.9% Retention Cut: [0.0292 0.0301 0.0285 0.0406 0.0364 0.0289 0.0516 0.0295 0.0837 0.03 ] Mean: 0.0389\n", - "99.5% Retention Cut: [0.0574 0.0798 0.0549 0.0791 0.0847 0.0877 0.1053 0.0953 0.1319 0.1038] Mean: 0.088\n" + "96% Retention Cut: [0.2185 0.2754 0.2794 0.3838 0.2719 0.2004 0.2749 0.1378 0.2626 0.1656] Mean: 0.247\n", + "99% Retention Cut: [0.093 0.1094 0.114 0.1597 0.1151 0.0812 0.1198 0.0212 0.1269 0.0687] Mean: 0.1009\n", + "99.9% Retention Cut: [0.03 0.058 0.0218 0.0451 0.0505 0.0141 0.0451 0.011 0.0341 0.0231] Mean: 0.0333\n", + "99.5% Retention Cut: [0.0687 0.0887 0.0689 0.0981 0.0739 0.0568 0.0771 0.0123 0.0798 0.0449] Mean: 0.0669\n" ] } ], @@ -277,8 +321,8 @@ "# Function to calculate cut values and plot for a given pt bin\n", "def plot_for_pt_bin(pt_min, pt_max):\n", " # Filter data based on pt bin\n", - " abs_eta = np.abs(t5_eta[(t5_isFake == 0) & (t5_pt > pt_min) & (t5_pt <= pt_max)])\n", - " predictions_filtered = predictions[(t5_isFake == 0) & (t5_pt > pt_min) & (t5_pt <= pt_max)]\n", + " abs_eta = np.abs(np.concatenate(branches['t5_eta'])[(np.concatenate(branches['t5_isFake']) == 0) & (np.concatenate(branches['t5_pt']) > pt_min) & (np.concatenate(branches['t5_pt']) <= pt_max)])\n", + " predictions_filtered = predictions[(np.concatenate(branches['t5_isFake']) == 0) & (np.concatenate(branches['t5_pt']) > pt_min) & (np.concatenate(branches['t5_pt']) <= pt_max)]\n", " \n", " # Lists to store cut values for different percentiles\n", " cut_values_96 = []\n", diff --git a/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb index e5685024ef52f..b22b42b670655 100644 --- a/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb +++ b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_37414/2045102495.py:4: DeprecationWarning: \n", + "/tmp/ipykernel_921783/49064298.py:4: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", @@ -18,13 +18,6 @@ " \n", " import pandas as pd\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z max: 267.2349853515625, R max: 110.10993957519531\n" - ] } ], "source": [ @@ -34,81 +27,135 @@ "import pandas as pd\n", "import awkward as ak\n", "\n", - "# Input ROOT file\n", - "file_path = \"DNNTrain_1000_new.root\"\n", - "with uproot.open(file_path) as file:\n", - " tree = file[\"tree\"]\n", - "\n", - " t5_innerRadius = np.concatenate(tree[\"t5_innerRadius\"].array(library=\"np\"))\n", - " t5_bridgeRadius = np.concatenate(tree[\"t5_bridgeRadius\"].array(library=\"np\"))\n", - " t5_outerRadius = np.concatenate(tree[\"t5_outerRadius\"].array(library=\"np\"))\n", - " t5_pt = np.concatenate(tree[\"t5_pt\"].array(library=\"np\"))\n", - " t5_eta = np.concatenate(tree[\"t5_eta\"].array(library=\"np\"))\n", - " t5_phi = np.concatenate(tree[\"t5_phi\"].array(library=\"np\"))\n", - " t5_isFake = np.concatenate(tree[\"t5_isFake\"].array(library=\"np\"))\n", - "\n", - " # Use these indices to extract two from each below\n", - " t5_t3_idx0 = np.concatenate(tree[\"t5_t3_idx0\"].array(library=\"np\"))\n", - " t5_t3_idx1 = np.concatenate(tree[\"t5_t3_idx1\"].array(library=\"np\"))\n", - "\n", - " # Each of these is two features below this line, index with above.\n", - " t3_pt = np.concatenate(tree[\"t5_t3_pt\"].array(library=\"np\"))\n", - "\n", - " t3_0_r = np.concatenate(tree[\"t5_t3_0_r\"].array(library=\"np\"))\n", - " t3_0_z = np.concatenate(tree[\"t5_t3_0_z\"].array(library=\"np\"))\n", - " t3_0_eta = np.concatenate(tree[\"t5_t3_0_eta\"].array(library=\"np\"))\n", - " t3_0_phi = np.concatenate(tree[\"t5_t3_0_phi\"].array(library=\"np\"))\n", - " t3_0_layer = np.concatenate(tree[\"t5_t3_0_layer\"].array(library=\"np\"))\n", - "\n", - " t3_2_r = np.concatenate(tree[\"t5_t3_2_r\"].array(library=\"np\"))\n", - " t3_2_z = np.concatenate(tree[\"t5_t3_2_z\"].array(library=\"np\"))\n", - " t3_2_eta = np.concatenate(tree[\"t5_t3_2_eta\"].array(library=\"np\"))\n", - " t3_2_phi = np.concatenate(tree[\"t5_t3_2_phi\"].array(library=\"np\"))\n", - " t3_2_layer = np.concatenate(tree[\"t5_t3_2_layer\"].array(library=\"np\"))\n", - "\n", - " t3_4_r = np.concatenate(tree[\"t5_t3_4_r\"].array(library=\"np\"))\n", - " t3_4_z = np.concatenate(tree[\"t5_t3_4_z\"].array(library=\"np\"))\n", - " t3_4_eta = np.concatenate(tree[\"t5_t3_4_eta\"].array(library=\"np\"))\n", - " t3_4_phi = np.concatenate(tree[\"t5_t3_4_phi\"].array(library=\"np\"))\n", - " t3_4_layer = np.concatenate(tree[\"t5_t3_4_layer\"].array(library=\"np\"))\n", - "\n", - "# Max value to normalize the z, r variables by.\n", - "z_max = np.max([t3_4_z, t3_2_z, t3_0_z])\n", - "r_max = np.max([t3_4_r, t3_2_r, t3_0_r])\n", + "def load_root_file(file_path, branches=None, print_branches=False):\n", + " all_branches = {}\n", + " with uproot.open(file_path) as file:\n", + " tree = file[\"tree\"]\n", + " # Load all ROOT branches into array if not specified\n", + " if branches is None:\n", + " branches = tree.keys()\n", + " # Option to print the branch names\n", + " if print_branches:\n", + " print(\"Branches:\", tree.keys())\n", + " # Each branch is added to the dictionary\n", + " for branch in branches:\n", + " try:\n", + " all_branches[branch] = (tree[branch].array(library=\"np\"))\n", + " except uproot.KeyInFileError as e:\n", + " print(f\"KeyInFileError: {e}\")\n", + " # Number of events in file\n", + " all_branches['event'] = tree.num_entries\n", + " return all_branches\n", + "\n", + "branches_list = [\n", + " 't5_innerRadius',\n", + " 't5_bridgeRadius',\n", + " 't5_outerRadius',\n", + " 't5_pt',\n", + " 't5_eta',\n", + " 't5_phi',\n", + " 't5_isFake',\n", + " 't5_t3_idx0',\n", + " 't5_t3_idx1',\n", + "]\n", "\n", - "print(f'Z max: {z_max}, R max: {r_max}')" + "# Hit-dependent branches\n", + "suffixes = ['r', 'z', 'eta', 'phi', 'layer']\n", + "branches_list += [f't5_t3_{i}_{suffix}' for i in [0, 2, 4] for suffix in suffixes]\n", + "\n", + "file_path = \"train_1000_final.root\"\n", + "branches = load_root_file(file_path, branches_list)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z max: 267.2349853515625, R max: 110.10993957519531\n" + ] + } + ], + "source": [ + "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n", + "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n", + "\n", + "print(f'Z max: {z_max}, R max: {r_max}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "features_list = []\n", + "\n", + "for event in range(branches['event']):\n", + " # Determine the number of elements in this event\n", + " num_elements = len(branches['t5_t3_idx0'][event])\n", + "\n", + " for i in range(num_elements):\n", + " features_iter = []\n", + " \n", + " idx0 = branches['t5_t3_idx0'][event][i]\n", + " idx1 = branches['t5_t3_idx1'][event][i]\n", + "\n", + " # Collect features using idx0\n", + " features_iter.extend([\n", + " branches['t5_t3_0_eta'][event][idx0],\n", + " branches['t5_t3_0_z'][event][idx0] / z_max,\n", + " branches['t5_t3_0_r'][event][idx0] / r_max,\n", + " branches['t5_t3_0_layer'][event][idx0],\n", + " branches['t5_t3_2_eta'][event][idx0],\n", + " branches['t5_t3_2_z'][event][idx0] / z_max,\n", + " branches['t5_t3_2_r'][event][idx0] / r_max,\n", + " branches['t5_t3_2_layer'][event][idx0],\n", + " branches['t5_t3_4_eta'][event][idx0],\n", + " branches['t5_t3_4_z'][event][idx0] / z_max,\n", + " branches['t5_t3_4_r'][event][idx0] / r_max,\n", + " branches['t5_t3_4_layer'][event][idx0],\n", + " ])\n", + " \n", + " # Collect features using idx1\n", + " features_iter.extend([\n", + " branches['t5_t3_2_eta'][event][idx1],\n", + " branches['t5_t3_2_z'][event][idx1] / z_max,\n", + " branches['t5_t3_2_r'][event][idx1] / r_max,\n", + " branches['t5_t3_2_layer'][event][idx1],\n", + " branches['t5_t3_4_eta'][event][idx1],\n", + " branches['t5_t3_4_z'][event][idx1] / z_max,\n", + " branches['t5_t3_4_r'][event][idx1] / r_max,\n", + " branches['t5_t3_4_layer'][event][idx1],\n", + " ])\n", + " \n", + " # Add remaining features\n", + " features_iter.extend([\n", + " branches['t5_eta'][event][i],\n", + " np.log10(branches['t5_innerRadius'][event][i]),\n", + " np.log10(branches['t5_bridgeRadius'][event][i]),\n", + " np.log10(branches['t5_outerRadius'][event][i]),\n", + " ])\n", + " \n", + " # Append the feature vector to the list\n", + " features_list.append(features_iter)\n", + "\n", + "# Convert the list of features to a NumPy array\n", + "features = np.array(features_list).T" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", - "# Function to fetch T3 features for given T5 indices\n", - "def fetch_t3_features_for_t5(t3_feature, t5_t3_idx):\n", - " return t3_feature[t5_t3_idx]\n", - "\n", - "# Initialize the features list without the T5 standalone variables at the beginning\n", - "features = []\n", - "\n", - "# Now, extract T3 features indexed by t5_t3_idx0 and t5_t3_idx1\n", - "for t3_feature in [t3_0_eta, t3_0_z/z_max, t3_0_r/r_max, t3_0_layer,\n", - " t3_2_eta, t3_2_z/z_max, t3_2_r/r_max, t3_2_layer,\n", - " t3_4_eta, t3_4_z/z_max, t3_4_r/r_max, t3_4_layer]:\n", - " features.append(fetch_t3_features_for_t5(t3_feature, t5_t3_idx0))\n", - "\n", - "for t3_feature in [t3_2_eta, t3_2_z/z_max, t3_2_r/r_max, t3_2_layer,\n", - " t3_4_eta, t3_4_z/z_max, t3_4_r/r_max, t3_4_layer]:\n", - " features.append(fetch_t3_features_for_t5(t3_feature, t5_t3_idx1))\n", - "\n", - "# Append T5 standalone variables at the end of the features list\n", - "features += [\n", - " t5_eta, np.log10(t5_innerRadius), np.log10(t5_bridgeRadius), np.log10(t5_outerRadius)\n", - "]\n", - "\n", "# Stack features along a new axis to form a single array suitable for NN input\n", "input_features_numpy = np.stack(features, axis=-1)\n", "\n", @@ -117,7 +164,7 @@ "\n", "# Apply mask across all columns: retain a row only if all its entries are neither NaN nor Inf\n", "filtered_input_features_numpy = input_features_numpy[np.all(mask, axis=1)]\n", - "t5_isFake_filtered = t5_isFake[np.all(mask, axis=1)]\n", + "t5_isFake_filtered = np.concatenate(branches['t5_isFake'])[np.all(mask, axis=1)]\n", "\n", "# Convert to PyTorch tensor when ready to use with NN\n", "input_features_tensor = torch.tensor(filtered_input_features_numpy, dtype=torch.float32)" @@ -125,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -133,157 +180,157 @@ "output_type": "stream", "text": [ "Using device: cuda\n", - "torch.Size([1959512]) torch.Size([7315940])\n", - "Epoch [1/150], Loss: 0.5629, Train Acc: 74.14%, Test Acc: 74.12%\n", - "Epoch [2/150], Loss: 0.5327, Train Acc: 76.23%, Test Acc: 76.20%\n", - "Epoch [3/150], Loss: 0.4985, Train Acc: 76.66%, Test Acc: 76.62%\n", - "Epoch [4/150], Loss: 0.4622, Train Acc: 77.87%, Test Acc: 77.84%\n", - "Epoch [5/150], Loss: 0.4269, Train Acc: 78.69%, Test Acc: 78.68%\n", - "Epoch [6/150], Loss: 0.4379, Train Acc: 78.45%, Test Acc: 78.45%\n", - "Epoch [7/150], Loss: 0.4771, Train Acc: 79.03%, Test Acc: 79.04%\n", - "Epoch [8/150], Loss: 0.4728, Train Acc: 77.10%, Test Acc: 77.13%\n", - "Epoch [9/150], Loss: 0.4479, Train Acc: 78.95%, Test Acc: 78.93%\n", - "Epoch [10/150], Loss: 0.4348, Train Acc: 79.38%, Test Acc: 79.39%\n", - "Epoch [11/150], Loss: 0.4917, Train Acc: 79.37%, Test Acc: 79.37%\n", - "Epoch [12/150], Loss: 0.4467, Train Acc: 79.62%, Test Acc: 79.65%\n", - "Epoch [13/150], Loss: 0.4475, Train Acc: 79.44%, Test Acc: 79.39%\n", - "Epoch [14/150], Loss: 0.4537, Train Acc: 79.52%, Test Acc: 79.52%\n", - "Epoch [15/150], Loss: 0.4418, Train Acc: 79.72%, Test Acc: 79.71%\n", - "Epoch [16/150], Loss: 0.4663, Train Acc: 79.40%, Test Acc: 79.38%\n", - "Epoch [17/150], Loss: 0.4837, Train Acc: 79.24%, Test Acc: 79.21%\n", - "Epoch [18/150], Loss: 0.4417, Train Acc: 79.59%, Test Acc: 79.59%\n", - "Epoch [19/150], Loss: 0.4422, Train Acc: 79.76%, Test Acc: 79.73%\n", - "Epoch [20/150], Loss: 0.4880, Train Acc: 79.63%, Test Acc: 79.61%\n", - "Epoch [21/150], Loss: 0.4394, Train Acc: 79.35%, Test Acc: 79.35%\n", - "Epoch [22/150], Loss: 0.4530, Train Acc: 79.53%, Test Acc: 79.54%\n", - "Epoch [23/150], Loss: 0.4718, Train Acc: 79.60%, Test Acc: 79.57%\n", - "Epoch [24/150], Loss: 0.4439, Train Acc: 79.79%, Test Acc: 79.80%\n", - "Epoch [25/150], Loss: 0.4601, Train Acc: 79.84%, Test Acc: 79.84%\n", - "Epoch [26/150], Loss: 0.4428, Train Acc: 79.84%, Test Acc: 79.81%\n", - "Epoch [27/150], Loss: 0.4346, Train Acc: 79.83%, Test Acc: 79.80%\n", - "Epoch [28/150], Loss: 0.4199, Train Acc: 79.73%, Test Acc: 79.70%\n", - "Epoch [29/150], Loss: 0.4313, Train Acc: 79.86%, Test Acc: 79.81%\n", - "Epoch [30/150], Loss: 0.4849, Train Acc: 79.78%, Test Acc: 79.79%\n", - "Epoch [31/150], Loss: 0.4764, Train Acc: 79.76%, Test Acc: 79.76%\n", - "Epoch [32/150], Loss: 0.4273, Train Acc: 79.49%, Test Acc: 79.50%\n", - "Epoch [33/150], Loss: 0.4771, Train Acc: 79.84%, Test Acc: 79.84%\n", - "Epoch [34/150], Loss: 0.4598, Train Acc: 79.92%, Test Acc: 79.90%\n", - "Epoch [35/150], Loss: 0.4755, Train Acc: 79.63%, Test Acc: 79.60%\n", - "Epoch [36/150], Loss: 0.4170, Train Acc: 79.79%, Test Acc: 79.78%\n", - "Epoch [37/150], Loss: 0.4175, Train Acc: 79.68%, Test Acc: 79.67%\n", - "Epoch [38/150], Loss: 0.4309, Train Acc: 79.83%, Test Acc: 79.79%\n", - "Epoch [39/150], Loss: 0.4480, Train Acc: 79.89%, Test Acc: 79.85%\n", - "Epoch [40/150], Loss: 0.4900, Train Acc: 79.75%, Test Acc: 79.70%\n", - "Epoch [41/150], Loss: 0.4148, Train Acc: 79.85%, Test Acc: 79.85%\n", - "Epoch [42/150], Loss: 0.4126, Train Acc: 79.71%, Test Acc: 79.70%\n", - "Epoch [43/150], Loss: 0.4508, Train Acc: 79.90%, Test Acc: 79.86%\n", - "Epoch [44/150], Loss: 0.4387, Train Acc: 79.38%, Test Acc: 79.34%\n", - "Epoch [45/150], Loss: 0.4512, Train Acc: 79.78%, Test Acc: 79.73%\n", - "Epoch [46/150], Loss: 0.4622, Train Acc: 79.53%, Test Acc: 79.52%\n", - "Epoch [47/150], Loss: 0.4509, Train Acc: 79.69%, Test Acc: 79.65%\n", - "Epoch [48/150], Loss: 0.4353, Train Acc: 79.55%, Test Acc: 79.54%\n", - "Epoch [49/150], Loss: 0.4296, Train Acc: 79.88%, Test Acc: 79.83%\n", - "Epoch [50/150], Loss: 0.4403, Train Acc: 80.00%, Test Acc: 79.95%\n", - "Epoch [51/150], Loss: 0.4292, Train Acc: 79.85%, Test Acc: 79.82%\n", - "Epoch [52/150], Loss: 0.4454, Train Acc: 79.43%, Test Acc: 79.40%\n", - "Epoch [53/150], Loss: 0.5096, Train Acc: 79.98%, Test Acc: 79.93%\n", - "Epoch [54/150], Loss: 0.4339, Train Acc: 79.89%, Test Acc: 79.86%\n", - "Epoch [55/150], Loss: 0.4513, Train Acc: 80.08%, Test Acc: 80.04%\n", - "Epoch [56/150], Loss: 0.4127, Train Acc: 79.96%, Test Acc: 79.91%\n", - "Epoch [57/150], Loss: 0.4463, Train Acc: 80.00%, Test Acc: 79.98%\n", - "Epoch [58/150], Loss: 0.4491, Train Acc: 79.79%, Test Acc: 79.75%\n", - "Epoch [59/150], Loss: 0.4182, Train Acc: 79.96%, Test Acc: 79.90%\n", - "Epoch [60/150], Loss: 0.4395, Train Acc: 79.78%, Test Acc: 79.79%\n", - "Epoch [61/150], Loss: 0.4419, Train Acc: 79.77%, Test Acc: 79.76%\n", - "Epoch [62/150], Loss: 0.4549, Train Acc: 79.60%, Test Acc: 79.53%\n", - "Epoch [63/150], Loss: 0.4200, Train Acc: 79.69%, Test Acc: 79.67%\n", - "Epoch [64/150], Loss: 0.4234, Train Acc: 79.70%, Test Acc: 79.68%\n", - "Epoch [65/150], Loss: 0.4693, Train Acc: 79.98%, Test Acc: 79.93%\n", - "Epoch [66/150], Loss: 0.3985, Train Acc: 79.96%, Test Acc: 79.93%\n", - "Epoch [67/150], Loss: 0.4433, Train Acc: 79.87%, Test Acc: 79.85%\n", - "Epoch [68/150], Loss: 0.4136, Train Acc: 79.71%, Test Acc: 79.70%\n", - "Epoch [69/150], Loss: 0.4662, Train Acc: 79.78%, Test Acc: 79.78%\n", - "Epoch [70/150], Loss: 0.4418, Train Acc: 79.93%, Test Acc: 79.88%\n", - "Epoch [71/150], Loss: 0.4032, Train Acc: 79.83%, Test Acc: 79.78%\n", - "Epoch [72/150], Loss: 0.4266, Train Acc: 79.90%, Test Acc: 79.89%\n", - "Epoch [73/150], Loss: 0.4501, Train Acc: 80.04%, Test Acc: 80.02%\n", - "Epoch [74/150], Loss: 0.4363, Train Acc: 79.90%, Test Acc: 79.89%\n", - "Epoch [75/150], Loss: 0.4299, Train Acc: 80.01%, Test Acc: 79.93%\n", - "Epoch [76/150], Loss: 0.4423, Train Acc: 79.87%, Test Acc: 79.84%\n", - "Epoch [77/150], Loss: 0.4110, Train Acc: 78.84%, Test Acc: 78.85%\n", - "Epoch [78/150], Loss: 0.4296, Train Acc: 80.01%, Test Acc: 79.96%\n", - "Epoch [79/150], Loss: 0.4067, Train Acc: 79.87%, Test Acc: 79.84%\n", - "Epoch [80/150], Loss: 0.4361, Train Acc: 79.83%, Test Acc: 79.80%\n", - "Epoch [81/150], Loss: 0.4109, Train Acc: 79.98%, Test Acc: 79.97%\n", - "Epoch [82/150], Loss: 0.4434, Train Acc: 80.02%, Test Acc: 80.00%\n", - "Epoch [83/150], Loss: 0.4288, Train Acc: 80.16%, Test Acc: 80.12%\n", - "Epoch [84/150], Loss: 0.4423, Train Acc: 79.65%, Test Acc: 79.64%\n", - "Epoch [85/150], Loss: 0.4393, Train Acc: 80.13%, Test Acc: 80.10%\n", - "Epoch [86/150], Loss: 0.4291, Train Acc: 79.75%, Test Acc: 79.75%\n", - "Epoch [87/150], Loss: 0.4382, Train Acc: 80.11%, Test Acc: 80.09%\n", - "Epoch [88/150], Loss: 0.4284, Train Acc: 79.37%, Test Acc: 79.33%\n", - "Epoch [89/150], Loss: 0.4438, Train Acc: 79.95%, Test Acc: 79.93%\n", - "Epoch [90/150], Loss: 0.4001, Train Acc: 80.08%, Test Acc: 80.04%\n", - "Epoch [91/150], Loss: 0.3887, Train Acc: 80.09%, Test Acc: 80.05%\n", - "Epoch [92/150], Loss: 0.4497, Train Acc: 80.08%, Test Acc: 80.02%\n", - "Epoch [93/150], Loss: 0.4554, Train Acc: 80.16%, Test Acc: 80.10%\n", - "Epoch [94/150], Loss: 0.4227, Train Acc: 79.78%, Test Acc: 79.78%\n", - "Epoch [95/150], Loss: 0.4252, Train Acc: 79.77%, Test Acc: 79.71%\n", - "Epoch [96/150], Loss: 0.4314, Train Acc: 79.95%, Test Acc: 79.92%\n", - "Epoch [97/150], Loss: 0.4704, Train Acc: 80.06%, Test Acc: 80.03%\n", - "Epoch [98/150], Loss: 0.4570, Train Acc: 79.91%, Test Acc: 79.89%\n", - "Epoch [99/150], Loss: 0.4322, Train Acc: 80.01%, Test Acc: 79.94%\n", - "Epoch [100/150], Loss: 0.4402, Train Acc: 80.11%, Test Acc: 80.06%\n", - "Epoch [101/150], Loss: 0.4323, Train Acc: 79.95%, Test Acc: 79.93%\n", - "Epoch [102/150], Loss: 0.4633, Train Acc: 80.07%, Test Acc: 80.08%\n", - "Epoch [103/150], Loss: 0.4351, Train Acc: 79.91%, Test Acc: 79.89%\n", - "Epoch [104/150], Loss: 0.4356, Train Acc: 80.16%, Test Acc: 80.12%\n", - "Epoch [105/150], Loss: 0.3963, Train Acc: 80.00%, Test Acc: 79.97%\n", - "Epoch [106/150], Loss: 0.4249, Train Acc: 79.78%, Test Acc: 79.75%\n", - "Epoch [107/150], Loss: 0.4262, Train Acc: 79.52%, Test Acc: 79.45%\n", - "Epoch [108/150], Loss: 0.4335, Train Acc: 79.97%, Test Acc: 79.92%\n", - "Epoch [109/150], Loss: 0.4616, Train Acc: 79.46%, Test Acc: 79.39%\n", - "Epoch [110/150], Loss: 0.4389, Train Acc: 79.98%, Test Acc: 79.93%\n", - "Epoch [111/150], Loss: 0.4462, Train Acc: 80.08%, Test Acc: 80.05%\n", - "Epoch [112/150], Loss: 0.4330, Train Acc: 80.11%, Test Acc: 80.04%\n", - "Epoch [113/150], Loss: 0.4621, Train Acc: 79.87%, Test Acc: 79.82%\n", - "Epoch [114/150], Loss: 0.4681, Train Acc: 80.01%, Test Acc: 79.99%\n", - "Epoch [115/150], Loss: 0.4153, Train Acc: 80.08%, Test Acc: 80.01%\n", - "Epoch [116/150], Loss: 0.4728, Train Acc: 80.24%, Test Acc: 80.17%\n", - "Epoch [117/150], Loss: 0.4334, Train Acc: 80.05%, Test Acc: 80.01%\n", - "Epoch [118/150], Loss: 0.4080, Train Acc: 80.16%, Test Acc: 80.09%\n", - "Epoch [119/150], Loss: 0.4669, Train Acc: 80.14%, Test Acc: 80.10%\n", - "Epoch [120/150], Loss: 0.4161, Train Acc: 80.03%, Test Acc: 79.98%\n", - "Epoch [121/150], Loss: 0.4575, Train Acc: 80.14%, Test Acc: 80.06%\n", - "Epoch [122/150], Loss: 0.3928, Train Acc: 80.15%, Test Acc: 80.07%\n", - "Epoch [123/150], Loss: 0.4109, Train Acc: 79.97%, Test Acc: 79.91%\n", - "Epoch [124/150], Loss: 0.4583, Train Acc: 79.78%, Test Acc: 79.81%\n", - "Epoch [125/150], Loss: 0.4379, Train Acc: 80.09%, Test Acc: 80.06%\n", - "Epoch [126/150], Loss: 0.4665, Train Acc: 79.86%, Test Acc: 79.86%\n", - "Epoch [127/150], Loss: 0.4418, Train Acc: 80.12%, Test Acc: 80.08%\n", - "Epoch [128/150], Loss: 0.4359, Train Acc: 79.95%, Test Acc: 79.88%\n", - "Epoch [129/150], Loss: 0.4201, Train Acc: 80.05%, Test Acc: 80.01%\n", - "Epoch [130/150], Loss: 0.4550, Train Acc: 80.18%, Test Acc: 80.15%\n", - "Epoch [131/150], Loss: 0.4213, Train Acc: 79.90%, Test Acc: 79.87%\n", - "Epoch [132/150], Loss: 0.4553, Train Acc: 79.85%, Test Acc: 79.82%\n", - "Epoch [133/150], Loss: 0.4203, Train Acc: 80.08%, Test Acc: 80.03%\n", - "Epoch [134/150], Loss: 0.4572, Train Acc: 79.64%, Test Acc: 79.64%\n", - "Epoch [135/150], Loss: 0.4406, Train Acc: 80.24%, Test Acc: 80.17%\n", - "Epoch [136/150], Loss: 0.4032, Train Acc: 79.74%, Test Acc: 79.72%\n", - "Epoch [137/150], Loss: 0.4098, Train Acc: 80.11%, Test Acc: 80.06%\n", - "Epoch [138/150], Loss: 0.4615, Train Acc: 79.80%, Test Acc: 79.74%\n", - "Epoch [139/150], Loss: 0.4109, Train Acc: 80.15%, Test Acc: 80.11%\n", - "Epoch [140/150], Loss: 0.4429, Train Acc: 79.92%, Test Acc: 79.86%\n", - "Epoch [141/150], Loss: 0.4452, Train Acc: 79.98%, Test Acc: 79.96%\n", - "Epoch [142/150], Loss: 0.4413, Train Acc: 80.19%, Test Acc: 80.13%\n", - "Epoch [143/150], Loss: 0.4190, Train Acc: 80.11%, Test Acc: 80.06%\n", - "Epoch [144/150], Loss: 0.3769, Train Acc: 80.14%, Test Acc: 80.10%\n", - "Epoch [145/150], Loss: 0.4151, Train Acc: 80.18%, Test Acc: 80.12%\n", - "Epoch [146/150], Loss: 0.4284, Train Acc: 80.01%, Test Acc: 79.99%\n", - "Epoch [147/150], Loss: 0.4394, Train Acc: 80.21%, Test Acc: 80.15%\n", - "Epoch [148/150], Loss: 0.4043, Train Acc: 79.92%, Test Acc: 79.87%\n", - "Epoch [149/150], Loss: 0.3959, Train Acc: 80.27%, Test Acc: 80.22%\n", - "Epoch [150/150], Loss: 0.4481, Train Acc: 80.21%, Test Acc: 80.16%\n" + "torch.Size([1959386]) torch.Size([7315929])\n", + "Epoch [1/150], Loss: 0.5115, Train Acc: 74.98%, Test Acc: 74.94%\n", + "Epoch [2/150], Loss: 0.5177, Train Acc: 76.74%, Test Acc: 76.78%\n", + "Epoch [3/150], Loss: 0.4744, Train Acc: 76.94%, Test Acc: 77.01%\n", + "Epoch [4/150], Loss: 0.4641, Train Acc: 78.26%, Test Acc: 78.24%\n", + "Epoch [5/150], Loss: 0.4863, Train Acc: 79.80%, Test Acc: 79.81%\n", + "Epoch [6/150], Loss: 0.4271, Train Acc: 79.45%, Test Acc: 79.48%\n", + "Epoch [7/150], Loss: 0.4607, Train Acc: 79.81%, Test Acc: 79.77%\n", + "Epoch [8/150], Loss: 0.4450, Train Acc: 80.26%, Test Acc: 80.27%\n", + "Epoch [9/150], Loss: 0.4461, Train Acc: 80.04%, Test Acc: 80.05%\n", + "Epoch [10/150], Loss: 0.4812, Train Acc: 80.07%, Test Acc: 80.10%\n", + "Epoch [11/150], Loss: 0.4179, Train Acc: 80.44%, Test Acc: 80.45%\n", + "Epoch [12/150], Loss: 0.4324, Train Acc: 80.54%, Test Acc: 80.54%\n", + "Epoch [13/150], Loss: 0.3995, Train Acc: 80.26%, Test Acc: 80.25%\n", + "Epoch [14/150], Loss: 0.4148, Train Acc: 80.86%, Test Acc: 80.89%\n", + "Epoch [15/150], Loss: 0.4148, Train Acc: 80.64%, Test Acc: 80.67%\n", + "Epoch [16/150], Loss: 0.4399, Train Acc: 80.48%, Test Acc: 80.49%\n", + "Epoch [17/150], Loss: 0.4080, Train Acc: 80.79%, Test Acc: 80.81%\n", + "Epoch [18/150], Loss: 0.4104, Train Acc: 80.17%, Test Acc: 80.16%\n", + "Epoch [19/150], Loss: 0.4381, Train Acc: 79.65%, Test Acc: 79.58%\n", + "Epoch [20/150], Loss: 0.4599, Train Acc: 80.84%, Test Acc: 80.84%\n", + "Epoch [21/150], Loss: 0.4455, Train Acc: 80.44%, Test Acc: 80.46%\n", + "Epoch [22/150], Loss: 0.4085, Train Acc: 79.95%, Test Acc: 79.95%\n", + "Epoch [23/150], Loss: 0.4848, Train Acc: 80.67%, Test Acc: 80.66%\n", + "Epoch [24/150], Loss: 0.4093, Train Acc: 80.85%, Test Acc: 80.82%\n", + "Epoch [25/150], Loss: 0.3990, Train Acc: 80.41%, Test Acc: 80.42%\n", + "Epoch [26/150], Loss: 0.4257, Train Acc: 80.93%, Test Acc: 80.92%\n", + "Epoch [27/150], Loss: 0.4595, Train Acc: 81.04%, Test Acc: 81.02%\n", + "Epoch [28/150], Loss: 0.3922, Train Acc: 81.08%, Test Acc: 81.07%\n", + "Epoch [29/150], Loss: 0.4134, Train Acc: 80.78%, Test Acc: 80.82%\n", + "Epoch [30/150], Loss: 0.4502, Train Acc: 79.55%, Test Acc: 79.55%\n", + "Epoch [31/150], Loss: 0.4346, Train Acc: 80.99%, Test Acc: 81.00%\n", + "Epoch [32/150], Loss: 0.4383, Train Acc: 80.97%, Test Acc: 80.94%\n", + "Epoch [33/150], Loss: 0.3846, Train Acc: 81.23%, Test Acc: 81.26%\n", + "Epoch [34/150], Loss: 0.4196, Train Acc: 80.80%, Test Acc: 80.81%\n", + "Epoch [35/150], Loss: 0.4406, Train Acc: 80.48%, Test Acc: 80.47%\n", + "Epoch [36/150], Loss: 0.4385, Train Acc: 80.92%, Test Acc: 80.96%\n", + "Epoch [37/150], Loss: 0.4520, Train Acc: 80.85%, Test Acc: 80.84%\n", + "Epoch [38/150], Loss: 0.3885, Train Acc: 81.26%, Test Acc: 81.19%\n", + "Epoch [39/150], Loss: 0.3782, Train Acc: 81.27%, Test Acc: 81.32%\n", + "Epoch [40/150], Loss: 0.4312, Train Acc: 80.76%, Test Acc: 80.75%\n", + "Epoch [41/150], Loss: 0.4351, Train Acc: 80.97%, Test Acc: 80.98%\n", + "Epoch [42/150], Loss: 0.4140, Train Acc: 81.11%, Test Acc: 81.06%\n", + "Epoch [43/150], Loss: 0.4191, Train Acc: 81.28%, Test Acc: 81.25%\n", + "Epoch [44/150], Loss: 0.3999, Train Acc: 81.19%, Test Acc: 81.17%\n", + "Epoch [45/150], Loss: 0.4078, Train Acc: 81.16%, Test Acc: 81.17%\n", + "Epoch [46/150], Loss: 0.4647, Train Acc: 81.14%, Test Acc: 81.17%\n", + "Epoch [47/150], Loss: 0.4304, Train Acc: 81.04%, Test Acc: 81.00%\n", + "Epoch [48/150], Loss: 0.4298, Train Acc: 81.25%, Test Acc: 81.25%\n", + "Epoch [49/150], Loss: 0.4327, Train Acc: 81.18%, Test Acc: 81.17%\n", + "Epoch [50/150], Loss: 0.4095, Train Acc: 81.04%, Test Acc: 81.07%\n", + "Epoch [51/150], Loss: 0.4093, Train Acc: 81.32%, Test Acc: 81.30%\n", + "Epoch [52/150], Loss: 0.4062, Train Acc: 80.96%, Test Acc: 80.92%\n", + "Epoch [53/150], Loss: 0.4460, Train Acc: 81.13%, Test Acc: 81.11%\n", + "Epoch [54/150], Loss: 0.4404, Train Acc: 81.16%, Test Acc: 81.15%\n", + "Epoch [55/150], Loss: 0.3972, Train Acc: 81.17%, Test Acc: 81.17%\n", + "Epoch [56/150], Loss: 0.3996, Train Acc: 81.36%, Test Acc: 81.40%\n", + "Epoch [57/150], Loss: 0.3874, Train Acc: 81.06%, Test Acc: 81.03%\n", + "Epoch [58/150], Loss: 0.4535, Train Acc: 81.27%, Test Acc: 81.25%\n", + "Epoch [59/150], Loss: 0.4000, Train Acc: 81.31%, Test Acc: 81.28%\n", + "Epoch [60/150], Loss: 0.3947, Train Acc: 81.23%, Test Acc: 81.22%\n", + "Epoch [61/150], Loss: 0.3979, Train Acc: 81.38%, Test Acc: 81.36%\n", + "Epoch [62/150], Loss: 0.4352, Train Acc: 80.94%, Test Acc: 80.94%\n", + "Epoch [63/150], Loss: 0.4103, Train Acc: 81.12%, Test Acc: 81.13%\n", + "Epoch [64/150], Loss: 0.4164, Train Acc: 81.56%, Test Acc: 81.52%\n", + "Epoch [65/150], Loss: 0.4395, Train Acc: 81.46%, Test Acc: 81.41%\n", + "Epoch [66/150], Loss: 0.3913, Train Acc: 81.41%, Test Acc: 81.37%\n", + "Epoch [67/150], Loss: 0.4198, Train Acc: 81.08%, Test Acc: 81.09%\n", + "Epoch [68/150], Loss: 0.4233, Train Acc: 81.33%, Test Acc: 81.33%\n", + "Epoch [69/150], Loss: 0.4067, Train Acc: 81.23%, Test Acc: 81.21%\n", + "Epoch [70/150], Loss: 0.4310, Train Acc: 81.42%, Test Acc: 81.38%\n", + "Epoch [71/150], Loss: 0.4070, Train Acc: 81.62%, Test Acc: 81.60%\n", + "Epoch [72/150], Loss: 0.3374, Train Acc: 81.53%, Test Acc: 81.51%\n", + "Epoch [73/150], Loss: 0.3700, Train Acc: 81.26%, Test Acc: 81.19%\n", + "Epoch [74/150], Loss: 0.4108, Train Acc: 81.30%, Test Acc: 81.27%\n", + "Epoch [75/150], Loss: 0.4065, Train Acc: 81.37%, Test Acc: 81.35%\n", + "Epoch [76/150], Loss: 0.4566, Train Acc: 81.25%, Test Acc: 81.22%\n", + "Epoch [77/150], Loss: 0.3794, Train Acc: 81.43%, Test Acc: 81.37%\n", + "Epoch [78/150], Loss: 0.3935, Train Acc: 81.30%, Test Acc: 81.32%\n", + "Epoch [79/150], Loss: 0.4369, Train Acc: 81.38%, Test Acc: 81.33%\n", + "Epoch [80/150], Loss: 0.4056, Train Acc: 81.68%, Test Acc: 81.70%\n", + "Epoch [81/150], Loss: 0.4121, Train Acc: 81.16%, Test Acc: 81.12%\n", + "Epoch [82/150], Loss: 0.3845, Train Acc: 81.21%, Test Acc: 81.11%\n", + "Epoch [83/150], Loss: 0.4175, Train Acc: 81.67%, Test Acc: 81.64%\n", + "Epoch [84/150], Loss: 0.4160, Train Acc: 81.59%, Test Acc: 81.55%\n", + "Epoch [85/150], Loss: 0.4226, Train Acc: 81.08%, Test Acc: 81.05%\n", + "Epoch [86/150], Loss: 0.4430, Train Acc: 81.48%, Test Acc: 81.44%\n", + "Epoch [87/150], Loss: 0.4124, Train Acc: 81.31%, Test Acc: 81.27%\n", + "Epoch [88/150], Loss: 0.4199, Train Acc: 81.55%, Test Acc: 81.51%\n", + "Epoch [89/150], Loss: 0.4129, Train Acc: 81.71%, Test Acc: 81.67%\n", + "Epoch [90/150], Loss: 0.3956, Train Acc: 81.58%, Test Acc: 81.58%\n", + "Epoch [91/150], Loss: 0.4176, Train Acc: 81.53%, Test Acc: 81.51%\n", + "Epoch [92/150], Loss: 0.4325, Train Acc: 81.13%, Test Acc: 81.12%\n", + "Epoch [93/150], Loss: 0.4099, Train Acc: 81.53%, Test Acc: 81.51%\n", + "Epoch [94/150], Loss: 0.3786, Train Acc: 81.45%, Test Acc: 81.41%\n", + "Epoch [95/150], Loss: 0.3990, Train Acc: 81.50%, Test Acc: 81.46%\n", + "Epoch [96/150], Loss: 0.3947, Train Acc: 81.82%, Test Acc: 81.80%\n", + "Epoch [97/150], Loss: 0.4560, Train Acc: 81.66%, Test Acc: 81.65%\n", + "Epoch [98/150], Loss: 0.4143, Train Acc: 81.52%, Test Acc: 81.49%\n", + "Epoch [99/150], Loss: 0.3913, Train Acc: 81.49%, Test Acc: 81.45%\n", + "Epoch [100/150], Loss: 0.4339, Train Acc: 81.62%, Test Acc: 81.60%\n", + "Epoch [101/150], Loss: 0.3643, Train Acc: 81.69%, Test Acc: 81.67%\n", + "Epoch [102/150], Loss: 0.4149, Train Acc: 81.35%, Test Acc: 81.34%\n", + "Epoch [103/150], Loss: 0.3937, Train Acc: 81.80%, Test Acc: 81.75%\n", + "Epoch [104/150], Loss: 0.3828, Train Acc: 81.82%, Test Acc: 81.79%\n", + "Epoch [105/150], Loss: 0.4123, Train Acc: 81.40%, Test Acc: 81.36%\n", + "Epoch [106/150], Loss: 0.4303, Train Acc: 81.57%, Test Acc: 81.58%\n", + "Epoch [107/150], Loss: 0.3652, Train Acc: 81.72%, Test Acc: 81.68%\n", + "Epoch [108/150], Loss: 0.3674, Train Acc: 81.70%, Test Acc: 81.68%\n", + "Epoch [109/150], Loss: 0.3979, Train Acc: 81.79%, Test Acc: 81.77%\n", + "Epoch [110/150], Loss: 0.4147, Train Acc: 81.62%, Test Acc: 81.61%\n", + "Epoch [111/150], Loss: 0.3787, Train Acc: 81.60%, Test Acc: 81.58%\n", + "Epoch [112/150], Loss: 0.3848, Train Acc: 81.71%, Test Acc: 81.66%\n", + "Epoch [113/150], Loss: 0.3659, Train Acc: 81.42%, Test Acc: 81.39%\n", + "Epoch [114/150], Loss: 0.4076, Train Acc: 81.13%, Test Acc: 81.08%\n", + "Epoch [115/150], Loss: 0.3686, Train Acc: 81.85%, Test Acc: 81.83%\n", + "Epoch [116/150], Loss: 0.4147, Train Acc: 81.95%, Test Acc: 81.94%\n", + "Epoch [117/150], Loss: 0.4403, Train Acc: 81.38%, Test Acc: 81.34%\n", + "Epoch [118/150], Loss: 0.3832, Train Acc: 81.85%, Test Acc: 81.82%\n", + "Epoch [119/150], Loss: 0.3964, Train Acc: 81.76%, Test Acc: 81.73%\n", + "Epoch [120/150], Loss: 0.3853, Train Acc: 81.72%, Test Acc: 81.68%\n", + "Epoch [121/150], Loss: 0.4087, Train Acc: 81.64%, Test Acc: 81.62%\n", + "Epoch [122/150], Loss: 0.4079, Train Acc: 81.66%, Test Acc: 81.61%\n", + "Epoch [123/150], Loss: 0.4323, Train Acc: 82.03%, Test Acc: 82.01%\n", + "Epoch [124/150], Loss: 0.4344, Train Acc: 81.34%, Test Acc: 81.31%\n", + "Epoch [125/150], Loss: 0.4143, Train Acc: 81.93%, Test Acc: 81.91%\n", + "Epoch [126/150], Loss: 0.4122, Train Acc: 81.86%, Test Acc: 81.86%\n", + "Epoch [127/150], Loss: 0.4337, Train Acc: 81.82%, Test Acc: 81.77%\n", + "Epoch [128/150], Loss: 0.3852, Train Acc: 81.86%, Test Acc: 81.86%\n", + "Epoch [129/150], Loss: 0.3925, Train Acc: 82.03%, Test Acc: 82.01%\n", + "Epoch [130/150], Loss: 0.3500, Train Acc: 81.97%, Test Acc: 81.96%\n", + "Epoch [131/150], Loss: 0.4321, Train Acc: 81.59%, Test Acc: 81.52%\n", + "Epoch [132/150], Loss: 0.4256, Train Acc: 82.04%, Test Acc: 82.05%\n", + "Epoch [133/150], Loss: 0.3933, Train Acc: 81.84%, Test Acc: 81.85%\n", + "Epoch [134/150], Loss: 0.4046, Train Acc: 81.88%, Test Acc: 81.83%\n", + "Epoch [135/150], Loss: 0.3698, Train Acc: 81.98%, Test Acc: 81.99%\n", + "Epoch [136/150], Loss: 0.4106, Train Acc: 81.71%, Test Acc: 81.73%\n", + "Epoch [137/150], Loss: 0.4241, Train Acc: 82.09%, Test Acc: 82.08%\n", + "Epoch [138/150], Loss: 0.4078, Train Acc: 81.81%, Test Acc: 81.80%\n", + "Epoch [139/150], Loss: 0.4003, Train Acc: 82.00%, Test Acc: 82.00%\n", + "Epoch [140/150], Loss: 0.3874, Train Acc: 81.93%, Test Acc: 81.96%\n", + "Epoch [141/150], Loss: 0.4104, Train Acc: 82.12%, Test Acc: 82.12%\n", + "Epoch [142/150], Loss: 0.3818, Train Acc: 81.99%, Test Acc: 81.99%\n", + "Epoch [143/150], Loss: 0.3877, Train Acc: 81.86%, Test Acc: 81.83%\n", + "Epoch [144/150], Loss: 0.3628, Train Acc: 82.05%, Test Acc: 82.05%\n", + "Epoch [145/150], Loss: 0.4029, Train Acc: 81.88%, Test Acc: 81.88%\n", + "Epoch [146/150], Loss: 0.4075, Train Acc: 82.13%, Test Acc: 82.12%\n", + "Epoch [147/150], Loss: 0.3636, Train Acc: 81.97%, Test Acc: 81.97%\n", + "Epoch [148/150], Loss: 0.3964, Train Acc: 81.86%, Test Acc: 81.90%\n", + "Epoch [149/150], Loss: 0.3839, Train Acc: 81.60%, Test Acc: 81.59%\n", + "Epoch [150/150], Loss: 0.4315, Train Acc: 82.14%, Test Acc: 82.16%\n" ] } ], @@ -388,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -397,39 +444,39 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Baseline accuracy: 0.8085886240005493\n", + "Baseline accuracy: 0.8040592670440674\n", "Feature importances:\n", - "Feature 22 importance: 0.4068\n", - "Feature 21 importance: 0.3699\n", - "Feature 23 importance: 0.2376\n", - "Feature 20 importance: 0.1619\n", - "Feature 12 importance: 0.0343\n", - "Feature 4 importance: 0.0272\n", - "Feature 8 importance: 0.0156\n", - "Feature 13 importance: 0.0112\n", - "Feature 17 importance: 0.0104\n", - "Feature 16 importance: 0.0046\n", - "Feature 15 importance: 0.0032\n", - "Feature 0 importance: 0.0028\n", - "Feature 9 importance: 0.0023\n", - "Feature 1 importance: 0.0022\n", - "Feature 14 importance: 0.0012\n", - "Feature 3 importance: 0.0010\n", - "Feature 19 importance: 0.0009\n", - "Feature 5 importance: 0.0001\n", - "Feature 11 importance: 0.0000\n", - "Feature 18 importance: -0.0000\n", - "Feature 7 importance: -0.0016\n", - "Feature 10 importance: -0.0020\n", - "Feature 6 importance: -0.0023\n", - "Feature 2 importance: -0.0032\n" + "Feature 8 importance: 0.5686\n", + "Feature 16 importance: 0.5662\n", + "Feature 0 importance: 0.5589\n", + "Feature 4 importance: 0.5551\n", + "Feature 20 importance: 0.5023\n", + "Feature 22 importance: 0.4204\n", + "Feature 21 importance: 0.3989\n", + "Feature 17 importance: 0.3953\n", + "Feature 12 importance: 0.3569\n", + "Feature 9 importance: 0.3118\n", + "Feature 23 importance: 0.3025\n", + "Feature 13 importance: 0.2205\n", + "Feature 1 importance: 0.2043\n", + "Feature 18 importance: 0.1858\n", + "Feature 15 importance: 0.1400\n", + "Feature 11 importance: 0.1197\n", + "Feature 5 importance: 0.1128\n", + "Feature 19 importance: 0.1040\n", + "Feature 10 importance: 0.0606\n", + "Feature 7 importance: 0.0495\n", + "Feature 14 importance: 0.0409\n", + "Feature 2 importance: 0.0270\n", + "Feature 3 importance: 0.0135\n", + "Feature 6 importance: 0.0101\n" ] } ], @@ -476,20 +523,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_37414/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "/tmp/ipykernel_921783/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", " inputs = torch.tensor(features, dtype=torch.float32).to(device)\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -532,16 +579,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.819999890649732, 0.21652840094880765, 0.48821035)" + "(0.8199998933833283, 0.1796430106165911, 0.44098014)" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -561,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -569,109 +616,109 @@ "output_type": "stream", "text": [ "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_0[32] = {\n", - "-0.8883630f, 0.2722728f, 0.1016291f, -1.1676310f, 1.1620101f, 0.1504889f, -1.5435610f, -0.0692244f, -4.2153363f, -4.2260766f, -0.1551204f, 0.1450144f, -0.1899760f, -0.0228148f, -0.3506508f, -0.2040648f, 0.1757486f, -0.1738663f, -0.2319748f, 0.0379042f, 0.0239017f, 0.0139185f, -0.1204398f, -0.1538555f, -0.1220760f, 0.0291483f, 0.1148766f, 0.3259082f, 1.3246336f, 0.6350448f, -0.2796395f, 1.6437438f };\n", + "0.0838128f, -0.0951467f, -0.4538043f, -0.1200482f, 0.1697024f, 0.0734245f, 0.0477040f, -0.1409838f, -0.1598700f, 0.1343284f, 0.7158028f, 0.5663804f, -0.0202696f, -0.0769930f, -0.1565632f, -0.4897312f, -0.1904009f, 0.4263237f, 0.1309257f, 0.1497203f, 0.1124899f, 0.3384814f, -0.2008730f, 0.2663862f, -0.0109372f, -0.2767799f, 0.1633506f, 0.6183366f, 2.0648596f, -0.0196532f, -0.1557027f, -0.3387919f };\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[24][32] = {\n", - "{ 0.0108952f, -0.0558019f, 0.0074057f, 0.0770754f, 0.0638310f, -0.1886167f, 0.0244425f, 0.0255687f, -0.0839688f, -0.1685015f, -0.2186795f, -0.0174607f, -0.1015779f, -0.0892610f, -0.2556714f, 0.0740566f, 0.1856497f, -0.0237876f, 0.1624987f, -0.0158742f, -0.0523282f, 0.1812486f, 0.0906745f, -0.1455424f, 0.1383075f, -0.1130149f, 0.0858218f, -0.0791841f, 0.0118138f, -1.0824271f, 0.0614133f, 0.0247215f },\n", - "{ -0.0615002f, 0.2489090f, -0.0765989f, -0.6762177f, -0.3041955f, 0.1424502f, 0.0689160f, -0.0613275f, -0.7172357f, 1.3835012f, -0.1517068f, -0.0571197f, 0.0906804f, 0.0345163f, 0.1861660f, -0.2219956f, -1.1799500f, -0.1914543f, -0.1125221f, 0.2518745f, -0.0189657f, 0.0231417f, 0.1691398f, 0.1866181f, 0.1034780f, 0.7616542f, 0.0739528f, 0.1423306f, 0.0655985f, 2.1955757f, -0.6869692f, -0.1286531f },\n", - "{ 0.0188954f, 0.0589353f, 0.0441611f, 0.0124622f, -0.0217846f, -0.0808924f, 1.5422715f, -0.0281864f, 0.7299182f, 0.5917343f, -0.1385602f, 0.8845096f, 0.1514033f, 0.0940068f, 0.0920146f, -0.2601782f, 2.6173670f, 0.1569604f, -0.0973214f, 0.7541708f, -0.0897320f, 0.0660505f, 0.1606622f, -0.2816422f, 0.0126745f, 1.0140301f, -0.1677477f, 0.2257937f, -0.2216167f, 0.2004709f, 0.2658010f, 0.0230178f },\n", - "{ 0.0037102f, -0.0040856f, -0.0033745f, 0.0247645f, -0.0453197f, 0.0380025f, 0.0650211f, 0.0057784f, -0.2155162f, -0.1491260f, -0.0617472f, -0.0399091f, 0.1454746f, -0.2295472f, 0.2150548f, -0.0577407f, 0.0278272f, 0.1403611f, 0.1048822f, 0.0695582f, 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-0.1062419f, -0.1901390f, 0.0837805f, -0.1380210f, 0.0310710f, -0.2166184f, -0.0498982f, -0.0786184f, 0.0268683f, -0.1294491f, 0.0897407f, 0.0206377f, -0.1643501f, -0.1812480f, -0.2876152f, -0.0682887f, 0.0446657f, -0.1231280f, -0.0275647f, -0.1300283f, -0.1491375f, 0.0015875f, 0.0132433f, -0.0549486f, -0.2495581f, 0.2349717f, -0.1369720f, -0.3496855f, -0.0560258f, 0.1234621f },\n", + "{ -0.1468480f, 0.1923417f, -0.4420354f, -0.0975916f, 0.0984100f, 0.0142761f, -1.8042085f, 0.0177031f, 0.0466983f, 0.1904279f, 0.2313117f, 0.8943673f, -0.1027022f, -0.0123586f, -0.0259216f, 0.0260046f, 0.1497788f, 0.2727981f, 0.1562366f, -0.3712008f, 0.0626981f, 0.2122544f, 0.3697388f, -0.2779360f, -0.3196765f, -1.3450428f, 0.3793195f, -0.0348413f, -0.1375810f, 0.7617420f, -0.0031145f, 0.1355706f },\n", + "{ 0.0679835f, 0.1254937f, -0.5262930f, 0.1686825f, 0.2174495f, -0.0218732f, -0.1299452f, 0.0997381f, 0.0435618f, 0.1211559f, -1.1586866f, -0.2731386f, 0.0748916f, -0.1903290f, 0.0807933f, 0.1435141f, -1.4356579f, 0.4171664f, 0.0434130f, -0.2107047f, 0.0788040f, -0.2853448f, 0.8026017f, -1.3761326f, 1.2578323f, -1.3311355f, -0.3247376f, -0.1237198f, 1.3546734f, 1.1058371f, 0.1375258f, 0.0693791f },\n", + "{ 0.1654906f, -0.1866392f, -2.5744381f, 0.1619695f, 0.0060606f, -0.0710510f, -4.9512372f, 0.1363884f, 0.0324911f, 0.0960657f, 0.0878271f, -0.0495669f, 0.0635484f, -0.2014371f, -0.1956824f, -0.2231105f, -0.5065056f, -0.2034401f, -0.1735438f, 0.4712444f, 0.0451609f, 0.2407379f, 1.5705422f, 0.8732644f, 0.5805451f, -0.9020337f, 0.1111905f, 0.0054292f, 2.0555997f, -3.0719264f, -0.1595502f, -0.3060012f },\n", + "{ 0.0130320f, -0.1412935f, 0.1939399f, 0.0957547f, -0.1142095f, -0.1203167f, 0.1188142f, 0.0639749f, -0.1311414f, -0.1461854f, -0.0772305f, -0.1230349f, -0.1671150f, -0.1981301f, -0.0226475f, -0.1619669f, 0.1555634f, -0.2390742f, -0.1453369f, -0.2721513f, -0.1140533f, -0.0536937f, 0.1709613f, -0.6350394f, -0.0099144f, -0.0350223f, 0.2447530f, 0.0993788f, -0.5841039f, 0.1384276f, -0.1107760f, -0.1433515f },\n", + "{ -0.0276203f, 0.0424715f, 0.4400442f, -0.1873313f, 0.1983742f, 0.0829819f, 0.6405745f, -0.1593508f, 0.1076834f, 0.1900531f, 1.4096446f, 0.2769921f, -0.1515984f, 0.0578545f, -0.0726278f, -0.0870836f, -3.2511303f, -0.7908387f, -0.0769899f, -1.2463226f, -0.0420534f, 1.5724318f, -0.8705097f, -2.5861135f, 3.8912401f, 4.0461211f, -1.5900346f, 0.3250549f, -2.2681830f, 2.1342385f, -0.1412408f, 0.0973785f },\n", + "{ -0.0631394f, 0.0809900f, -0.8641825f, 0.1149525f, -0.0955641f, -0.0403090f, -0.6845544f, -0.1285507f, -0.0701147f, 0.1852410f, 0.5936438f, 0.1158317f, -0.0838372f, -0.0165345f, -0.0889874f, -0.0779972f, -2.0281746f, 1.2746787f, 0.0807270f, -0.1737853f, -0.1318350f, 0.2905773f, -0.4960580f, 0.2812797f, 0.0395625f, 2.4000103f, -0.3797377f, -0.5155146f, -1.6649362f, 2.7477472f, 0.1732312f, 0.1769285f },\n", + "{ -0.0839652f, 0.1048384f, -2.6453409f, -0.1549998f, 0.0332292f, 0.1805567f, 1.5299331f, 0.1614436f, 0.1030123f, -0.1750580f, -0.0790422f, 0.1303435f, -0.0649011f, -0.1773880f, -0.0186087f, -0.4794982f, -6.5970545f, 0.9689565f, -0.0698152f, 0.2821720f, -0.0943349f, 0.6448916f, -0.5019226f, -2.0137179f, -1.9753901f, 2.3883131f, -0.6336402f, -0.2053137f, -0.1029351f, -3.6074843f, -0.0803197f, -0.3316223f },\n", + "{ -0.1005511f, -0.0575567f, -0.2696874f, -0.1879897f, -0.1549900f, -0.1585334f, -0.2431465f, 0.0235179f, -0.0953478f, -0.0292804f, -0.0737122f, 0.1184362f, -0.1744826f, -0.1343563f, -0.1822797f, 0.3344489f, 0.1855060f, -0.0245743f, 0.0700775f, 0.3525808f, -0.1741875f, 0.0696346f, 0.0178112f, 0.5294384f, -0.0518728f, 0.0456072f, -0.0179709f, -0.3133727f, -0.0621307f, 0.0000746f, -0.1714160f, 0.0660782f },\n", + "{ 0.1368161f, 0.1452735f, -1.0996736f, -0.1370980f, 0.0454142f, -0.2010279f, -0.3923539f, 0.0751393f, 0.1552261f, -0.1690396f, -0.7361290f, 2.2066221f, 0.1860844f, -0.0471949f, -0.1001091f, -0.0785898f, 0.3381744f, 0.2242116f, 0.0059051f, -1.1038984f, -0.0776049f, -0.9301085f, -0.2579322f, 1.0752188f, 0.0572336f, -0.4376161f, 0.4391319f, -0.0920684f, -0.3661392f, -0.0926421f, 0.1151504f, 0.0568435f },\n", + "{ 0.1527036f, 0.0719917f, 1.6668177f, 0.1541860f, 0.1094950f, -0.1874002f, 0.2204740f, 0.0663509f, 0.0846452f, 0.0338055f, 2.6957841f, 2.2497787f, -0.1595391f, 0.1440347f, -0.0457316f, 3.2653592f, 0.3555845f, 0.0221110f, 0.1668054f, 1.9366297f, 0.0111978f, -3.0021029f, 2.4726274f, -1.7558607f, -1.1594486f, 0.0734959f, -2.3178720f, -0.0162731f, 1.3418909f, 0.5486869f, -0.1103047f, -0.0437041f },\n", + "{ 0.0302621f, -0.2280054f, -1.1536891f, 0.0624857f, -0.1923327f, 0.0903575f, 2.9342484f, -0.1453349f, -0.1462534f, 0.1626090f, -2.3898613f, -2.0969083f, 0.1033970f, -0.0779580f, 0.1544486f, 0.0079555f, -0.4402241f, 2.7067254f, -0.0814941f, -2.0344024f, -0.1009802f, 2.3716676f, -2.8162944f, 1.7359691f, 1.1204399f, -0.0530971f, 3.1334810f, 2.6716938f, -1.5494148f, -0.1560390f, 0.1592183f, -0.5042306f },\n", + "{ -0.1881559f, 0.1079059f, 0.8104222f, 0.0100862f, 0.0803289f, -0.1001732f, -3.0945404f, -0.1863656f, 0.1092684f, -0.0122846f, 0.0211765f, 0.0882070f, 0.0887852f, 0.1788233f, 0.1035344f, -3.2046664f, 0.1207470f, -2.7201638f, -0.1267492f, 0.3226216f, 0.0849743f, 0.7428753f, 0.0196231f, 0.0904721f, 0.3495182f, -0.2576671f, -0.4849421f, -2.6337876f, 0.4418600f, -0.0151254f, 0.1613163f, -0.0374872f },\n", "};\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_2[32] = {\n", - "0.5574176f, -0.0158378f, 0.6081951f, -0.2482200f, 1.4885056f, -0.9065233f, -0.8430861f, -0.2685009f, 1.5197985f, 0.2150985f, 3.3223093f, -0.2417838f, 1.1797755f, -1.0702871f, 0.2581681f, 0.2042585f, 1.6163963f, -0.2037005f, 1.4874136f, -1.5563414f, 0.6079807f, -0.2871149f, -1.0653205f, -0.2456795f, 0.6591544f, -0.2727272f, -1.3100427f, 0.2261839f, -0.2026590f, -0.1331568f, 1.0972227f, -1.3647238f };\n", + "-0.1445483f, -0.7468296f, 2.0087461f, 0.9193060f, 0.1838548f, -0.2429122f, -0.3490699f, 0.0500740f, -2.4078236f, -0.1702676f, 0.2733943f, 0.2180888f, -0.2089010f, 1.3755207f, 0.5235071f, -0.4656681f, -0.3255171f, 0.0164221f, 1.8814882f, 0.3419669f, -0.1090209f, 1.3948971f, 0.0137390f, -1.0124286f, -0.5950485f, 0.8902460f, 0.9638857f, -3.0627401f, 1.3535937f, 0.7002685f, -0.3063155f, 0.1972668f };\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_2[32][32] = {\n", - "{ 0.0451835f, -0.2068697f, -0.1455017f, -0.2000958f, 0.1953720f, -0.0868650f, 0.0185502f, -0.1359519f, -0.0893761f, -0.2565004f, -0.5655026f, 0.0635366f, -0.3550099f, 0.1558128f, 0.0306805f, 0.3792097f, 1.8771213f, -0.2383017f, -0.2512103f, -0.8434259f, 0.1358105f, -0.1323305f, -0.0145602f, -0.1016658f, 1.3442085f, 0.1507007f, 0.9711186f, -0.0945338f, 0.2191976f, -0.1611731f, -1.2007302f, 1.1104904f },\n", - "{ -1.3594574f, -0.0378055f, 1.5503316f, -0.1488620f, -1.4691522f, -2.3224826f, 1.1786927f, -0.1508618f, -4.2288308f, 1.2377062f, 0.2050966f, -0.0741773f, -1.5114043f, -2.2442327f, 2.2897842f, -1.3733587f, 0.5636392f, -0.1783939f, 2.4285316f, 0.1796620f, -0.6498010f, -0.2876171f, -1.3547685f, -0.0461334f, -0.9012266f, -1.3740121f, -0.0379446f, -1.0507129f, -1.4431050f, 0.0294942f, -0.3823238f, -2.1220925f },\n", - "{ -1.1411303f, -0.2431736f, -9.9841690f, -0.2129669f, 0.6981345f, -2.1666744f, -10.6869678f, 0.0611885f, 3.2361872f, -0.6304339f, -0.5848778f, 0.1157968f, 0.2787877f, 0.9803044f, -10.3797789f, -0.9443294f, 2.5538800f, -0.0633187f, -10.9630623f, -1.1149275f, -3.8756664f, -0.1332296f, 1.7392989f, 0.0613354f, 0.6508241f, -1.1899891f, 1.0534809f, -0.2019602f, -0.1229644f, 0.1375276f, -4.7887559f, 0.5126888f },\n", - "{ 0.7176909f, -0.0214850f, 0.5826513f, -0.1142717f, 0.9390327f, 1.1849338f, 0.7845815f, -0.1581640f, 0.2477809f, -1.3536477f, -0.6782305f, -0.1947700f, 1.1930919f, 0.5593164f, 0.7056133f, 1.2611432f, 1.3779073f, -0.1165161f, 0.4806055f, 0.1155640f, 0.0619001f, -0.2589326f, 0.7354722f, 0.0066148f, 2.0458858f, 0.8164052f, -0.3876709f, 0.5323008f, 1.1789955f, -0.0268815f, 0.3633842f, 0.4662197f },\n", - "{ 1.9750929f, -0.1468043f, 1.7405676f, -0.1992684f, 2.3235869f, 1.7946367f, 2.0761003f, 0.0511989f, 1.1833738f, 2.0538511f, 0.6360483f, -0.0645854f, 2.5635352f, 1.0803665f, 1.4006170f, 1.9379770f, 0.2737490f, 0.0391885f, 1.1377318f, 1.6247250f, 0.5832859f, -0.0003641f, 2.5831740f, -0.0072090f, -0.7454425f, 1.8515724f, 0.1098597f, 1.7219243f, 1.6892309f, -0.0324973f, -0.4168116f, 0.6863616f },\n", - "{ 0.0752462f, -0.1662275f, 0.1713349f, 0.0999224f, 0.0412468f, 0.0073274f, 0.0941098f, -0.1631816f, -0.0020100f, 0.1488188f, -0.0753984f, -0.0390763f, -0.0592567f, -0.0363634f, 0.0577131f, 0.0113215f, 0.1331281f, 0.0892927f, 0.0323133f, 0.1725931f, -0.0647239f, 0.0597323f, -0.0323511f, 0.0390394f, -0.0969601f, 0.0892076f, 0.1507631f, 0.0258195f, 0.1564926f, 0.0089697f, 0.1074804f, 0.0107034f },\n", - "{ 0.4456752f, -0.0011744f, -0.2483061f, -0.0932769f, 0.6201064f, 0.7728632f, -0.5952806f, -0.3104276f, 0.3310406f, -1.1115030f, 0.6661707f, -0.0648445f, 0.7863559f, 0.9962517f, -0.3727600f, 0.7995458f, -2.2710021f, 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1.5889113f, 2.1328118f, 1.5073299f, -2.3405824f, -0.1999585f, -0.0802431f, -0.8596631f },\n", + "{ -0.1908100f, -0.4481063f, 0.2141561f, -0.8853260f, -0.4353443f, 0.3517573f, -0.2082850f, 0.4145460f, 1.2857832f, -0.3344910f, -0.3868592f, -1.3608005f, -0.0205920f, -0.2011125f, 0.1415093f, -0.9596402f, -0.1987539f, -0.4543975f, 0.1642402f, 0.4037207f, -0.0663036f, -0.5793740f, -0.5624130f, -0.5605171f, -1.0553323f, -1.1488312f, 0.1353024f, -0.6865259f, -1.0829402f, -0.2182627f, -1.7520431f, -0.1677930f },\n", + "{ -0.0542481f, -0.6652975f, 1.7451638f, -1.6006671f, 2.0816922f, -0.0348452f, 0.7610720f, -0.4425903f, 2.3857727f, -0.0982129f, 0.5580666f, -2.4727480f, -0.1531142f, -1.2225132f, -5.4196248f, -1.4664276f, -0.1340837f, -0.3680613f, -1.1763957f, 0.1487435f, -0.1843779f, -1.3475177f, -6.4113221f, 1.9062191f, -1.3199326f, -2.6787331f, -1.0793540f, 1.4754863f, -0.5329170f, -1.0470232f, -0.1719929f, -1.0616958f },\n", + "{ -0.1261046f, -1.2438362f, -3.8054571f, 0.5956330f, 7.1549497f, -0.1402366f, -4.0609927f, 7.6677914f, -2.6154006f, 0.0234051f, 1.2700831f, 7.4880581f, 0.0808564f, -0.2905704f, 2.5056512f, 0.6301501f, -0.1501096f, -0.3806727f, 6.1768942f, -0.2920033f, -0.1472883f, -4.7388387f, 5.9521456f, -2.7825656f, 6.7927275f, 4.6885090f, -2.2919989f, -14.2969074f, 8.4207087f, -6.5338097f, 10.7289143f, -0.5509484f },\n", + "{ -0.1605100f, -2.7664142f, 0.1855993f, -1.0547638f, -0.1559302f, 2.3516369f, -0.0384686f, -0.0421699f, 1.2288936f, -0.1374861f, -0.8669472f, 0.4701964f, -0.2579978f, -1.8223869f, 1.9952940f, -0.8765189f, 0.0131214f, -2.6571078f, 0.8116180f, 1.8054768f, -0.0726431f, 0.5150636f, 0.9614642f, -1.3712974f, -0.4931213f, 1.2799782f, -1.6611663f, 0.4448277f, 0.0846980f, 0.5529866f, -0.0449053f, -0.4308737f },\n", + "{ 0.1551290f, 0.1396936f, -0.0968593f, -0.6554329f, -0.3199727f, -0.3910193f, -1.2765325f, -0.2355604f, -2.7272804f, -0.2628681f, -8.6636467f, 0.3586271f, -0.1602254f, 0.3281886f, 0.0922947f, -5.1625209f, 0.0448225f, 0.4242346f, 2.4615154f, -0.0818966f, 0.0419294f, -15.5337925f, 0.5842085f, -10.8753138f, 0.9091803f, -1.7685370f, 2.0125909f, 0.6737279f, -1.4667115f, -21.1957169f, 1.2548140f, -4.0338879f },\n", + "{ -0.0536758f, 0.0545643f, -1.1698856f, 1.8836620f, -0.1045039f, -0.6019166f, 1.6155248f, -0.2697649f, 1.7763392f, -0.0670235f, 0.2441206f, 0.9861935f, -0.1286846f, -1.2111757f, 1.0097101f, 0.7566754f, -0.0151789f, 0.4013853f, -0.7427294f, -0.2104458f, -0.2336174f, 0.9817276f, 0.6893633f, 0.6530916f, 0.3608560f, -0.2619474f, -0.7111229f, 0.4748580f, 0.4240529f, 0.5841066f, 1.6432278f, 1.4601723f },\n", + "{ 0.0447264f, -0.2559440f, -1.3964423f, 0.9887335f, 0.0025529f, -0.3780882f, 1.8015873f, -0.0102492f, 0.6349514f, -0.0652549f, -11.5391188f, 0.2737688f, -0.0360017f, -1.0004791f, -0.1981463f, 1.8440535f, 0.0371480f, -0.2467426f, -0.6359706f, -0.3593787f, -0.2423154f, -0.3130093f, 0.1481221f, 0.9980756f, 0.4402424f, -0.3383209f, 0.1110670f, 0.1886230f, 0.0224243f, -0.5134141f, 0.2146088f, 1.0987345f },\n", + "{ -0.0964645f, -0.1176648f, -0.1362459f, -0.0702352f, -0.1269714f, 0.0788451f, 0.1349706f, -0.0130557f, -0.1754480f, -0.0714703f, -0.1319758f, 0.1684060f, -0.0253212f, 0.0238707f, 0.1894423f, 0.0064412f, 0.0870870f, 0.1185605f, 0.0594264f, -0.0779627f, 0.1756235f, -0.0078578f, -0.0954016f, 0.0852176f, 0.0516563f, 0.0918788f, -0.0913408f, -0.1448558f, -0.0154965f, -0.0207913f, -0.0115856f, 0.0777761f },\n", + "{ -0.1909802f, -0.2315316f, -0.1041895f, -0.1264509f, -0.0980112f, -0.0968891f, 0.1441685f, -0.1048653f, -0.0836623f, 0.0766368f, -0.3555300f, 0.1044188f, 0.0242796f, 0.2485393f, 0.1461953f, 0.0435378f, 0.0990156f, 0.0944424f, 0.0022342f, -0.4283264f, -0.1376556f, 0.0390784f, -0.1326635f, -0.0682079f, 0.0576475f, -0.0730707f, 0.0102653f, -0.0201001f, 0.0372333f, 0.0019655f, -0.0474310f, -0.0362500f },\n", "};\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {\n", - "0.7004032f };\n", + "-1.0653121f };\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_4[32][1] = {\n", - "{ -1.1784149f },\n", - "{ 0.0064484f },\n", - "{ -1.7084764f },\n", - "{ -0.0371976f },\n", - "{ 1.1174239f },\n", - "{ 0.5888167f },\n", - "{ 1.7407018f },\n", - "{ 0.0329500f },\n", - "{ -0.5985520f },\n", - "{ -0.2985888f },\n", - "{ -0.7626860f },\n", - "{ -0.0314763f },\n", - "{ 1.0361871f },\n", - "{ -0.6576214f },\n", - "{ -1.8324782f },\n", - "{ -1.2968894f },\n", - "{ -0.3412971f },\n", - "{ -0.0388680f },\n", - "{ 1.8274761f },\n", - "{ 0.3623946f },\n", - "{ 0.4834338f },\n", - "{ -0.0507033f },\n", - "{ 0.5347728f },\n", - "{ 0.0183969f },\n", - "{ 0.5529681f },\n", - "{ 0.5601515f },\n", - "{ 0.5157045f },\n", - "{ -1.1073836f },\n", - "{ -0.9019504f },\n", - "{ -0.0337475f },\n", - "{ -0.6909413f },\n", - "{ 0.2963411f },\n", + "{ 0.0599457f },\n", + "{ 2.4589522f },\n", + "{ 0.5886621f },\n", + "{ 0.5058215f },\n", + "{ -1.1172724f },\n", + "{ 1.8007317f },\n", + "{ 0.5281580f },\n", + "{ 0.5044137f },\n", + "{ 0.5756003f },\n", + "{ 0.1070907f },\n", + "{ 0.2529266f },\n", + "{ -1.0334966f },\n", + "{ 0.0026251f },\n", + "{ -0.9314435f },\n", + "{ -1.6016932f },\n", + "{ 0.3742798f },\n", + "{ -0.0195212f },\n", + "{ -2.0840223f },\n", + "{ 0.9543520f },\n", + "{ -3.1566474f },\n", + "{ 0.1217308f },\n", + "{ 1.1695908f },\n", + "{ 0.8822579f },\n", + "{ -1.2197880f },\n", + "{ -2.0250206f },\n", + "{ 0.7175051f },\n", + "{ 0.2349268f },\n", + "{ 0.5229219f },\n", + "{ 0.5292190f },\n", + "{ -1.7123013f },\n", + "{ 0.8434106f },\n", + "{ -1.0713193f },\n", "};\n", "\n" ] diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc index fd4a490c4f60c..babc005a13d2b 100644 --- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc +++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc @@ -2,7 +2,7 @@ using namespace ALPAKA_ACCELERATOR_NAMESPACE; -constexpr int NUM_HITS = 3; // Used by T5 DNN +constexpr int NUM_HITS = 6; // Used by T5 DNN //________________________________________________________________________________________________________________________________ void createOutputBranches() { @@ -170,7 +170,7 @@ void createT5DNNBranches() { ana.tx->createBranch>("t5_t3_phi"); // Hit-specific branches - std::array hitIndices = {"0", "2", "4"}; + std::array hitIndices = {"0", "1", "2", "3", "4", "5"}; std::array hitProperties = {"r", "x", "y", "z", "eta", "phi", "detId", "layer", "moduleType"}; for (const auto& idx : hitIndices) { @@ -617,20 +617,19 @@ void fillT5DNNBranches(lst::Event* event, unsigned int iT3) { lst::Modules const* modules = event->getModules()->data(); std::vector hitIdx = getHitsFromT3(event, iT3); - std::array hitIndices = {hitIdx[0], hitIdx[2], hitIdx[4]}; std::array hitObjects; std::array hitR; for (int i = 0; i < NUM_HITS; ++i) { - unsigned int hit = hitIndices[i]; + unsigned int hit = hitIdx[i]; float x = hits->xs[hit]; float y = hits->ys[hit]; float z = hits->zs[hit]; hitObjects[i] = lst_math::Hit(x, y, z); hitR[i] = sqrt(x * x + y * y); - std::string idx = std::to_string(i * 2); // "0", "2", "4" + std::string idx = std::to_string(i); ana.tx->pushbackToBranch("t5_t3_" + idx + "_r", hitR[i]); ana.tx->pushbackToBranch("t5_t3_" + idx + "_x", x); ana.tx->pushbackToBranch("t5_t3_" + idx + "_y", y); From 472f96da00b490eac59985829bddd0746306a057 Mon Sep 17 00:00:00 2001 From: GNiendorf Date: Mon, 30 Sep 2024 14:03:14 -0400 Subject: [PATCH 2/5] Remove NUM_HITS --- .../standalone/code/core/write_lst_ntuple.cc | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc index babc005a13d2b..00a15b85a8f67 100644 --- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc +++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc @@ -2,8 +2,6 @@ using namespace ALPAKA_ACCELERATOR_NAMESPACE; -constexpr int NUM_HITS = 6; // Used by T5 DNN - //________________________________________________________________________________________________________________________________ void createOutputBranches() { createRequiredOutputBranches(); @@ -170,8 +168,8 @@ void createT5DNNBranches() { ana.tx->createBranch>("t5_t3_phi"); // Hit-specific branches - std::array hitIndices = {"0", "1", "2", "3", "4", "5"}; - std::array hitProperties = {"r", "x", "y", "z", "eta", "phi", "detId", "layer", "moduleType"}; + std::vector hitIndices = {"0", "1", "2", "3", "4", "5"}; + std::vector hitProperties = {"r", "x", "y", "z", "eta", "phi", "detId", "layer", "moduleType"}; for (const auto& idx : hitIndices) { for (const auto& prop : hitProperties) { @@ -617,20 +615,17 @@ void fillT5DNNBranches(lst::Event* event, unsigned int iT3) { lst::Modules const* modules = event->getModules()->data(); std::vector hitIdx = getHitsFromT3(event, iT3); + std::vector hitObjects(hitIdx.size()); - std::array hitObjects; - std::array hitR; - - for (int i = 0; i < NUM_HITS; ++i) { + for (int i = 0; i < hitIdx.size(); ++i) { unsigned int hit = hitIdx[i]; float x = hits->xs[hit]; float y = hits->ys[hit]; float z = hits->zs[hit]; hitObjects[i] = lst_math::Hit(x, y, z); - hitR[i] = sqrt(x * x + y * y); std::string idx = std::to_string(i); - ana.tx->pushbackToBranch("t5_t3_" + idx + "_r", hitR[i]); + ana.tx->pushbackToBranch("t5_t3_" + idx + "_r", sqrt(x * x + y * y)); ana.tx->pushbackToBranch("t5_t3_" + idx + "_x", x); ana.tx->pushbackToBranch("t5_t3_" + idx + "_y", y); ana.tx->pushbackToBranch("t5_t3_" + idx + "_z", z); From debbcc39a624d3450334aa3b5e9bed97adf92330 Mon Sep 17 00:00:00 2001 From: GNiendorf Date: Mon, 30 Sep 2024 21:47:46 -0400 Subject: [PATCH 3/5] better input normalization, remove unused layer input --- .../LSTCore/interface/alpaka/Constants.h | 5 +- .../LSTCore/src/alpaka/NeuralNetwork.h | 58 +- .../LSTCore/src/alpaka/NeuralNetworkWeights.h | 454 +++++++------- .../standalone/analysis/DNN/cut_T5_DNN.ipynb | 67 +- .../analysis/DNN/train_T5_DNN.ipynb | 588 ++++++++---------- 5 files changed, 541 insertions(+), 631 deletions(-) diff --git a/RecoTracker/LSTCore/interface/alpaka/Constants.h b/RecoTracker/LSTCore/interface/alpaka/Constants.h index ebb9f61ec9109..f73cec3d8e891 100644 --- a/RecoTracker/LSTCore/interface/alpaka/Constants.h +++ b/RecoTracker/LSTCore/interface/alpaka/Constants.h @@ -96,12 +96,13 @@ namespace lst { namespace t5dnn { ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kZ_max = 267.2349854f; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kR_max = 110.1099396f; + ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kEta_norm = 2.5f; // pt, eta binned constexpr unsigned int kPtBins = 2; constexpr unsigned int kEtaBins = 10; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kWp[kPtBins][kEtaBins] = { - {0.4386, 0.435, 0.4638, 0.5505, 0.4071, 0.4107, 0.4946, 0.5259, 0.6527, 0.6379}, - {0.2185, 0.2754, 0.2794, 0.3838, 0.2719, 0.2004, 0.2749, 0.1378, 0.2626, 0.1656}}; + {0.2747, 0.2954, 0.3673, 0.4729, 0.419, 0.464, 0.4843, 0.5571, 0.6796, 0.6885}, + {0.1696, 0.255, 0.2716, 0.3713, 0.1902, 0.1597, 0.1446, 0.095, 0.1797, 0.1104}}; } // namespace t5dnn } //namespace lst diff --git a/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h b/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h index 601bfab93ffe4..6be16e52683a7 100644 --- a/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h +++ b/RecoTracker/LSTCore/src/alpaka/NeuralNetwork.h @@ -70,58 +70,42 @@ namespace lst::t5dnn { unsigned int mdIndex3 = mdIndices[2]; unsigned int mdIndex4 = mdIndices[3]; unsigned int mdIndex5 = mdIndices[4]; - // Unpack module indices - uint16_t lowerModuleIndex1 = lowerModuleIndices[0]; - uint16_t lowerModuleIndex2 = lowerModuleIndices[1]; - uint16_t lowerModuleIndex3 = lowerModuleIndices[2]; - uint16_t lowerModuleIndex4 = lowerModuleIndices[3]; - uint16_t lowerModuleIndex5 = lowerModuleIndices[4]; // Compute some convenience variables short layer2_adjustment = 0; - if (modulesInGPU.layers[lowerModuleIndex1] == 1) { + if (modulesInGPU.layers[lowerModuleIndices[0]] == 1) { layer2_adjustment = 1; // get upper segment to be in second layer } unsigned int md_idx_for_t5_eta_phi = segmentsInGPU.mdIndices[2 * tripletsInGPU.segmentIndices[2 * innerTripletIndex + layer2_adjustment]]; - bool is_endcap1 = (modulesInGPU.subdets[lowerModuleIndex1] == 4); // true if anchor hit 1 is in the endcap - bool is_endcap2 = (modulesInGPU.subdets[lowerModuleIndex2] == 4); // true if anchor hit 2 is in the endcap - bool is_endcap3 = (modulesInGPU.subdets[lowerModuleIndex3] == 4); // true if anchor hit 3 is in the endcap - bool is_endcap4 = (modulesInGPU.subdets[lowerModuleIndex4] == 4); // true if anchor hit 4 is in the endcap - bool is_endcap5 = (modulesInGPU.subdets[lowerModuleIndex5] == 4); // true if anchor hit 5 is in the endcap float t5_eta = mdsInGPU.anchorEta[md_idx_for_t5_eta_phi]; // Constants - constexpr unsigned int kinputFeatures = 24; + constexpr unsigned int kinputFeatures = 18; constexpr unsigned int khiddenFeatures = 32; // Build DNN input vector (corresponding output N-tuple branch noted in parenthetical in comment) float x[kinputFeatures] = { - mdsInGPU.anchorEta[mdIndex1], // inner T3 anchor hit 1 eta (t3_0_eta) - mdsInGPU.anchorZ[mdIndex1] / kZ_max, // inner T3 anchor hit 1 z (t3_0_z) - alpaka::math::sqrt(acc, x1 * x1 + y1 * y1) / kR_max, // inner T3 anchor hit 1 r (t3_0_r) - float(modulesInGPU.layers[lowerModuleIndex1] + 6 * is_endcap1), // inner T3 anchor hit 1 layer (t3_0_layer) - mdsInGPU.anchorEta[mdIndex2], // inner T3 anchor hit 2 eta (t3_2_eta) - mdsInGPU.anchorZ[mdIndex2] / kZ_max, // inner T3 anchor hit 2 z (t3_2_z) - alpaka::math::sqrt(acc, x2 * x2 + y2 * y2) / kR_max, // inner T3 anchor hit 2 r (t3_2_r) - float(modulesInGPU.layers[lowerModuleIndex2] + 6 * is_endcap2), // inner T3 anchor hit 2 layer (t3_2_layer) - mdsInGPU.anchorEta[mdIndex3], // inner T3 anchor hit 3 eta (t3_4_eta) - mdsInGPU.anchorZ[mdIndex3] / kZ_max, // inner T3 anchor hit 3 z (t3_4_z) - alpaka::math::sqrt(acc, x3 * x3 + y3 * y3) / kR_max, // inner T3 anchor hit 3 r (t3_4_r) - float(modulesInGPU.layers[lowerModuleIndex3] + 6 * is_endcap3), // inner T3 anchor hit 3 layer (t3_4_layer) - mdsInGPU.anchorEta[mdIndex4], // outer T3 anchor hit 4 eta (t3_2_eta) - mdsInGPU.anchorZ[mdIndex4] / kZ_max, // outer T3 anchor hit 4 z (t3_2_z) - alpaka::math::sqrt(acc, x4 * x4 + y4 * y4) / kR_max, // outer T3 anchor hit 4 r (t3_2_r) - float(modulesInGPU.layers[lowerModuleIndex4] + 6 * is_endcap4), // outer T3 anchor hit 4 layer (t3_2_layer) - mdsInGPU.anchorEta[mdIndex5], // outer T3 anchor hit 5 eta (t3_4_eta) - mdsInGPU.anchorZ[mdIndex5] / kZ_max, // outer T3 anchor hit 5 z (t3_4_z) - alpaka::math::sqrt(acc, x5 * x5 + y5 * y5) / kR_max, // outer T3 anchor hit 5 r (t3_4_r) - float(modulesInGPU.layers[lowerModuleIndex5] + 6 * is_endcap5), // outer T3 anchor hit 5 layer (t3_4_layer) - t5_eta, // T5 eta (t5_eta) - alpaka::math::log10(acc, innerRadius), // T5 inner radius (t5_innerRadius) - alpaka::math::log10(acc, bridgeRadius), // T5 bridge radius (t5_bridgeRadius) - alpaka::math::log10(acc, outerRadius) // T5 outer radius (t5_outerRadius) + alpaka::math::abs(acc, mdsInGPU.anchorEta[mdIndex1]) / kEta_norm, // inner T3 anchor hit 1 eta (t3_0_eta) + alpaka::math::abs(acc, mdsInGPU.anchorZ[mdIndex1]) / kZ_max, // inner T3 anchor hit 1 z (t3_0_z) + alpaka::math::sqrt(acc, x1 * x1 + y1 * y1) / kR_max, // inner T3 anchor hit 1 r (t3_0_r) + alpaka::math::abs(acc, mdsInGPU.anchorEta[mdIndex2]) / kEta_norm, // inner T3 anchor hit 2 eta (t3_2_eta) + alpaka::math::abs(acc, mdsInGPU.anchorZ[mdIndex2]) / kZ_max, // inner T3 anchor hit 2 z (t3_2_z) + alpaka::math::sqrt(acc, x2 * x2 + y2 * y2) / kR_max, // inner T3 anchor hit 2 r (t3_2_r) + alpaka::math::abs(acc, mdsInGPU.anchorEta[mdIndex3]) / kEta_norm, // inner T3 anchor hit 3 eta (t3_4_eta) + alpaka::math::abs(acc, mdsInGPU.anchorZ[mdIndex3]) / kZ_max, // inner T3 anchor hit 3 z (t3_4_z) + alpaka::math::sqrt(acc, x3 * x3 + y3 * y3) / kR_max, // inner T3 anchor hit 3 r (t3_4_r) + alpaka::math::abs(acc, mdsInGPU.anchorEta[mdIndex4]) / kEta_norm, // outer T3 anchor hit 4 eta (t3_2_eta) + alpaka::math::abs(acc, mdsInGPU.anchorZ[mdIndex4]) / kZ_max, // outer T3 anchor hit 4 z (t3_2_z) + alpaka::math::sqrt(acc, x4 * x4 + y4 * y4) / kR_max, // outer T3 anchor hit 4 r (t3_2_r) + alpaka::math::abs(acc, mdsInGPU.anchorEta[mdIndex5]) / kEta_norm, // outer T3 anchor hit 5 eta (t3_4_eta) + alpaka::math::abs(acc, mdsInGPU.anchorZ[mdIndex5]) / kZ_max, // outer T3 anchor hit 5 z (t3_4_z) + alpaka::math::sqrt(acc, x5 * x5 + y5 * y5) / kR_max, // outer T3 anchor hit 5 r (t3_4_r) + + alpaka::math::log10(acc, innerRadius), // T5 inner radius (t5_innerRadius) + alpaka::math::log10(acc, bridgeRadius), // T5 bridge radius (t5_bridgeRadius) + alpaka::math::log10(acc, outerRadius) // T5 outer radius (t5_outerRadius) }; // Layer 1: Linear diff --git a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h index 60cbe80e17637..501492f1b65ac 100644 --- a/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h +++ b/RecoTracker/LSTCore/src/alpaka/NeuralNetworkWeights.h @@ -6,255 +6,231 @@ namespace lst::t5dnn { ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_0[32] = { - 0.0838128f, -0.0951467f, -0.4538043f, -0.1200482f, 0.1697024f, 0.0734245f, 0.0477040f, -0.1409838f, - -0.1598700f, 0.1343284f, 0.7158028f, 0.5663804f, -0.0202696f, -0.0769930f, -0.1565632f, -0.4897312f, - -0.1904009f, 0.4263237f, 0.1309257f, 0.1497203f, 0.1124899f, 0.3384814f, -0.2008730f, 0.2663862f, - -0.0109372f, -0.2767799f, 0.1633506f, 0.6183366f, 2.0648596f, -0.0196532f, -0.1557027f, -0.3387919f}; + -0.1940323f, 0.1562515f, -0.4598289f, -0.0067528f, -0.1187210f, -1.2986372f, -0.7766901f, 0.0709069f, + -0.0413468f, -0.1649964f, -0.0411712f, -0.0118713f, -0.3321564f, 1.0123911f, -0.0118469f, -0.0948771f, + -0.3071513f, -0.0202074f, -0.0227748f, 2.0413475f, -0.9568492f, -0.7617306f, -0.8173049f, 0.0168511f, + 2.0595605f, -2.6405857f, -0.0102134f, 0.3227372f, 0.0028400f, 0.6402014f, -0.2776071f, 0.1328146f}; - ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[24][32] = { - {-0.0482477f, 0.0348692f, -0.6279378f, 0.2042783f, -0.0943811f, -0.0019772f, 1.1357815f, 0.1307896f, - 0.1233541f, -0.0307808f, 0.4762143f, 2.3105178f, -0.1138477f, -0.1789541f, 0.0728833f, 0.0368915f, - 0.1061521f, -0.0356611f, -0.0947667f, -1.7470065f, -0.0434104f, 0.3295992f, -0.6358198f, -0.2336755f, - 1.8262464f, 2.1567848f, -0.4115292f, 0.0250860f, -1.4501210f, -0.5921007f, 0.0049636f, 0.1055770f}, - {-0.0124859f, -0.1331200f, 0.7014464f, -0.1568218f, 0.0331185f, -0.1542812f, -0.1333411f, 0.0127694f, - 0.2013001f, 0.0947020f, -1.4170154f, -0.1189228f, -0.0019860f, 0.1502178f, 0.1740465f, 0.0477101f, - 0.1527060f, -1.3259488f, -0.1455328f, 0.1777444f, 0.1768609f, -0.1144335f, 1.7819276f, -2.2551980f, - 1.8525611f, 1.0002149f, 0.7666884f, 0.5857834f, 0.0189218f, 0.0130888f, 0.0520591f, 0.1602217f}, - {0.0089779f, -0.0046051f, 5.9403129f, -0.0760328f, 0.0868518f, -0.1175489f, 2.4591918f, -0.2272298f, - -0.0336123f, -0.1102009f, 0.4388122f, -0.1027233f, 0.1439395f, 0.0260359f, -0.1548838f, 0.5144961f, - -0.0036697f, -0.0587723f, 0.1452203f, -0.7863321f, -0.0143598f, -0.7969503f, -1.4715855f, 1.8720422f, - -0.5365977f, 1.2278979f, 0.1856042f, 0.0087406f, -0.4033080f, 0.5311748f, -0.0624044f, -0.0473539f}, - {0.0122028f, -0.2384637f, -0.0065890f, -0.1581197f, -0.2024812f, -0.0350186f, -0.1173539f, -0.2257327f, - -0.1062854f, -0.2007362f, -0.0033018f, 0.0028686f, 0.0887160f, -0.0825604f, -0.1360626f, 0.0014523f, - -0.1611109f, 0.0363360f, -0.1258196f, 0.0118614f, -0.1436693f, 0.0181161f, -0.0238848f, -0.0896234f, - -0.0039286f, -0.0287253f, -0.0076090f, -0.0047007f, 0.3169636f, -0.1194062f, 0.0768581f, -0.1023149f}, - {0.0223803f, -0.1106429f, 0.5197684f, 0.0797990f, -0.0985189f, -0.0219536f, -0.8999558f, 0.0139441f, - -0.1065298f, -0.0700749f, -0.2372502f, -2.3253367f, -0.1771734f, -0.1391621f, -0.0327205f, 0.0384778f, - 0.3209504f, 0.0964711f, -0.1038988f, 2.0001328f, 0.0278964f, -0.6402076f, 0.1002734f, 0.9442523f, - -2.3371346f, -1.3519518f, 0.5144972f, -0.1425193f, 1.6520097f, 0.2593979f, -0.1068970f, -0.0010823f}, - {-0.0564048f, 0.0277004f, 0.4086671f, -0.1428425f, -0.1345516f, -0.0642777f, -1.4967046f, -0.0728123f, - 0.1499694f, -0.0660839f, 1.1170599f, -0.0109156f, -0.0217163f, -0.1839852f, -0.0723716f, -0.2208258f, - -2.1450739f, -0.7447581f, 0.2110604f, -0.6763240f, -0.0871866f, 0.1980208f, -0.3258589f, 0.3583415f, - -1.5599525f, -0.9544728f, 0.3547978f, 0.2802322f, -0.3988611f, 1.6168940f, 0.1046868f, -0.0736501f}, - {0.0401295f, 0.0301625f, 2.3904793f, 0.0874403f, -0.1706232f, 0.0243270f, 0.2055506f, 0.0659292f, - 0.0279800f, -0.1013176f, -0.4468603f, -0.0820311f, 0.1084237f, -0.0980428f, 0.0153869f, 0.4169671f, - 1.1679637f, -0.7452526f, 0.0690438f, -0.9760146f, -0.0150821f, -0.1838745f, 0.0022138f, 0.4647771f, - 0.6666930f, -0.5373036f, -0.4382644f, 0.1287971f, 0.4949077f, -0.2705404f, -0.0644356f, -0.1727526f}, - {0.1045402f, -0.0420851f, -0.0156088f, 0.1043605f, -0.0468452f, -0.1996713f, -0.1536377f, -0.0548137f, - 0.0782699f, -0.1749692f, 0.0110849f, -0.0338493f, -0.0110409f, -0.1772420f, 0.1318522f, 0.0084175f, - -0.2084620f, 0.3595731f, -0.2131062f, -0.0738994f, -0.1284403f, 0.0381397f, 0.0738881f, -0.0387961f, - 0.0070784f, -0.0201305f, 0.0226532f, -0.0150129f, 0.1880418f, -0.1988496f, 0.0518940f, -0.0426258f}, - {0.0555184f, -0.1434738f, 1.5241326f, -0.1319865f, -0.1433413f, 0.0522304f, -1.4563835f, 0.0005232f, - 0.1506346f, -0.0265684f, -1.2287844f, -3.0413165f, 0.0025170f, 0.0599654f, -0.1306973f, 0.0784461f, - 0.5079768f, -0.0244722f, -0.1015498f, 2.7846479f, -0.0642611f, -0.7250540f, 0.8876019f, 1.4510521f, - -2.9753745f, -3.3278594f, 0.8736179f, -0.0000811f, 2.6898973f, 0.4387932f, -0.0675147f, 0.1717749f}, - {-0.2273859f, -0.0783074f, 0.1170714f, 0.1458802f, 0.1328210f, 0.0364441f, 1.4164704f, 0.1079648f, - 0.1553876f, 0.0617190f, -0.7688578f, -0.5899147f, -0.0151878f, 0.0300152f, 0.1985418f, 0.1041053f, - 0.2805509f, -0.3530872f, 0.1957988f, 0.0688579f, 0.1145971f, -0.1384836f, 0.7624677f, -0.7887678f, - -1.1695358f, -2.7840865f, -0.0668691f, 0.1524266f, 0.9591433f, -0.9727835f, 0.1578966f, 0.1385039f}, - {-0.0925403f, 0.1268597f, 1.9899433f, -0.0697888f, -0.1969929f, -0.0471073f, -5.3116655f, -0.0091008f, - -0.1728659f, -0.1753619f, -0.1478174f, 0.2951335f, 0.1457811f, -0.0851820f, 0.1457849f, 0.4183387f, - -2.3975048f, -0.4645267f, 0.0937851f, -0.3560269f, -0.1869052f, 0.0136887f, 0.5827022f, 1.1965749f, - 1.5047479f, -1.9283267f, 0.0473070f, 0.0353213f, 0.4265198f, -3.5288622f, 0.1322581f, -0.5738994f}, - {-0.1227338f, 0.0047513f, -0.1062419f, -0.1901390f, 0.0837805f, -0.1380210f, 0.0310710f, -0.2166184f, - -0.0498982f, -0.0786184f, 0.0268683f, -0.1294491f, 0.0897407f, 0.0206377f, -0.1643501f, -0.1812480f, - -0.2876152f, -0.0682887f, 0.0446657f, -0.1231280f, -0.0275647f, -0.1300283f, -0.1491375f, 0.0015875f, - 0.0132433f, -0.0549486f, -0.2495581f, 0.2349717f, -0.1369720f, -0.3496855f, -0.0560258f, 0.1234621f}, - {-0.1468480f, 0.1923417f, -0.4420354f, -0.0975916f, 0.0984100f, 0.0142761f, -1.8042085f, 0.0177031f, - 0.0466983f, 0.1904279f, 0.2313117f, 0.8943673f, -0.1027022f, -0.0123586f, -0.0259216f, 0.0260046f, - 0.1497788f, 0.2727981f, 0.1562366f, -0.3712008f, 0.0626981f, 0.2122544f, 0.3697388f, -0.2779360f, - -0.3196765f, -1.3450428f, 0.3793195f, -0.0348413f, -0.1375810f, 0.7617420f, -0.0031145f, 0.1355706f}, - {0.0679835f, 0.1254937f, -0.5262930f, 0.1686825f, 0.2174495f, -0.0218732f, -0.1299452f, 0.0997381f, - 0.0435618f, 0.1211559f, -1.1586866f, -0.2731386f, 0.0748916f, -0.1903290f, 0.0807933f, 0.1435141f, - -1.4356579f, 0.4171664f, 0.0434130f, -0.2107047f, 0.0788040f, -0.2853448f, 0.8026017f, -1.3761326f, - 1.2578323f, -1.3311355f, -0.3247376f, -0.1237198f, 1.3546734f, 1.1058371f, 0.1375258f, 0.0693791f}, - {0.1654906f, -0.1866392f, -2.5744381f, 0.1619695f, 0.0060606f, -0.0710510f, -4.9512372f, 0.1363884f, - 0.0324911f, 0.0960657f, 0.0878271f, -0.0495669f, 0.0635484f, -0.2014371f, -0.1956824f, -0.2231105f, - -0.5065056f, -0.2034401f, -0.1735438f, 0.4712444f, 0.0451609f, 0.2407379f, 1.5705422f, 0.8732644f, - 0.5805451f, -0.9020337f, 0.1111905f, 0.0054292f, 2.0555997f, -3.0719264f, -0.1595502f, -0.3060012f}, - {0.0130320f, -0.1412935f, 0.1939399f, 0.0957547f, -0.1142095f, -0.1203167f, 0.1188142f, 0.0639749f, - -0.1311414f, -0.1461854f, -0.0772305f, -0.1230349f, -0.1671150f, -0.1981301f, -0.0226475f, -0.1619669f, - 0.1555634f, -0.2390742f, -0.1453369f, -0.2721513f, -0.1140533f, -0.0536937f, 0.1709613f, -0.6350394f, - -0.0099144f, -0.0350223f, 0.2447530f, 0.0993788f, -0.5841039f, 0.1384276f, -0.1107760f, -0.1433515f}, - {-0.0276203f, 0.0424715f, 0.4400442f, -0.1873313f, 0.1983742f, 0.0829819f, 0.6405745f, -0.1593508f, - 0.1076834f, 0.1900531f, 1.4096446f, 0.2769921f, -0.1515984f, 0.0578545f, -0.0726278f, -0.0870836f, - -3.2511303f, -0.7908387f, -0.0769899f, -1.2463226f, -0.0420534f, 1.5724318f, -0.8705097f, -2.5861135f, - 3.8912401f, 4.0461211f, -1.5900346f, 0.3250549f, -2.2681830f, 2.1342385f, -0.1412408f, 0.0973785f}, - {-0.0631394f, 0.0809900f, -0.8641825f, 0.1149525f, -0.0955641f, -0.0403090f, -0.6845544f, -0.1285507f, - -0.0701147f, 0.1852410f, 0.5936438f, 0.1158317f, -0.0838372f, -0.0165345f, -0.0889874f, -0.0779972f, - -2.0281746f, 1.2746787f, 0.0807270f, -0.1737853f, -0.1318350f, 0.2905773f, -0.4960580f, 0.2812797f, - 0.0395625f, 2.4000103f, -0.3797377f, -0.5155146f, -1.6649362f, 2.7477472f, 0.1732312f, 0.1769285f}, - {-0.0839652f, 0.1048384f, -2.6453409f, -0.1549998f, 0.0332292f, 0.1805567f, 1.5299331f, 0.1614436f, - 0.1030123f, -0.1750580f, -0.0790422f, 0.1303435f, -0.0649011f, -0.1773880f, -0.0186087f, -0.4794982f, - -6.5970545f, 0.9689565f, -0.0698152f, 0.2821720f, -0.0943349f, 0.6448916f, -0.5019226f, -2.0137179f, - -1.9753901f, 2.3883131f, -0.6336402f, -0.2053137f, -0.1029351f, -3.6074843f, -0.0803197f, -0.3316223f}, - {-0.1005511f, -0.0575567f, -0.2696874f, -0.1879897f, -0.1549900f, -0.1585334f, -0.2431465f, 0.0235179f, - -0.0953478f, -0.0292804f, -0.0737122f, 0.1184362f, -0.1744826f, -0.1343563f, -0.1822797f, 0.3344489f, - 0.1855060f, -0.0245743f, 0.0700775f, 0.3525808f, -0.1741875f, 0.0696346f, 0.0178112f, 0.5294384f, - -0.0518728f, 0.0456072f, -0.0179709f, -0.3133727f, -0.0621307f, 0.0000746f, -0.1714160f, 0.0660782f}, - {0.1368161f, 0.1452735f, -1.0996736f, -0.1370980f, 0.0454142f, -0.2010279f, -0.3923539f, 0.0751393f, - 0.1552261f, -0.1690396f, -0.7361290f, 2.2066221f, 0.1860844f, -0.0471949f, -0.1001091f, -0.0785898f, - 0.3381744f, 0.2242116f, 0.0059051f, -1.1038984f, -0.0776049f, -0.9301085f, -0.2579322f, 1.0752188f, - 0.0572336f, -0.4376161f, 0.4391319f, -0.0920684f, -0.3661392f, -0.0926421f, 0.1151504f, 0.0568435f}, - {0.1527036f, 0.0719917f, 1.6668177f, 0.1541860f, 0.1094950f, -0.1874002f, 0.2204740f, 0.0663509f, - 0.0846452f, 0.0338055f, 2.6957841f, 2.2497787f, -0.1595391f, 0.1440347f, -0.0457316f, 3.2653592f, - 0.3555845f, 0.0221110f, 0.1668054f, 1.9366297f, 0.0111978f, -3.0021029f, 2.4726274f, -1.7558607f, - -1.1594486f, 0.0734959f, -2.3178720f, -0.0162731f, 1.3418909f, 0.5486869f, -0.1103047f, -0.0437041f}, - {0.0302621f, -0.2280054f, -1.1536891f, 0.0624857f, -0.1923327f, 0.0903575f, 2.9342484f, -0.1453349f, - -0.1462534f, 0.1626090f, -2.3898613f, -2.0969083f, 0.1033970f, -0.0779580f, 0.1544486f, 0.0079555f, - -0.4402241f, 2.7067254f, -0.0814941f, -2.0344024f, -0.1009802f, 2.3716676f, -2.8162944f, 1.7359691f, - 1.1204399f, -0.0530971f, 3.1334810f, 2.6716938f, -1.5494148f, -0.1560390f, 0.1592183f, -0.5042306f}, - {-0.1881559f, 0.1079059f, 0.8104222f, 0.0100862f, 0.0803289f, -0.1001732f, -3.0945404f, -0.1863656f, - 0.1092684f, -0.0122846f, 0.0211765f, 0.0882070f, 0.0887852f, 0.1788233f, 0.1035344f, -3.2046664f, - 0.1207470f, -2.7201638f, -0.1267492f, 0.3226216f, 0.0849743f, 0.7428753f, 0.0196231f, 0.0904721f, - 0.3495182f, -0.2576671f, -0.4849421f, -2.6337876f, 0.4418600f, -0.0151254f, 0.1613163f, -0.0374872f}, + ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[18][32] = { + {0.0452029f, -0.2047395f, -0.2531095f, 0.8308914f, 0.1026031f, -0.5966103f, 1.4759157f, -0.0898649f, + -0.1962480f, -0.1148645f, 0.1196134f, 0.0548274f, -0.1407367f, -1.4861078f, -0.0349070f, -1.1813899f, + -0.2423109f, 0.0454410f, -0.0287287f, -0.0014218f, 0.2253178f, -1.6985301f, 3.8701379f, -0.9332715f, + -1.6066414f, 0.7657530f, -0.1616500f, 3.7211533f, -0.4365099f, -0.5467676f, 0.0612534f, -0.1052311f}, + {-0.2059304f, 0.0514157f, 1.5633677f, 0.1810661f, -0.0710148f, -1.3003422f, -0.0254397f, -0.2410158f, + -0.2113242f, -0.2738193f, 0.0433658f, -0.0664363f, -0.7619049f, -0.6734435f, -0.2305467f, 0.7696750f, + -0.3101756f, -0.0359285f, 0.1335582f, -1.5694221f, 0.9542036f, -0.6244334f, 2.4346864f, -0.7677089f, + -1.1617776f, -3.1731126f, -0.0121344f, 0.9073577f, -0.2181434f, -0.3400409f, -0.1639360f, -0.1658078f}, + {0.1472459f, -0.0126969f, 3.1386833f, 0.4109284f, 0.1190476f, 0.3891498f, -0.1224196f, -0.0641578f, + 0.0702143f, -0.1379193f, -0.1345438f, 0.0194056f, 0.1905062f, 2.0310915f, -0.1589341f, 0.7496570f, + -0.1105359f, -0.0770280f, 0.0943906f, -0.5389218f, 0.0715920f, 0.6358653f, 1.0064040f, 0.4764405f, + 0.9542342f, 0.2242897f, 0.1616169f, -1.3778440f, -0.2705311f, -1.2358010f, -0.1538814f, -0.0438948f}, + {-0.1763135f, -0.0535656f, 0.5643759f, -1.5390383f, -0.2005513f, 0.6275172f, -0.0381056f, 0.1703591f, + 0.0655765f, 0.1213387f, 0.0543906f, -0.0061972f, 0.5066937f, -0.1915186f, 0.4624113f, 0.7285631f, + 1.9588530f, -0.1498045f, 0.2705339f, 0.4348728f, -0.8126648f, -0.5962468f, 0.2308548f, 0.5948858f, + 1.7573367f, 0.3198567f, 0.0890794f, -1.0203332f, 0.9259652f, 1.9378320f, -0.0893076f, -0.1205216f}, + {0.0800774f, -0.3748720f, -1.4099081f, -0.7579946f, 0.1303505f, 1.2617210f, -0.4090959f, -0.2189664f, + 0.1956004f, -0.2206552f, -0.0664704f, -0.1259056f, 0.5783974f, 0.2635866f, -0.4015432f, -0.5615420f, + 1.1665078f, 0.3563137f, -0.4841422f, 0.0495584f, -1.0221890f, -2.2138078f, 0.1603903f, -0.2881177f, + 0.3440269f, -1.4136810f, -0.1204567f, -0.2976407f, -0.3660032f, 0.6511291f, -0.0036185f, -0.1103064f}, + {-0.1190768f, 0.1079465f, -1.6465895f, 0.8783018f, 0.0417785f, -0.4893148f, 0.5675116f, -0.0493726f, + 0.0923501f, -0.3304987f, -0.2314915f, -0.0379382f, 0.3644635f, 1.6061251f, 0.0743982f, -1.7036310f, + 0.0469393f, -0.0169076f, -0.0846457f, 0.3039347f, 1.5323132f, 1.1516945f, -0.4392051f, 0.0519393f, + -0.8842877f, 0.0210567f, 0.0042385f, 0.3631802f, 0.0299365f, -0.1161491f, -0.2673467f, 0.0801027f}, + {-0.1606232f, -0.1174612f, 1.1393912f, -3.1626005f, 0.2020190f, 0.3364365f, -1.4127800f, -0.2035749f, + 0.0290988f, 0.0998231f, -0.0858107f, -0.0822701f, 0.4266679f, 1.1170434f, -0.2634015f, 2.6879389f, + 3.5829473f, -0.0074217f, -0.2283435f, -0.4018083f, -0.5503482f, -1.4264138f, -1.8605294f, 1.9073273f, + 2.7757249f, 1.0840679f, -0.1608723f, -4.1708899f, 0.5306313f, 3.3872285f, 0.0317254f, 0.0453062f}, + {0.1028529f, -0.1862935f, -0.3514106f, -1.2896683f, -0.2075851f, 1.7440273f, -0.0317084f, -0.0256567f, + 0.0827107f, 0.0535544f, -0.0810672f, 0.1013722f, 0.2883120f, -1.0009999f, 0.2229198f, 1.1056199f, + 0.9040332f, -0.2615984f, 0.2158209f, -0.9291855f, 0.3614044f, -2.4015853f, -2.1240189f, 0.7009790f, + 0.9322513f, 0.6083323f, 0.1290441f, -1.7820632f, 0.5162377f, 1.6934893f, 0.1093729f, 0.1678278f}, + {-0.0738688f, -0.2484219f, -2.2049072f, 0.6735009f, 0.0845890f, -0.4458043f, 1.6374553f, -0.1284482f, + -0.0363122f, -0.2654149f, -0.0454312f, -0.0264876f, -0.1786719f, -2.2485936f, -0.0132505f, -2.4829731f, + -1.0582074f, 0.0639074f, -0.0570137f, 0.2790116f, 1.2043806f, 0.6573969f, 0.2257148f, -0.7038473f, + -1.1433469f, -0.2824484f, -0.1686299f, 0.6064975f, -0.1482748f, 0.9308873f, 0.0353047f, -0.2331351f}, + {-0.0381717f, 0.0235109f, 1.2123448f, 1.5028327f, -0.0427347f, -0.6673427f, -1.3916098f, -0.2319166f, + -0.0112034f, -0.0724124f, -0.0266188f, 0.0489188f, -2.5781660f, 0.9075448f, -0.2781274f, -0.5351671f, + -1.4783920f, 0.2318245f, 0.1258357f, 1.6879724f, 2.2178104f, 2.5865467f, -2.9686368f, 2.0748942f, + -1.0530401f, -1.1760957f, -0.1100548f, 0.7415332f, -1.2369473f, -2.2372057f, -0.1560877f, -0.2036909f}, + {-0.0767220f, -0.3813806f, -0.6692066f, 1.1171360f, 0.1955123f, 0.0927913f, -0.6164231f, -0.1936815f, + -0.2253457f, 0.2098780f, -0.2356415f, -0.0879891f, -2.2692015f, -0.7018560f, 0.2930437f, 1.4553533f, + -1.0976124f, -0.2235222f, -0.5320002f, 0.6203124f, 2.2931771f, -0.4128396f, -2.1847551f, 1.8210577f, + -1.0626109f, 0.4428261f, -0.1638004f, 0.5285351f, -0.1491041f, -0.0726089f, -0.1858867f, 0.1690394f}, + {0.1563593f, -0.1862237f, -1.8475114f, -0.7378953f, 0.0983567f, 0.1551867f, 2.2162786f, -0.0932272f, + -0.1106849f, -0.1326451f, 0.0376333f, 0.0157612f, 2.8148468f, -1.8456433f, 0.0834190f, 2.4704628f, + 0.1500785f, -0.0245777f, 0.0363232f, -2.9865775f, -1.7058473f, 0.6917011f, 1.1903836f, -1.1306528f, + 1.4213639f, 1.8961535f, -0.0105329f, -0.2176351f, 0.4710608f, 1.1942669f, -0.0887468f, 0.2132694f}, + {0.2071738f, -0.2212865f, 0.7850192f, 2.1796567f, -0.0984315f, 0.0805375f, 2.7735672f, -0.1555860f, + 0.0239974f, 0.0246591f, 0.1187868f, -0.0409408f, 2.2348316f, 1.5618666f, 0.0371693f, -1.9186479f, + -2.7904696f, -0.0837378f, -0.1520312f, 1.2995666f, -1.4488002f, 2.6741729f, 1.2172064f, -3.4318860f, + -0.3286010f, 1.1488677f, -0.1920759f, 1.0220169f, 0.0960360f, -2.2466736f, -0.1439289f, -0.2038657f}, + {0.0203539f, -0.0770830f, -0.0548076f, 0.3161826f, -0.1546766f, 1.5058581f, 0.3716706f, -0.0279853f, + -0.0396944f, 0.1678968f, -0.0523217f, 0.1077326f, 1.3584960f, 0.5697956f, -0.0556758f, -1.0488331f, + -0.9394681f, 0.1332565f, 0.4668984f, 0.1539229f, -1.3861572f, 0.1012657f, 1.3388863f, -1.5068510f, + -0.1488119f, 2.1504519f, 0.0248965f, 0.1813536f, 0.1426364f, -2.6684127f, 0.1982870f, 0.1384249f}, + {-0.1769641f, -0.0247985f, -0.1647689f, -1.3870394f, -0.1162030f, 0.3048662f, -2.8828626f, 0.1057448f, + 0.1339286f, 0.0585208f, 0.1083090f, 0.0354991f, -2.4567196f, -2.1254835f, 0.0228132f, -0.1640477f, + 0.6994863f, 0.0368678f, 0.0509801f, -2.8892326f, 0.9703321f, 0.0786054f, 0.0938517f, 1.1638378f, + 1.1636230f, -0.1186888f, 0.0215163f, 0.1806985f, -0.1176552f, -1.1696986f, 0.1148661f, 0.2213662f}, + {0.1038774f, -0.1047234f, 1.4868294f, 0.2761928f, 0.0134923f, -1.4871290f, -0.2099304f, -0.1526921f, + -0.1791854f, 0.0421853f, -0.2292036f, -0.0114745f, -1.5435252f, -1.5150722f, -1.7114086f, 0.0551655f, + 0.1734200f, 1.7328296f, -0.0102979f, -0.0923821f, 1.5666803f, -0.1455855f, -0.1308604f, -0.0883516f, + 0.0417202f, 0.0224278f, -0.1899172f, -0.0607252f, 1.7729037f, -0.7333850f, 0.1708566f, -0.1185266f}, + {-0.1968291f, 0.1226442f, -1.4931865f, 0.2072496f, 0.0330375f, 1.5259913f, 0.2502024f, 0.1059954f, + -0.2105065f, 0.0289575f, -0.1410347f, -1.6446069f, 0.4217086f, 0.9671080f, 1.6962469f, -0.2187338f, + -0.1057917f, 0.1871334f, 2.0668314f, 0.1059684f, -0.7089236f, 0.1020357f, -0.0763040f, 0.3453935f, + -1.0794582f, 0.6731552f, -0.2174709f, -0.1014975f, -1.8536516f, 0.4382406f, -0.1931745f, -0.1499902f}, + {-0.0451520f, -0.0075974f, -0.0751040f, -0.0006091f, -0.0962802f, -0.0943780f, 0.0421998f, -0.0221936f, + 0.0959424f, -0.0376601f, -0.2136606f, 1.6487268f, 1.1979698f, 0.6900949f, -0.0029702f, 0.1591523f, + 0.1682678f, -1.9215510f, -2.0697236f, 0.0993362f, -0.7588745f, 0.1796909f, 0.2314903f, -0.2894705f, + -0.7456803f, -0.5973564f, -0.2129087f, 0.1293475f, 0.0351008f, 0.2747299f, -0.2403971f, -0.2218651f}, }; ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_2[32] = { - -0.1445483f, -0.7468296f, 2.0087461f, 0.9193060f, 0.1838548f, -0.2429122f, -0.3490699f, 0.0500740f, - -2.4078236f, -0.1702676f, 0.2733943f, 0.2180888f, -0.2089010f, 1.3755207f, 0.5235071f, -0.4656681f, - -0.3255171f, 0.0164221f, 1.8814882f, 0.3419669f, -0.1090209f, 1.3948971f, 0.0137390f, -1.0124286f, - -0.5950485f, 0.8902460f, 0.9638857f, -3.0627401f, 1.3535937f, 0.7002685f, -0.3063155f, 0.1972668f}; + 0.7934635f, -0.9544018f, 0.8020973f, -0.0911000f, -0.2036114f, -0.6670265f, -2.4799440f, -0.3794742f, + -0.9009696f, 0.3158563f, 0.6310644f, 0.0615856f, -0.0852578f, 0.2415757f, 0.9112729f, 0.4271888f, + 0.4842319f, -0.2101376f, 0.0233746f, 0.4788695f, -0.1818675f, 0.2429213f, -0.1633921f, -0.1643308f, + -0.1243287f, 0.8031887f, -0.2019781f, -0.0105518f, -0.2115961f, -0.0436087f, -1.2556835f, 0.4181046f}; ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_2[32][32] = { - {0.1387030f, 0.0578285f, 0.0290207f, 0.1366628f, -0.0933111f, -0.1532757f, 0.0912275f, 0.0719271f, - -0.1804712f, 0.0502727f, 0.0857729f, -0.1440653f, 0.1051157f, -0.0418550f, -0.0386804f, 0.1002010f, - 0.1612325f, -0.0438476f, -0.1817690f, 0.0312359f, 0.0725582f, -0.0386392f, 0.0083110f, 0.0938581f, - 0.0128068f, -0.1367846f, 0.0775078f, 0.1099567f, 0.1366342f, -0.1446508f, -0.1838375f, 0.1260892f}, - {-0.0889172f, -0.1109058f, -0.0090499f, -0.0161888f, 0.1067007f, 0.0519907f, 0.0063411f, -0.1817823f, - -0.0761359f, -0.1343981f, -0.2255760f, 0.0553785f, 0.0583183f, 0.1439044f, -0.0505752f, 0.0720810f, - 0.1430652f, -0.1004833f, 0.0520413f, -0.0544464f, 0.0066742f, 0.0136002f, 0.1225696f, -0.1489387f, - -0.0167017f, 0.0657442f, 0.0523059f, 0.0774479f, -0.0676060f, 0.0078165f, -0.0837835f, -0.1274106f}, - {-0.0766311f, 0.5671838f, 0.5687331f, 0.8707032f, -0.5383500f, -0.1704384f, 0.6303636f, -0.0391398f, - -0.9736328f, -0.0891117f, 0.6284696f, 0.2864082f, -0.0644129f, 1.0049138f, 0.2648700f, 0.7155174f, - -0.1337392f, 0.1083976f, -0.0553817f, -0.3307861f, -0.0462360f, 0.0436576f, 0.0994856f, -0.3148151f, - 0.0330411f, 0.4916244f, 0.4885519f, 0.6376212f, -0.4065242f, 0.3103931f, -0.1638803f, 0.8019430f}, - {0.1626605f, 0.1472229f, -0.1055374f, -0.0479780f, -0.1384696f, -0.0590811f, -0.1443276f, 0.0591924f, - -0.1503216f, -0.0844078f, 0.0829195f, 0.0628104f, 0.1337180f, 0.1166687f, -0.0531621f, -0.0368584f, - 0.1572870f, 0.0799009f, 0.1366610f, -0.0022395f, -0.0167398f, -0.0069433f, -0.0972410f, -0.1593408f, - -0.0069130f, 0.1214338f, -0.0003829f, -0.1232101f, -0.1103388f, 0.0411238f, 0.0677348f, -0.1645412f}, - {0.1631524f, 0.1126567f, -0.0254589f, -0.1650690f, 0.0330545f, 0.1297599f, 0.1422060f, -0.0161238f, - -0.1338266f, -0.0908349f, -0.1737766f, -0.0230168f, -0.1365137f, -0.0978333f, -0.0950168f, 0.0188880f, - 0.1741089f, -0.0294606f, 0.1752428f, 0.1644219f, 0.1659159f, 0.0680770f, 0.1018012f, -0.1279233f, - -0.1190702f, 0.1255178f, 0.0606153f, 0.0523735f, -0.0188357f, 0.0630456f, -0.0843453f, -0.1214397f}, - {0.0315945f, -0.0944538f, -0.0443451f, 0.0960032f, -0.1633853f, -0.0567770f, 0.0730636f, -0.0249795f, - 0.1353328f, 0.0574157f, -0.0449853f, -0.1242244f, 0.0586789f, -0.1486070f, -0.0526930f, -0.1114865f, - -0.1567526f, 0.0295868f, -0.0528042f, -0.1501717f, 0.0414447f, 0.0129001f, -0.1081947f, -0.0410711f, - -0.1285951f, -0.0928800f, 0.1285344f, -0.1409433f, -0.1518052f, -0.0423772f, -0.1049293f, -0.0810852f}, - {-0.0440271f, 0.0225357f, -0.2800989f, -0.5222433f, 0.1739161f, -0.1177652f, 0.7314388f, 1.3036448f, - -0.1802850f, 0.1001820f, 1.6026971f, 0.7367986f, -0.1845069f, -0.2386096f, -0.7598913f, -3.4017467f, - -0.2444120f, -0.2411640f, -0.3806236f, -0.0073185f, -0.0934157f, -1.4343293f, -0.1366896f, 0.0053347f, - 0.6870482f, 0.5276791f, -17.2342720f, -1.4306241f, 2.8685725f, -1.2868855f, -0.8033525f, -0.5142425f}, - {-0.1103665f, -0.0303087f, 0.0721292f, 0.0373054f, 0.1220894f, -0.1477783f, 0.0285878f, -0.1038156f, - 0.0902443f, 0.0387605f, -0.1263115f, -0.0328336f, -0.1103565f, -0.0743521f, 0.0545940f, -0.1695098f, - -0.0291077f, -0.0735082f, 0.0353564f, -0.0256771f, -0.0926927f, 0.0906134f, -0.0178829f, 0.0771544f, - -0.1435781f, -0.1402228f, 0.1299576f, -0.0348156f, -0.0974614f, -0.0316729f, 0.0602557f, 0.0533389f}, - {0.1760506f, -0.0859205f, -0.0774204f, 0.0131467f, 0.1391187f, -0.0065549f, 0.1738602f, 0.1052436f, - 0.0141879f, -0.1249992f, 0.1461107f, 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1.8491809f, -0.0846886f, 1.4933884f, 0.7164953f, -1.6774675f}, + {-0.8079155f, 0.3042560f, -0.3772373f, 1.2257376f, 0.0458694f, 0.9980869f, 0.5855812f, 1.1597202f, + 1.0134934f, 1.4520537f, 0.2989449f, -1.9389783f, -0.0603162f, 0.4532191f, -1.2892554f, 0.6409937f, + 0.2874748f, -0.1664991f, 0.8620583f, 0.4548919f, -0.1099896f, -0.6457837f, -0.0331152f, -0.3548099f, + -0.1999796f, -0.8440968f, -0.9994648f, -1.4008234f, -0.2282692f, 0.7556357f, 1.6185784f, -0.3155516f}, + {-0.6114326f, 0.6241459f, -0.1294827f, 0.7645832f, 0.0769744f, -0.0152130f, -0.3445112f, 0.2391609f, + 0.6720194f, -0.0221121f, -0.6067243f, 0.1666614f, 0.2123979f, 0.1255938f, -0.3345444f, 2.0463579f, + -1.0124280f, -0.0962251f, -0.8581216f, -0.7843506f, -0.0683384f, -1.4236431f, -0.0455818f, -0.0086093f, + -0.1512000f, -0.4655181f, 0.9514965f, 1.3690557f, 0.0408875f, 0.1874518f, 0.0787549f, 0.3078735f}, + {-1.1322931f, 0.2055396f, 0.2440919f, 0.1584100f, 0.0394220f, -0.4304818f, 1.9068531f, -0.3199218f, + 0.1083218f, 0.3324785f, -1.0065278f, 0.5865496f, -0.3660905f, -0.2893182f, 0.9594055f, -2.2053986f, + -1.8336630f, 0.1346838f, -1.2970884f, -1.0563467f, 0.0139557f, 0.3008765f, -0.0802731f, 0.0236470f, + -0.1399251f, -1.2832147f, 0.8925338f, 0.3402558f, -0.3059315f, 0.1579103f, 0.0145165f, 0.2028373f}, + {0.9092978f, 5.3319697f, 0.2311574f, -3.5974376f, -0.1275796f, -1.4258120f, -2.3457932f, 1.4555787f, + 4.1725507f, 1.6273310f, 1.1012670f, -10.8166332f, -1.0383276f, -2.8341811f, -8.7946100f, -0.5989234f, + 6.0220866f, 0.0696776f, 6.2771916f, 4.7770872f, -0.1951438f, -9.8738918f, -0.0931274f, -0.2637655f, + -0.1809545f, 5.6408782f, -5.2057590f, -3.3519218f, -0.0901985f, -4.2171011f, -1.3777201f, -1.0980587f}, + {1.1602964f, -0.2687808f, 0.2866415f, -0.0052205f, -0.2066985f, -0.0813395f, 0.2317347f, -0.5450784f, + -0.3015029f, -0.0025978f, 0.2577495f, -0.8998445f, 0.0237917f, -0.7391278f, 1.0870081f, 0.5068464f, + 0.7862117f, -0.1163245f, 1.1998304f, 1.2305090f, -0.0814890f, -0.4244935f, -0.0811057f, -0.3696067f, + -0.0861830f, 0.8701236f, -0.2289178f, -0.5556117f, -0.2133671f, -0.4388837f, 0.2475479f, -0.3172738f}, + {-0.0219820f, -2.1448636f, -3.3920438f, 0.6697657f, -0.1315291f, -0.2115767f, 0.2022458f, -0.7636336f, + -0.5977439f, 0.4382638f, -0.0576872f, 0.8378263f, -11.2913847f, -2.9919012f, -0.1020024f, 0.4660376f, + -0.4539831f, -0.0388663f, -0.8509534f, -0.0664919f, -0.0877846f, 0.5318345f, 0.1295045f, -0.0434388f, + -0.1501493f, -0.6051254f, 0.1701003f, -0.2226598f, -0.1804415f, -1.1222337f, 1.0541458f, 1.5254205f}, + {0.1420137f, -0.0074933f, 0.0067201f, 0.0537190f, 0.1508925f, -0.0164899f, -0.1151883f, 0.0132960f, + -0.0462974f, -0.1229345f, -0.0079655f, 0.1646094f, -0.0517144f, 0.0189287f, 0.0986247f, 0.1024838f, + 0.0433510f, -0.0154221f, 0.0869387f, 0.1480348f, 0.1454650f, -0.1666742f, 0.0122260f, 0.0189736f, + -0.0929280f, -0.0581923f, 0.1017774f, 0.0487137f, 0.0692026f, -0.1308839f, 0.0729854f, -0.1369110f}, + {-4.8658338f, 1.5531696f, -0.2368621f, -1.4992270f, -0.1853091f, -0.0324190f, 2.7762654f, 3.2764831f, + 4.1259456f, 2.0090275f, 1.8382020f, 0.7285845f, 0.8604074f, 0.9081600f, -2.6200793f, -6.3929181f, + 1.3202529f, 0.0161599f, -2.6859567f, -3.4331827f, -0.2077297f, 0.3192596f, -0.1211096f, 0.0369134f, + -0.0057372f, -2.5233526f, 1.4855275f, -0.8395525f, -0.0542362f, 0.3147244f, 0.5668546f, 0.1128324f}, + {0.2185126f, -4.1735148f, -0.0584993f, -6.9970717f, -0.1872474f, 3.6046195f, -0.3391172f, -0.2528733f, + -1.8540699f, -3.6080678f, -6.8880978f, 2.8173163f, 1.1419358f, -0.5156693f, 0.6379013f, -0.8689519f, + -0.5731303f, -0.0135396f, -3.8898597f, 3.9107504f, -0.0314495f, 3.7597058f, -0.1313266f, -0.0638606f, + -0.1112971f, 0.5994733f, -2.0880733f, -3.4605267f, -0.0657795f, 2.9317980f, -4.0776825f, -4.6187515f}, + {1.4306588f, -0.0924573f, -0.0255171f, -0.4936610f, -0.1691499f, 0.0794270f, -0.4408483f, 0.1652155f, + -0.3639386f, 0.6207126f, 0.2110025f, -1.1174132f, 0.3868589f, -0.3762612f, -0.2353463f, -0.7807826f, + 1.1994385f, 0.0715448f, 2.5127218f, 1.3676612f, -0.1004328f, -0.9425803f, -0.0994552f, -0.3417697f, + -0.1076159f, 0.9109147f, -0.8347710f, 0.3245273f, 0.0240645f, 0.0364262f, 0.7244037f, 0.1504523f}, + {-0.0121835f, -0.0466975f, 0.1144113f, 0.0201244f, -0.0245609f, 0.2358061f, -0.0764014f, -0.2775822f, + -0.2624222f, -0.0493765f, -0.1292863f, -0.1509897f, 0.0240071f, 0.0579032f, -0.0009308f, 0.0350776f, + -0.1369558f, 0.0210988f, 0.1830976f, -0.0220976f, -0.1343361f, 0.1934764f, -0.0788890f, 0.1397982f, + -0.0866419f, -0.0710399f, -0.1467413f, -0.1701790f, 0.0331260f, -0.0187575f, 0.0254800f, 0.1567311f}, + {0.0645845f, -0.0035480f, 0.1547592f, -0.1243696f, -0.0513117f, -0.0051543f, 0.0720794f, 0.0330604f, + -0.0983970f, -0.0621364f, 0.1500005f, -0.0834217f, 0.0614519f, -0.1354975f, -0.1619547f, 0.1409725f, + 0.0976805f, 0.0951235f, -0.1414357f, 0.0502984f, -0.0352365f, -0.0664824f, -0.1300988f, -0.0588439f, + -0.0466940f, -0.1232816f, 0.0044184f, 0.1664057f, 0.0861590f, -0.0289132f, -0.1097487f, -0.0449765f}, }; - ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {-1.0653121f}; + ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {0.2672311f}; ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_4[32][1] = { - {0.0599457f}, {2.4589522f}, {0.5886621f}, {0.5058215f}, {-1.1172724f}, {1.8007317f}, {0.5281580f}, - {0.5044137f}, {0.5756003f}, {0.1070907f}, {0.2529266f}, {-1.0334966f}, {0.0026251f}, {-0.9314435f}, - {-1.6016932f}, {0.3742798f}, {-0.0195212f}, {-2.0840223f}, {0.9543520f}, {-3.1566474f}, {0.1217308f}, - {1.1695908f}, {0.8822579f}, {-1.2197880f}, {-2.0250206f}, {0.7175051f}, {0.2349268f}, {0.5229219f}, - {0.5292190f}, {-1.7123013f}, {0.8434106f}, {-1.0713193f}, + {-2.1982157f}, {1.2918848f}, {2.5765901f}, {1.3302900f}, {0.0534258f}, {1.6870886f}, {-1.5375290f}, + {-1.7200431f}, {-1.6248944f}, {1.0983198f}, {0.9794226f}, {-3.2129023f}, {1.6483670f}, {0.7734902f}, + {-0.9205871f}, {-0.9046884f}, {-0.9422176f}, {0.0940378f}, {2.0178165f}, {-2.5040693f}, {0.0147824f}, + {1.8138467f}, {0.0226220f}, {0.0023397f}, {0.0879065f}, {1.4868226f}, {1.2005153f}, {-1.0860479f}, + {0.0119623f}, {1.2583143f}, {1.1449329f}, {1.2751638f}, }; } //namespace lst::t5dnn diff --git a/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb b/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb index e67564cee946c..ee914c9f72137 100644 --- a/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb +++ b/RecoTracker/LSTCore/standalone/analysis/DNN/cut_T5_DNN.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_904678/3231266229.py:5: DeprecationWarning: \n", + "/tmp/ipykernel_1669972/3231266229.py:5: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", @@ -77,15 +77,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Z max: 267.2349853515625, R max: 110.10993957519531\n" + "Z max: 267.2349853515625, R max: 110.10993957519531, Eta max: 2.5\n" ] } ], "source": [ "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n", "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n", + "eta_max = 2.5\n", "\n", - "print(f'Z max: {z_max}, R max: {r_max}')" + "print(f'Z max: {z_max}, R max: {r_max}, Eta max: {eta_max}')" ] }, { @@ -108,35 +109,29 @@ "\n", " # Collect features using idx0\n", " features_iter.extend([\n", - " branches['t5_t3_0_eta'][event][idx0],\n", - " branches['t5_t3_0_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_0_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_0_z'][event][idx0]) / z_max,\n", " branches['t5_t3_0_r'][event][idx0] / r_max,\n", - " branches['t5_t3_0_layer'][event][idx0],\n", - " branches['t5_t3_2_eta'][event][idx0],\n", - " branches['t5_t3_2_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_2_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_2_z'][event][idx0]) / z_max,\n", " branches['t5_t3_2_r'][event][idx0] / r_max,\n", - " branches['t5_t3_2_layer'][event][idx0],\n", - " branches['t5_t3_4_eta'][event][idx0],\n", - " branches['t5_t3_4_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_4_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_4_z'][event][idx0]) / z_max,\n", " branches['t5_t3_4_r'][event][idx0] / r_max,\n", - " branches['t5_t3_4_layer'][event][idx0],\n", " ])\n", " \n", " # Collect features using idx1\n", " features_iter.extend([\n", - " branches['t5_t3_2_eta'][event][idx1],\n", - " branches['t5_t3_2_z'][event][idx1] / z_max,\n", + " np.abs(branches['t5_t3_2_eta'][event][idx1]) / eta_max,\n", + " np.abs(branches['t5_t3_2_z'][event][idx1]) / z_max,\n", " branches['t5_t3_2_r'][event][idx1] / r_max,\n", - " branches['t5_t3_2_layer'][event][idx1],\n", - " branches['t5_t3_4_eta'][event][idx1],\n", - " branches['t5_t3_4_z'][event][idx1] / z_max,\n", + " np.abs(branches['t5_t3_4_eta'][event][idx1]) / eta_max,\n", + " np.abs(branches['t5_t3_4_z'][event][idx1]) / z_max,\n", " branches['t5_t3_4_r'][event][idx1] / r_max,\n", - " branches['t5_t3_4_layer'][event][idx1],\n", " ])\n", " \n", " # Add remaining features\n", " features_iter.extend([\n", - " branches['t5_eta'][event][i],\n", " np.log10(branches['t5_innerRadius'][event][i]),\n", " np.log10(branches['t5_bridgeRadius'][event][i]),\n", " np.log10(branches['t5_outerRadius'][event][i]),\n", @@ -242,7 +237,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -263,12 +258,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -281,15 +276,14 @@ "output_type": "stream", "text": [ "pt: 0 to 5\n", - "96% Retention Cut: [0.4386 0.435 0.4638 0.5505 0.4071 0.4107 0.4946 0.5259 0.6527 0.6379] Mean: 0.5017\n", - "99% Retention Cut: [0.1294 0.1237 0.1594 0.2274 0.2029 0.2416 0.3168 0.3414 0.4438 0.3951] Mean: 0.2582\n", - "99.9% Retention Cut: [0.0323 0.0298 0.0422 0.0607 0.0595 0.0654 0.1282 0.1102 0.1015 0.0903] Mean: 0.072\n", - "99.5% Retention Cut: [0.077 0.074 0.0998 0.1392 0.1352 0.1574 0.2531 0.2601 0.322 0.2704] Mean: 0.1788\n" + "97.5% Retention Cut: [0.2747 0.2954 0.3673 0.4729 0.419 0.464 0.4843 0.5571 0.6796 0.6885] Mean: 0.4703\n", + "99% Retention Cut: [0.1289 0.1517 0.2095 0.2914 0.267 0.3383 0.3729 0.4156 0.5056 0.4715] Mean: 0.3152\n", + "99% Retention Cut: [0.0278 0.0212 0.0491 0.0777 0.0522 0.0967 0.1503 0.1203 0.1123 0.0928] Mean: 0.08\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -302,10 +296,9 @@ "output_type": "stream", "text": [ "pt: 5 to inf\n", - "96% Retention Cut: [0.2185 0.2754 0.2794 0.3838 0.2719 0.2004 0.2749 0.1378 0.2626 0.1656] Mean: 0.247\n", - "99% Retention Cut: [0.093 0.1094 0.114 0.1597 0.1151 0.0812 0.1198 0.0212 0.1269 0.0687] Mean: 0.1009\n", - "99.9% Retention Cut: [0.03 0.058 0.0218 0.0451 0.0505 0.0141 0.0451 0.011 0.0341 0.0231] Mean: 0.0333\n", - "99.5% Retention Cut: [0.0687 0.0887 0.0689 0.0981 0.0739 0.0568 0.0771 0.0123 0.0798 0.0449] Mean: 0.0669\n" + "97.5% Retention Cut: [0.1696 0.255 0.2716 0.3713 0.1902 0.1597 0.1446 0.095 0.1797 0.1104] Mean: 0.1947\n", + "99% Retention Cut: [0.0666 0.1075 0.1381 0.1792 0.0809 0.0831 0.0722 0.0453 0.1181 0.0676] Mean: 0.0959\n", + "99% Retention Cut: [0.0074 0.0256 0.0151 0.0042 0.0094 0.0035 0.0195 0.0082 0.0457 0.0123] Mean: 0.0151\n" ] } ], @@ -326,6 +319,7 @@ " \n", " # Lists to store cut values for different percentiles\n", " cut_values_96 = []\n", + " cut_values_97 = []\n", " cut_values_99 = []\n", " cut_values_99_9 = []\n", " cut_values_99_5 = []\n", @@ -340,12 +334,14 @@ " \n", " # Calculate the percentile cut values for the current bin\n", " cut_value_96 = np.percentile(bin_predictions, 4)\n", + " cut_value_97 = np.percentile(bin_predictions, 2.5)\n", " cut_value_99 = np.percentile(bin_predictions, 1)\n", " cut_value_99_5 = np.percentile(bin_predictions, 0.5)\n", " cut_value_99_9 = np.percentile(bin_predictions, 0.1)\n", " \n", " # Store the cut values\n", " cut_values_96.append(cut_value_96)\n", + " cut_values_97.append(cut_value_97)\n", " cut_values_99.append(cut_value_99)\n", " cut_values_99_9.append(cut_value_99_9)\n", " cut_values_99_5.append(cut_value_99_5)\n", @@ -359,19 +355,18 @@ "\n", " # Plot the cut values\n", " cut_x = eta_bin_edges[:-1] + (eta_bin_edges[1] - eta_bin_edges[0]) / 2 # Mid-points of the bins\n", - " plt.plot(cut_x, cut_values_96, 'g-', marker='o', label='96% Retention Cut')\n", + " plt.plot(cut_x, cut_values_97, 'g-', marker='o', label='97.5% Retention Cut')\n", " plt.plot(cut_x, cut_values_99, 'r-', marker='o', label='99% Retention Cut')\n", " plt.plot(cut_x, cut_values_99_9, 'b-', marker='o', label='99.9% Retention Cut')\n", " plt.legend()\n", - "\n", + " \n", " plt.show()\n", " \n", " # Print the cut values\n", " print(f\"pt: {pt_min} to {pt_max}\")\n", - " print(\"96% Retention Cut:\", np.round(cut_values_96, 4), \" Mean: \", np.round(np.mean(cut_values_96),4))\n", + " print(\"97.5% Retention Cut:\", np.round(cut_values_97, 4), \" Mean: \", np.round(np.mean(cut_values_97),4))\n", " print(\"99% Retention Cut:\", np.round(cut_values_99, 4), \" Mean: \", np.round(np.mean(cut_values_99),4))\n", - " print(\"99.9% Retention Cut:\", np.round(cut_values_99_9, 4), \" Mean: \", np.round(np.mean(cut_values_99_9),4))\n", - " print(\"99.5% Retention Cut:\", np.round(cut_values_99_5, 4), \" Mean: \", np.round(np.mean(cut_values_99_5),4))\n", + " print(\"99% Retention Cut:\", np.round(cut_values_99_9, 4), \" Mean: \", np.round(np.mean(cut_values_99_9),4))\n", "\n", "# Plot for each pt bin\n", "for i in range(len(pt_bins) - 1):\n", diff --git a/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb index b22b42b670655..96e79a748cd18 100644 --- a/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb +++ b/RecoTracker/LSTCore/standalone/analysis/DNN/train_T5_DNN.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_921783/49064298.py:4: DeprecationWarning: \n", + "/tmp/ipykernel_1570202/49064298.py:4: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", @@ -76,15 +76,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Z max: 267.2349853515625, R max: 110.10993957519531\n" + "Z max: 267.2349853515625, R max: 110.10993957519531, Eta max: 2.5\n" ] } ], "source": [ "z_max = np.max([np.max(event) for event in branches[f't5_t3_4_z']])\n", "r_max = np.max([np.max(event) for event in branches[f't5_t3_4_r']])\n", + "eta_max = 2.5\n", "\n", - "print(f'Z max: {z_max}, R max: {r_max}')" + "print(f'Z max: {z_max}, R max: {r_max}, Eta max: {eta_max}')" ] }, { @@ -107,35 +108,29 @@ "\n", " # Collect features using idx0\n", " features_iter.extend([\n", - " branches['t5_t3_0_eta'][event][idx0],\n", - " branches['t5_t3_0_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_0_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_0_z'][event][idx0]) / z_max,\n", " branches['t5_t3_0_r'][event][idx0] / r_max,\n", - " branches['t5_t3_0_layer'][event][idx0],\n", - " branches['t5_t3_2_eta'][event][idx0],\n", - " branches['t5_t3_2_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_2_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_2_z'][event][idx0]) / z_max,\n", " branches['t5_t3_2_r'][event][idx0] / r_max,\n", - " branches['t5_t3_2_layer'][event][idx0],\n", - " branches['t5_t3_4_eta'][event][idx0],\n", - " branches['t5_t3_4_z'][event][idx0] / z_max,\n", + " np.abs(branches['t5_t3_4_eta'][event][idx0]) / eta_max,\n", + " np.abs(branches['t5_t3_4_z'][event][idx0]) / z_max,\n", " branches['t5_t3_4_r'][event][idx0] / r_max,\n", - " branches['t5_t3_4_layer'][event][idx0],\n", " ])\n", " \n", " # Collect features using idx1\n", " features_iter.extend([\n", - " branches['t5_t3_2_eta'][event][idx1],\n", - " branches['t5_t3_2_z'][event][idx1] / z_max,\n", + " np.abs(branches['t5_t3_2_eta'][event][idx1]) / eta_max,\n", + " np.abs(branches['t5_t3_2_z'][event][idx1]) / z_max,\n", " branches['t5_t3_2_r'][event][idx1] / r_max,\n", - " branches['t5_t3_2_layer'][event][idx1],\n", - " branches['t5_t3_4_eta'][event][idx1],\n", - " branches['t5_t3_4_z'][event][idx1] / z_max,\n", + " np.abs(branches['t5_t3_4_eta'][event][idx1]) / eta_max,\n", + " np.abs(branches['t5_t3_4_z'][event][idx1]) / z_max,\n", " branches['t5_t3_4_r'][event][idx1] / r_max,\n", - " branches['t5_t3_4_layer'][event][idx1],\n", " ])\n", " \n", " # Add remaining features\n", " features_iter.extend([\n", - " branches['t5_eta'][event][i],\n", " np.log10(branches['t5_innerRadius'][event][i]),\n", " np.log10(branches['t5_bridgeRadius'][event][i]),\n", " np.log10(branches['t5_outerRadius'][event][i]),\n", @@ -181,156 +176,156 @@ "text": [ "Using device: cuda\n", "torch.Size([1959386]) torch.Size([7315929])\n", - "Epoch [1/150], Loss: 0.5115, Train Acc: 74.98%, Test Acc: 74.94%\n", - "Epoch [2/150], Loss: 0.5177, Train Acc: 76.74%, Test Acc: 76.78%\n", - "Epoch [3/150], Loss: 0.4744, Train Acc: 76.94%, Test Acc: 77.01%\n", - "Epoch [4/150], Loss: 0.4641, Train Acc: 78.26%, Test Acc: 78.24%\n", - "Epoch [5/150], Loss: 0.4863, Train Acc: 79.80%, Test Acc: 79.81%\n", - "Epoch [6/150], Loss: 0.4271, Train Acc: 79.45%, Test Acc: 79.48%\n", - "Epoch [7/150], Loss: 0.4607, Train Acc: 79.81%, Test Acc: 79.77%\n", - "Epoch [8/150], Loss: 0.4450, Train Acc: 80.26%, Test Acc: 80.27%\n", - "Epoch [9/150], Loss: 0.4461, Train Acc: 80.04%, Test Acc: 80.05%\n", - "Epoch [10/150], Loss: 0.4812, Train Acc: 80.07%, Test Acc: 80.10%\n", - "Epoch [11/150], Loss: 0.4179, Train Acc: 80.44%, Test Acc: 80.45%\n", - "Epoch [12/150], Loss: 0.4324, Train Acc: 80.54%, Test Acc: 80.54%\n", - "Epoch [13/150], Loss: 0.3995, Train Acc: 80.26%, Test Acc: 80.25%\n", - "Epoch [14/150], Loss: 0.4148, Train Acc: 80.86%, Test Acc: 80.89%\n", - "Epoch [15/150], Loss: 0.4148, Train Acc: 80.64%, Test Acc: 80.67%\n", - "Epoch [16/150], Loss: 0.4399, Train Acc: 80.48%, Test Acc: 80.49%\n", - "Epoch [17/150], Loss: 0.4080, Train Acc: 80.79%, Test Acc: 80.81%\n", - "Epoch [18/150], Loss: 0.4104, Train Acc: 80.17%, Test Acc: 80.16%\n", - "Epoch [19/150], Loss: 0.4381, Train Acc: 79.65%, Test Acc: 79.58%\n", - "Epoch [20/150], Loss: 0.4599, Train Acc: 80.84%, Test Acc: 80.84%\n", - "Epoch [21/150], Loss: 0.4455, Train Acc: 80.44%, Test Acc: 80.46%\n", - "Epoch [22/150], Loss: 0.4085, Train Acc: 79.95%, Test Acc: 79.95%\n", - "Epoch [23/150], Loss: 0.4848, Train Acc: 80.67%, Test Acc: 80.66%\n", - "Epoch [24/150], Loss: 0.4093, Train Acc: 80.85%, Test Acc: 80.82%\n", - "Epoch [25/150], Loss: 0.3990, Train Acc: 80.41%, Test Acc: 80.42%\n", - "Epoch [26/150], Loss: 0.4257, Train Acc: 80.93%, Test Acc: 80.92%\n", - "Epoch [27/150], Loss: 0.4595, Train Acc: 81.04%, Test Acc: 81.02%\n", - "Epoch [28/150], Loss: 0.3922, Train Acc: 81.08%, Test Acc: 81.07%\n", - "Epoch [29/150], Loss: 0.4134, Train Acc: 80.78%, Test Acc: 80.82%\n", - "Epoch [30/150], Loss: 0.4502, Train Acc: 79.55%, Test Acc: 79.55%\n", - "Epoch [31/150], Loss: 0.4346, Train Acc: 80.99%, Test Acc: 81.00%\n", - "Epoch [32/150], Loss: 0.4383, Train Acc: 80.97%, Test Acc: 80.94%\n", - "Epoch [33/150], Loss: 0.3846, Train Acc: 81.23%, Test Acc: 81.26%\n", - "Epoch [34/150], Loss: 0.4196, Train Acc: 80.80%, Test Acc: 80.81%\n", - "Epoch [35/150], Loss: 0.4406, Train Acc: 80.48%, Test Acc: 80.47%\n", - "Epoch [36/150], Loss: 0.4385, Train Acc: 80.92%, Test Acc: 80.96%\n", - "Epoch [37/150], Loss: 0.4520, Train Acc: 80.85%, Test Acc: 80.84%\n", - "Epoch [38/150], Loss: 0.3885, Train Acc: 81.26%, Test Acc: 81.19%\n", - "Epoch [39/150], Loss: 0.3782, Train Acc: 81.27%, Test Acc: 81.32%\n", - "Epoch [40/150], Loss: 0.4312, Train Acc: 80.76%, Test Acc: 80.75%\n", - "Epoch [41/150], Loss: 0.4351, Train Acc: 80.97%, Test Acc: 80.98%\n", - "Epoch [42/150], Loss: 0.4140, Train Acc: 81.11%, Test Acc: 81.06%\n", - "Epoch [43/150], Loss: 0.4191, Train Acc: 81.28%, Test Acc: 81.25%\n", - "Epoch [44/150], Loss: 0.3999, Train Acc: 81.19%, Test Acc: 81.17%\n", - "Epoch [45/150], Loss: 0.4078, Train Acc: 81.16%, Test Acc: 81.17%\n", - "Epoch [46/150], Loss: 0.4647, Train Acc: 81.14%, Test Acc: 81.17%\n", - "Epoch [47/150], Loss: 0.4304, Train Acc: 81.04%, Test Acc: 81.00%\n", - "Epoch [48/150], Loss: 0.4298, Train Acc: 81.25%, Test Acc: 81.25%\n", - "Epoch [49/150], Loss: 0.4327, Train Acc: 81.18%, Test Acc: 81.17%\n", - "Epoch [50/150], Loss: 0.4095, Train Acc: 81.04%, Test Acc: 81.07%\n", - "Epoch [51/150], Loss: 0.4093, Train Acc: 81.32%, Test Acc: 81.30%\n", - "Epoch [52/150], Loss: 0.4062, Train Acc: 80.96%, Test Acc: 80.92%\n", - "Epoch [53/150], Loss: 0.4460, Train Acc: 81.13%, Test Acc: 81.11%\n", - "Epoch [54/150], Loss: 0.4404, Train Acc: 81.16%, Test Acc: 81.15%\n", - "Epoch [55/150], Loss: 0.3972, Train Acc: 81.17%, Test Acc: 81.17%\n", - "Epoch [56/150], Loss: 0.3996, Train Acc: 81.36%, Test Acc: 81.40%\n", - "Epoch [57/150], Loss: 0.3874, Train Acc: 81.06%, Test Acc: 81.03%\n", - "Epoch [58/150], Loss: 0.4535, Train Acc: 81.27%, Test Acc: 81.25%\n", - "Epoch [59/150], Loss: 0.4000, Train Acc: 81.31%, Test Acc: 81.28%\n", - "Epoch [60/150], Loss: 0.3947, Train Acc: 81.23%, Test Acc: 81.22%\n", - "Epoch [61/150], Loss: 0.3979, Train Acc: 81.38%, Test Acc: 81.36%\n", - "Epoch [62/150], Loss: 0.4352, Train Acc: 80.94%, Test Acc: 80.94%\n", - "Epoch [63/150], Loss: 0.4103, Train Acc: 81.12%, Test Acc: 81.13%\n", - "Epoch [64/150], Loss: 0.4164, Train Acc: 81.56%, Test Acc: 81.52%\n", - "Epoch [65/150], Loss: 0.4395, Train Acc: 81.46%, Test Acc: 81.41%\n", - "Epoch [66/150], Loss: 0.3913, Train Acc: 81.41%, Test Acc: 81.37%\n", - "Epoch [67/150], Loss: 0.4198, Train Acc: 81.08%, Test Acc: 81.09%\n", - "Epoch [68/150], Loss: 0.4233, Train Acc: 81.33%, Test Acc: 81.33%\n", - "Epoch [69/150], Loss: 0.4067, Train Acc: 81.23%, Test Acc: 81.21%\n", - "Epoch [70/150], Loss: 0.4310, Train Acc: 81.42%, Test Acc: 81.38%\n", - "Epoch [71/150], Loss: 0.4070, Train Acc: 81.62%, Test Acc: 81.60%\n", - "Epoch [72/150], Loss: 0.3374, Train Acc: 81.53%, Test Acc: 81.51%\n", - "Epoch [73/150], Loss: 0.3700, Train Acc: 81.26%, Test Acc: 81.19%\n", - "Epoch [74/150], Loss: 0.4108, Train Acc: 81.30%, Test Acc: 81.27%\n", - "Epoch [75/150], Loss: 0.4065, Train Acc: 81.37%, Test Acc: 81.35%\n", - "Epoch [76/150], Loss: 0.4566, Train Acc: 81.25%, Test Acc: 81.22%\n", - "Epoch [77/150], Loss: 0.3794, Train Acc: 81.43%, Test Acc: 81.37%\n", - "Epoch [78/150], Loss: 0.3935, Train Acc: 81.30%, Test Acc: 81.32%\n", - "Epoch [79/150], Loss: 0.4369, Train Acc: 81.38%, Test Acc: 81.33%\n", - "Epoch [80/150], Loss: 0.4056, Train Acc: 81.68%, Test Acc: 81.70%\n", - "Epoch [81/150], Loss: 0.4121, Train Acc: 81.16%, Test Acc: 81.12%\n", - "Epoch [82/150], Loss: 0.3845, Train Acc: 81.21%, Test Acc: 81.11%\n", - "Epoch [83/150], Loss: 0.4175, Train Acc: 81.67%, Test Acc: 81.64%\n", - "Epoch [84/150], Loss: 0.4160, Train Acc: 81.59%, Test Acc: 81.55%\n", - "Epoch [85/150], Loss: 0.4226, Train Acc: 81.08%, Test Acc: 81.05%\n", - "Epoch [86/150], Loss: 0.4430, Train Acc: 81.48%, Test Acc: 81.44%\n", - "Epoch [87/150], Loss: 0.4124, Train Acc: 81.31%, Test Acc: 81.27%\n", - "Epoch [88/150], Loss: 0.4199, Train Acc: 81.55%, Test Acc: 81.51%\n", - "Epoch [89/150], Loss: 0.4129, Train Acc: 81.71%, Test Acc: 81.67%\n", - "Epoch [90/150], Loss: 0.3956, Train Acc: 81.58%, Test Acc: 81.58%\n", - "Epoch [91/150], Loss: 0.4176, Train Acc: 81.53%, Test Acc: 81.51%\n", - "Epoch [92/150], Loss: 0.4325, Train Acc: 81.13%, Test Acc: 81.12%\n", - "Epoch [93/150], Loss: 0.4099, Train Acc: 81.53%, Test Acc: 81.51%\n", - "Epoch [94/150], Loss: 0.3786, Train Acc: 81.45%, Test Acc: 81.41%\n", - "Epoch [95/150], Loss: 0.3990, Train Acc: 81.50%, Test Acc: 81.46%\n", - "Epoch [96/150], Loss: 0.3947, Train Acc: 81.82%, Test Acc: 81.80%\n", - "Epoch [97/150], Loss: 0.4560, Train Acc: 81.66%, Test Acc: 81.65%\n", - "Epoch [98/150], Loss: 0.4143, Train Acc: 81.52%, Test Acc: 81.49%\n", - "Epoch [99/150], Loss: 0.3913, Train Acc: 81.49%, Test Acc: 81.45%\n", - "Epoch [100/150], Loss: 0.4339, Train Acc: 81.62%, Test Acc: 81.60%\n", - "Epoch [101/150], Loss: 0.3643, Train Acc: 81.69%, Test Acc: 81.67%\n", - "Epoch [102/150], Loss: 0.4149, Train Acc: 81.35%, Test Acc: 81.34%\n", - "Epoch [103/150], Loss: 0.3937, Train Acc: 81.80%, Test Acc: 81.75%\n", - "Epoch [104/150], Loss: 0.3828, Train Acc: 81.82%, Test Acc: 81.79%\n", - "Epoch [105/150], Loss: 0.4123, Train Acc: 81.40%, Test Acc: 81.36%\n", - "Epoch [106/150], Loss: 0.4303, Train Acc: 81.57%, Test Acc: 81.58%\n", - "Epoch [107/150], Loss: 0.3652, Train Acc: 81.72%, Test Acc: 81.68%\n", - "Epoch [108/150], Loss: 0.3674, Train Acc: 81.70%, Test Acc: 81.68%\n", - "Epoch [109/150], Loss: 0.3979, Train Acc: 81.79%, Test Acc: 81.77%\n", - "Epoch [110/150], Loss: 0.4147, Train Acc: 81.62%, Test Acc: 81.61%\n", - "Epoch [111/150], Loss: 0.3787, Train Acc: 81.60%, Test Acc: 81.58%\n", - "Epoch [112/150], Loss: 0.3848, Train Acc: 81.71%, Test Acc: 81.66%\n", - "Epoch [113/150], Loss: 0.3659, Train Acc: 81.42%, Test Acc: 81.39%\n", - "Epoch [114/150], Loss: 0.4076, Train Acc: 81.13%, Test Acc: 81.08%\n", - "Epoch [115/150], Loss: 0.3686, Train Acc: 81.85%, Test Acc: 81.83%\n", - "Epoch [116/150], Loss: 0.4147, Train Acc: 81.95%, Test Acc: 81.94%\n", - "Epoch [117/150], Loss: 0.4403, Train Acc: 81.38%, Test Acc: 81.34%\n", - "Epoch [118/150], Loss: 0.3832, Train Acc: 81.85%, Test Acc: 81.82%\n", - "Epoch [119/150], Loss: 0.3964, Train Acc: 81.76%, Test Acc: 81.73%\n", - "Epoch [120/150], Loss: 0.3853, Train Acc: 81.72%, Test Acc: 81.68%\n", - "Epoch [121/150], Loss: 0.4087, Train Acc: 81.64%, Test Acc: 81.62%\n", - "Epoch [122/150], Loss: 0.4079, Train Acc: 81.66%, Test Acc: 81.61%\n", - "Epoch [123/150], Loss: 0.4323, Train Acc: 82.03%, Test Acc: 82.01%\n", - "Epoch [124/150], Loss: 0.4344, Train Acc: 81.34%, Test Acc: 81.31%\n", - "Epoch [125/150], Loss: 0.4143, Train Acc: 81.93%, Test Acc: 81.91%\n", - "Epoch [126/150], Loss: 0.4122, Train Acc: 81.86%, Test Acc: 81.86%\n", - "Epoch [127/150], Loss: 0.4337, Train Acc: 81.82%, Test Acc: 81.77%\n", - "Epoch [128/150], Loss: 0.3852, Train Acc: 81.86%, Test Acc: 81.86%\n", - "Epoch [129/150], Loss: 0.3925, Train Acc: 82.03%, Test Acc: 82.01%\n", - "Epoch [130/150], Loss: 0.3500, Train Acc: 81.97%, Test Acc: 81.96%\n", - "Epoch [131/150], Loss: 0.4321, Train Acc: 81.59%, Test Acc: 81.52%\n", - "Epoch [132/150], Loss: 0.4256, Train Acc: 82.04%, Test Acc: 82.05%\n", - "Epoch [133/150], Loss: 0.3933, Train Acc: 81.84%, Test Acc: 81.85%\n", - "Epoch [134/150], Loss: 0.4046, Train Acc: 81.88%, Test Acc: 81.83%\n", - "Epoch [135/150], Loss: 0.3698, Train Acc: 81.98%, Test Acc: 81.99%\n", - "Epoch [136/150], Loss: 0.4106, Train Acc: 81.71%, Test Acc: 81.73%\n", - "Epoch [137/150], Loss: 0.4241, Train Acc: 82.09%, Test Acc: 82.08%\n", - "Epoch [138/150], Loss: 0.4078, Train Acc: 81.81%, Test Acc: 81.80%\n", - "Epoch [139/150], Loss: 0.4003, Train Acc: 82.00%, Test Acc: 82.00%\n", - "Epoch [140/150], Loss: 0.3874, Train Acc: 81.93%, Test Acc: 81.96%\n", - "Epoch [141/150], Loss: 0.4104, Train Acc: 82.12%, Test Acc: 82.12%\n", - "Epoch [142/150], Loss: 0.3818, Train Acc: 81.99%, Test Acc: 81.99%\n", - "Epoch [143/150], Loss: 0.3877, Train Acc: 81.86%, Test Acc: 81.83%\n", - "Epoch [144/150], Loss: 0.3628, Train Acc: 82.05%, Test Acc: 82.05%\n", - "Epoch [145/150], Loss: 0.4029, Train Acc: 81.88%, Test Acc: 81.88%\n", - "Epoch [146/150], Loss: 0.4075, Train Acc: 82.13%, Test Acc: 82.12%\n", - "Epoch [147/150], Loss: 0.3636, Train Acc: 81.97%, Test Acc: 81.97%\n", - "Epoch [148/150], Loss: 0.3964, Train Acc: 81.86%, Test Acc: 81.90%\n", - "Epoch [149/150], Loss: 0.3839, Train Acc: 81.60%, Test Acc: 81.59%\n", - "Epoch [150/150], Loss: 0.4315, Train Acc: 82.14%, Test Acc: 82.16%\n" + "Epoch [1/150], Loss: 0.4609, Train Acc: 78.77%, Test Acc: 78.70%\n", + "Epoch [2/150], Loss: 0.4638, Train Acc: 80.15%, Test Acc: 80.10%\n", + "Epoch [3/150], Loss: 0.4109, Train Acc: 79.89%, Test Acc: 79.82%\n", + "Epoch [4/150], Loss: 0.4491, Train Acc: 80.81%, Test Acc: 80.77%\n", + "Epoch [5/150], Loss: 0.4077, Train Acc: 80.56%, Test Acc: 80.51%\n", + "Epoch [6/150], Loss: 0.4371, Train Acc: 80.78%, Test Acc: 80.71%\n", + "Epoch [7/150], Loss: 0.4530, Train Acc: 80.66%, Test Acc: 80.62%\n", + "Epoch [8/150], Loss: 0.4417, Train Acc: 81.10%, Test Acc: 81.04%\n", + "Epoch [9/150], Loss: 0.4234, Train Acc: 81.24%, Test Acc: 81.18%\n", + "Epoch [10/150], Loss: 0.4624, Train Acc: 80.44%, Test Acc: 80.35%\n", + "Epoch [11/150], Loss: 0.4066, Train Acc: 81.28%, Test Acc: 81.27%\n", + "Epoch [12/150], Loss: 0.4193, Train Acc: 80.90%, Test Acc: 80.86%\n", + "Epoch [13/150], Loss: 0.4167, Train Acc: 81.50%, Test Acc: 81.43%\n", + "Epoch [14/150], Loss: 0.4359, Train Acc: 81.04%, Test Acc: 80.97%\n", + "Epoch [15/150], Loss: 0.4016, Train Acc: 81.01%, Test Acc: 80.95%\n", + "Epoch [16/150], Loss: 0.4177, Train Acc: 81.62%, Test Acc: 81.54%\n", + "Epoch [17/150], Loss: 0.3949, Train Acc: 81.60%, Test Acc: 81.52%\n", + "Epoch [18/150], Loss: 0.4142, Train Acc: 82.01%, Test Acc: 81.94%\n", + "Epoch [19/150], Loss: 0.3825, Train Acc: 81.84%, Test Acc: 81.75%\n", + "Epoch [20/150], Loss: 0.4074, Train Acc: 81.67%, Test Acc: 81.57%\n", + "Epoch [21/150], Loss: 0.4219, Train Acc: 82.21%, Test Acc: 82.16%\n", + "Epoch [22/150], Loss: 0.4048, Train Acc: 81.59%, Test Acc: 81.49%\n", + "Epoch [23/150], Loss: 0.3645, Train Acc: 82.14%, Test Acc: 82.06%\n", + "Epoch [24/150], Loss: 0.4075, Train Acc: 81.75%, Test Acc: 81.66%\n", + "Epoch [25/150], Loss: 0.4366, Train Acc: 81.36%, Test Acc: 81.30%\n", + "Epoch [26/150], Loss: 0.3583, Train Acc: 81.86%, Test Acc: 81.83%\n", + "Epoch [27/150], Loss: 0.3760, Train Acc: 81.96%, Test Acc: 81.88%\n", + "Epoch [28/150], Loss: 0.4366, Train Acc: 82.03%, Test Acc: 81.97%\n", + "Epoch [29/150], Loss: 0.4057, Train Acc: 81.91%, Test Acc: 81.84%\n", + "Epoch [30/150], Loss: 0.4379, Train Acc: 82.04%, Test Acc: 82.01%\n", + "Epoch [31/150], Loss: 0.3724, Train Acc: 82.01%, Test Acc: 81.92%\n", + "Epoch [32/150], Loss: 0.4205, Train Acc: 81.90%, Test Acc: 81.81%\n", + "Epoch [33/150], Loss: 0.3962, Train Acc: 82.47%, Test Acc: 82.44%\n", + "Epoch [34/150], Loss: 0.4007, Train Acc: 82.35%, Test Acc: 82.30%\n", + "Epoch [35/150], Loss: 0.4094, Train Acc: 81.86%, Test Acc: 81.76%\n", + "Epoch [36/150], Loss: 0.3967, Train Acc: 81.83%, Test Acc: 81.74%\n", + "Epoch [37/150], Loss: 0.3890, Train Acc: 82.21%, Test Acc: 82.14%\n", + "Epoch [38/150], Loss: 0.4021, Train Acc: 81.78%, Test Acc: 81.70%\n", + "Epoch [39/150], Loss: 0.4284, Train Acc: 82.60%, Test Acc: 82.54%\n", + "Epoch [40/150], Loss: 0.3572, Train Acc: 82.34%, Test Acc: 82.28%\n", + "Epoch [41/150], Loss: 0.3846, Train Acc: 82.13%, Test Acc: 82.08%\n", + "Epoch [42/150], Loss: 0.3969, Train Acc: 82.17%, Test Acc: 82.12%\n", + "Epoch [43/150], Loss: 0.3746, Train Acc: 82.38%, Test Acc: 82.31%\n", + "Epoch [44/150], Loss: 0.4016, Train Acc: 82.30%, Test Acc: 82.23%\n", + "Epoch [45/150], Loss: 0.4039, Train Acc: 82.18%, Test Acc: 82.10%\n", + "Epoch [46/150], Loss: 0.4134, Train Acc: 82.48%, Test Acc: 82.39%\n", + "Epoch [47/150], Loss: 0.3769, Train Acc: 82.23%, Test Acc: 82.17%\n", + "Epoch [48/150], Loss: 0.3601, Train Acc: 82.08%, Test Acc: 82.04%\n", + "Epoch [49/150], Loss: 0.3920, Train Acc: 82.64%, Test Acc: 82.57%\n", + "Epoch [50/150], Loss: 0.3986, Train Acc: 82.29%, Test Acc: 82.21%\n", + "Epoch [51/150], Loss: 0.3392, Train Acc: 82.21%, Test Acc: 82.13%\n", + "Epoch [52/150], Loss: 0.3751, Train Acc: 82.32%, Test Acc: 82.27%\n", + "Epoch [53/150], Loss: 0.3851, Train Acc: 82.36%, Test Acc: 82.27%\n", + "Epoch [54/150], Loss: 0.4385, Train Acc: 82.45%, Test Acc: 82.42%\n", + "Epoch [55/150], Loss: 0.4022, Train Acc: 81.95%, Test Acc: 81.89%\n", + "Epoch [56/150], Loss: 0.3842, Train Acc: 82.27%, Test Acc: 82.22%\n", + "Epoch [57/150], Loss: 0.4275, Train Acc: 82.49%, Test Acc: 82.44%\n", + "Epoch [58/150], Loss: 0.3801, Train Acc: 82.06%, Test Acc: 81.97%\n", + "Epoch [59/150], Loss: 0.3462, Train Acc: 82.27%, Test Acc: 82.21%\n", + "Epoch [60/150], Loss: 0.4013, Train Acc: 82.64%, Test Acc: 82.61%\n", + "Epoch [61/150], Loss: 0.4001, Train Acc: 82.44%, Test Acc: 82.41%\n", + "Epoch [62/150], Loss: 0.3613, Train Acc: 81.96%, Test Acc: 81.89%\n", + "Epoch [63/150], Loss: 0.3574, Train Acc: 82.53%, Test Acc: 82.51%\n", + "Epoch [64/150], Loss: 0.3836, Train Acc: 82.54%, Test Acc: 82.50%\n", + "Epoch [65/150], Loss: 0.3536, Train Acc: 82.43%, Test Acc: 82.33%\n", + "Epoch [66/150], Loss: 0.3505, Train Acc: 82.02%, Test Acc: 81.95%\n", + "Epoch [67/150], Loss: 0.3949, Train Acc: 82.27%, Test Acc: 82.17%\n", + "Epoch [68/150], Loss: 0.3773, Train Acc: 82.14%, Test Acc: 82.04%\n", + "Epoch [69/150], Loss: 0.3779, Train Acc: 82.50%, Test Acc: 82.43%\n", + "Epoch [70/150], Loss: 0.3770, Train Acc: 82.68%, Test Acc: 82.63%\n", + "Epoch [71/150], Loss: 0.3636, Train Acc: 82.00%, Test Acc: 81.92%\n", + "Epoch [72/150], Loss: 0.4204, Train Acc: 82.48%, Test Acc: 82.44%\n", + "Epoch [73/150], Loss: 0.4016, Train Acc: 82.59%, Test Acc: 82.54%\n", + "Epoch [74/150], Loss: 0.3913, Train Acc: 82.59%, Test Acc: 82.55%\n", + "Epoch [75/150], Loss: 0.4155, Train Acc: 82.30%, Test Acc: 82.27%\n", + "Epoch [76/150], Loss: 0.3763, Train Acc: 82.71%, Test Acc: 82.63%\n", + "Epoch [77/150], Loss: 0.3292, Train Acc: 82.66%, Test Acc: 82.58%\n", + "Epoch [78/150], Loss: 0.3347, Train Acc: 82.80%, Test Acc: 82.75%\n", + "Epoch [79/150], Loss: 0.4015, Train Acc: 82.70%, Test Acc: 82.63%\n", + "Epoch [80/150], Loss: 0.3620, Train Acc: 82.27%, Test Acc: 82.18%\n", + "Epoch [81/150], Loss: 0.4036, Train Acc: 82.37%, Test Acc: 82.31%\n", + "Epoch [82/150], Loss: 0.3943, Train Acc: 82.39%, Test Acc: 82.34%\n", + "Epoch [83/150], Loss: 0.3865, Train Acc: 82.44%, Test Acc: 82.33%\n", + "Epoch [84/150], Loss: 0.3443, Train Acc: 82.74%, Test Acc: 82.68%\n", + "Epoch [85/150], Loss: 0.3789, Train Acc: 82.69%, Test Acc: 82.61%\n", + "Epoch [86/150], Loss: 0.4131, Train Acc: 82.77%, Test Acc: 82.69%\n", + "Epoch [87/150], Loss: 0.3869, Train Acc: 82.75%, Test Acc: 82.67%\n", + "Epoch [88/150], Loss: 0.3644, Train Acc: 82.48%, Test Acc: 82.39%\n", + "Epoch [89/150], Loss: 0.3926, Train Acc: 82.75%, Test Acc: 82.69%\n", + "Epoch [90/150], Loss: 0.3976, Train Acc: 82.49%, Test Acc: 82.45%\n", + "Epoch [91/150], Loss: 0.4135, Train Acc: 82.59%, Test Acc: 82.52%\n", + "Epoch [92/150], Loss: 0.3668, Train Acc: 82.64%, Test Acc: 82.55%\n", + "Epoch [93/150], Loss: 0.4164, Train Acc: 82.68%, Test Acc: 82.65%\n", + "Epoch [94/150], Loss: 0.3904, Train Acc: 82.87%, Test Acc: 82.77%\n", + "Epoch [95/150], Loss: 0.3771, Train Acc: 82.73%, Test Acc: 82.64%\n", + "Epoch [96/150], Loss: 0.4441, Train Acc: 82.48%, Test Acc: 82.42%\n", + "Epoch [97/150], Loss: 0.3591, Train Acc: 82.54%, Test Acc: 82.52%\n", + "Epoch [98/150], Loss: 0.4293, Train Acc: 82.51%, Test Acc: 82.45%\n", + "Epoch [99/150], Loss: 0.3646, Train Acc: 82.43%, Test Acc: 82.38%\n", + "Epoch [100/150], Loss: 0.3396, Train Acc: 82.42%, Test Acc: 82.36%\n", + "Epoch [101/150], Loss: 0.3455, Train Acc: 82.58%, Test Acc: 82.54%\n", + "Epoch [102/150], Loss: 0.3919, Train Acc: 82.47%, Test Acc: 82.43%\n", + "Epoch [103/150], Loss: 0.4062, Train Acc: 82.58%, Test Acc: 82.54%\n", + "Epoch [104/150], Loss: 0.3706, Train Acc: 82.84%, Test Acc: 82.76%\n", + "Epoch [105/150], Loss: 0.3815, Train Acc: 82.33%, Test Acc: 82.29%\n", + "Epoch [106/150], Loss: 0.3800, Train Acc: 82.88%, Test Acc: 82.83%\n", + "Epoch [107/150], Loss: 0.4236, Train Acc: 82.46%, Test Acc: 82.39%\n", + "Epoch [108/150], Loss: 0.3818, Train Acc: 82.77%, Test Acc: 82.73%\n", + "Epoch [109/150], Loss: 0.3373, Train Acc: 82.34%, Test Acc: 82.28%\n", + "Epoch [110/150], Loss: 0.4092, Train Acc: 82.41%, Test Acc: 82.36%\n", + "Epoch [111/150], Loss: 0.4080, Train Acc: 82.78%, Test Acc: 82.74%\n", + "Epoch [112/150], Loss: 0.4181, Train Acc: 82.42%, Test Acc: 82.36%\n", + "Epoch [113/150], Loss: 0.3976, Train Acc: 82.17%, Test Acc: 82.12%\n", + "Epoch [114/150], Loss: 0.3875, Train Acc: 82.83%, Test Acc: 82.78%\n", + "Epoch [115/150], Loss: 0.3707, Train Acc: 82.48%, Test Acc: 82.41%\n", + "Epoch [116/150], Loss: 0.3774, Train Acc: 82.76%, Test Acc: 82.68%\n", + "Epoch [117/150], Loss: 0.3867, Train Acc: 82.85%, Test Acc: 82.79%\n", + "Epoch [118/150], Loss: 0.3681, Train Acc: 82.85%, Test Acc: 82.80%\n", + "Epoch [119/150], Loss: 0.3784, Train Acc: 82.78%, Test Acc: 82.70%\n", + "Epoch [120/150], Loss: 0.3875, Train Acc: 82.87%, Test Acc: 82.79%\n", + "Epoch [121/150], Loss: 0.4150, Train Acc: 82.82%, Test Acc: 82.76%\n", + "Epoch [122/150], Loss: 0.3873, Train Acc: 82.76%, Test Acc: 82.69%\n", + "Epoch [123/150], Loss: 0.4046, Train Acc: 82.65%, Test Acc: 82.58%\n", + "Epoch [124/150], Loss: 0.3514, Train Acc: 82.58%, Test Acc: 82.50%\n", + "Epoch [125/150], Loss: 0.3978, Train Acc: 82.77%, Test Acc: 82.71%\n", + "Epoch [126/150], Loss: 0.3839, Train Acc: 82.45%, Test Acc: 82.41%\n", + "Epoch [127/150], Loss: 0.4088, Train Acc: 82.80%, Test Acc: 82.77%\n", + "Epoch [128/150], Loss: 0.3523, Train Acc: 82.81%, Test Acc: 82.73%\n", + "Epoch [129/150], Loss: 0.3857, Train Acc: 82.86%, Test Acc: 82.78%\n", + "Epoch [130/150], Loss: 0.4131, Train Acc: 82.10%, Test Acc: 82.04%\n", + "Epoch [131/150], Loss: 0.3502, Train Acc: 82.87%, Test Acc: 82.81%\n", + "Epoch [132/150], Loss: 0.3692, Train Acc: 82.53%, Test Acc: 82.46%\n", + "Epoch [133/150], Loss: 0.3682, Train Acc: 82.58%, Test Acc: 82.50%\n", + "Epoch [134/150], Loss: 0.4157, Train Acc: 82.94%, Test Acc: 82.83%\n", + "Epoch [135/150], Loss: 0.3399, Train Acc: 82.89%, Test Acc: 82.83%\n", + "Epoch [136/150], Loss: 0.3861, Train Acc: 82.86%, Test Acc: 82.77%\n", + "Epoch [137/150], Loss: 0.3815, Train Acc: 82.90%, Test Acc: 82.83%\n", + "Epoch [138/150], Loss: 0.3995, Train Acc: 82.47%, Test Acc: 82.41%\n", + "Epoch [139/150], Loss: 0.3952, Train Acc: 82.55%, Test Acc: 82.47%\n", + "Epoch [140/150], Loss: 0.3669, Train Acc: 82.88%, Test Acc: 82.83%\n", + "Epoch [141/150], Loss: 0.4388, Train Acc: 82.35%, Test Acc: 82.26%\n", + "Epoch [142/150], Loss: 0.3819, Train Acc: 82.58%, Test Acc: 82.55%\n", + "Epoch [143/150], Loss: 0.3356, Train Acc: 82.87%, Test Acc: 82.79%\n", + "Epoch [144/150], Loss: 0.3640, Train Acc: 82.72%, Test Acc: 82.66%\n", + "Epoch [145/150], Loss: 0.4053, Train Acc: 82.43%, Test Acc: 82.33%\n", + "Epoch [146/150], Loss: 0.3777, Train Acc: 82.88%, Test Acc: 82.80%\n", + "Epoch [147/150], Loss: 0.3683, Train Acc: 82.71%, Test Acc: 82.67%\n", + "Epoch [148/150], Loss: 0.3458, Train Acc: 82.92%, Test Acc: 82.85%\n", + "Epoch [149/150], Loss: 0.3482, Train Acc: 82.73%, Test Acc: 82.68%\n", + "Epoch [150/150], Loss: 0.3782, Train Acc: 82.97%, Test Acc: 82.91%\n" ] } ], @@ -451,32 +446,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Baseline accuracy: 0.8040592670440674\n", + "Baseline accuracy: 0.8132245540618896\n", "Feature importances:\n", - "Feature 8 importance: 0.5686\n", - "Feature 16 importance: 0.5662\n", - "Feature 0 importance: 0.5589\n", - "Feature 4 importance: 0.5551\n", - "Feature 20 importance: 0.5023\n", - "Feature 22 importance: 0.4204\n", - "Feature 21 importance: 0.3989\n", - "Feature 17 importance: 0.3953\n", - "Feature 12 importance: 0.3569\n", - "Feature 9 importance: 0.3118\n", - "Feature 23 importance: 0.3025\n", - "Feature 13 importance: 0.2205\n", - "Feature 1 importance: 0.2043\n", - "Feature 18 importance: 0.1858\n", - "Feature 15 importance: 0.1400\n", - "Feature 11 importance: 0.1197\n", - "Feature 5 importance: 0.1128\n", - "Feature 19 importance: 0.1040\n", - "Feature 10 importance: 0.0606\n", - "Feature 7 importance: 0.0495\n", - "Feature 14 importance: 0.0409\n", - "Feature 2 importance: 0.0270\n", - "Feature 3 importance: 0.0135\n", - "Feature 6 importance: 0.0101\n" + "Feature 6 importance: 0.5653\n", + "Feature 12 importance: 0.5472\n", + "Feature 0 importance: 0.5410\n", + "Feature 9 importance: 0.5238\n", + "Feature 7 importance: 0.4906\n", + "Feature 10 importance: 0.4857\n", + "Feature 3 importance: 0.4806\n", + "Feature 13 importance: 0.4411\n", + "Feature 16 importance: 0.4187\n", + "Feature 15 importance: 0.3488\n", + "Feature 14 importance: 0.3469\n", + "Feature 4 importance: 0.3385\n", + "Feature 11 importance: 0.3373\n", + "Feature 17 importance: 0.3256\n", + "Feature 8 importance: 0.3130\n", + "Feature 1 importance: 0.2848\n", + "Feature 2 importance: 0.0795\n", + "Feature 5 importance: 0.0605\n" ] } ], @@ -530,13 +519,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_921783/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "/tmp/ipykernel_1570202/52354147.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", " inputs = torch.tensor(features, dtype=torch.float32).to(device)\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -581,144 +570,109 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8199998933833283, 0.1796430106165911, 0.44098014)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Target TPR value\n", - "target_tpr = 0.82\n", - "\n", - "# Find the closest TPR value to the target and its corresponding FPR and threshold\n", - "closest_index = np.argmin(np.abs(tpr - target_tpr))\n", - "closest_tpr = tpr[closest_index]\n", - "closest_fpr = fpr[closest_index]\n", - "closest_threshold = thresholds[closest_index]\n", - "\n", - "closest_tpr, closest_fpr, closest_threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_0[32] = {\n", - "0.0838128f, -0.0951467f, -0.4538043f, -0.1200482f, 0.1697024f, 0.0734245f, 0.0477040f, -0.1409838f, -0.1598700f, 0.1343284f, 0.7158028f, 0.5663804f, -0.0202696f, -0.0769930f, -0.1565632f, -0.4897312f, -0.1904009f, 0.4263237f, 0.1309257f, 0.1497203f, 0.1124899f, 0.3384814f, -0.2008730f, 0.2663862f, -0.0109372f, -0.2767799f, 0.1633506f, 0.6183366f, 2.0648596f, -0.0196532f, -0.1557027f, -0.3387919f };\n", + "-0.1940323f, 0.1562515f, -0.4598289f, -0.0067528f, -0.1187210f, -1.2986372f, -0.7766901f, 0.0709069f, -0.0413468f, -0.1649964f, -0.0411712f, -0.0118713f, -0.3321564f, 1.0123911f, -0.0118469f, -0.0948771f, -0.3071513f, -0.0202074f, -0.0227748f, 2.0413475f, -0.9568492f, -0.7617306f, -0.8173049f, 0.0168511f, 2.0595605f, -2.6405857f, -0.0102134f, 0.3227372f, 0.0028400f, 0.6402014f, -0.2776071f, 0.1328146f };\n", "\n", - "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[24][32] = {\n", - "{ -0.0482477f, 0.0348692f, -0.6279378f, 0.2042783f, -0.0943811f, -0.0019772f, 1.1357815f, 0.1307896f, 0.1233541f, -0.0307808f, 0.4762143f, 2.3105178f, -0.1138477f, -0.1789541f, 0.0728833f, 0.0368915f, 0.1061521f, -0.0356611f, -0.0947667f, -1.7470065f, -0.0434104f, 0.3295992f, -0.6358198f, -0.2336755f, 1.8262464f, 2.1567848f, -0.4115292f, 0.0250860f, -1.4501210f, -0.5921007f, 0.0049636f, 0.1055770f },\n", - "{ -0.0124859f, -0.1331200f, 0.7014464f, -0.1568218f, 0.0331185f, -0.1542812f, -0.1333411f, 0.0127694f, 0.2013001f, 0.0947020f, -1.4170154f, -0.1189228f, -0.0019860f, 0.1502178f, 0.1740465f, 0.0477101f, 0.1527060f, -1.3259488f, -0.1455328f, 0.1777444f, 0.1768609f, -0.1144335f, 1.7819276f, -2.2551980f, 1.8525611f, 1.0002149f, 0.7666884f, 0.5857834f, 0.0189218f, 0.0130888f, 0.0520591f, 0.1602217f },\n", - "{ 0.0089779f, -0.0046051f, 5.9403129f, -0.0760328f, 0.0868518f, -0.1175489f, 2.4591918f, -0.2272298f, -0.0336123f, -0.1102009f, 0.4388122f, -0.1027233f, 0.1439395f, 0.0260359f, -0.1548838f, 0.5144961f, -0.0036697f, -0.0587723f, 0.1452203f, -0.7863321f, -0.0143598f, -0.7969503f, -1.4715855f, 1.8720422f, -0.5365977f, 1.2278979f, 0.1856042f, 0.0087406f, -0.4033080f, 0.5311748f, -0.0624044f, -0.0473539f },\n", - "{ 0.0122028f, -0.2384637f, -0.0065890f, -0.1581197f, -0.2024812f, -0.0350186f, -0.1173539f, -0.2257327f, -0.1062854f, -0.2007362f, -0.0033018f, 0.0028686f, 0.0887160f, -0.0825604f, -0.1360626f, 0.0014523f, -0.1611109f, 0.0363360f, -0.1258196f, 0.0118614f, -0.1436693f, 0.0181161f, -0.0238848f, -0.0896234f, -0.0039286f, -0.0287253f, -0.0076090f, -0.0047007f, 0.3169636f, -0.1194062f, 0.0768581f, -0.1023149f },\n", - "{ 0.0223803f, -0.1106429f, 0.5197684f, 0.0797990f, -0.0985189f, -0.0219536f, -0.8999558f, 0.0139441f, -0.1065298f, -0.0700749f, -0.2372502f, -2.3253367f, -0.1771734f, -0.1391621f, -0.0327205f, 0.0384778f, 0.3209504f, 0.0964711f, -0.1038988f, 2.0001328f, 0.0278964f, -0.6402076f, 0.1002734f, 0.9442523f, -2.3371346f, -1.3519518f, 0.5144972f, -0.1425193f, 1.6520097f, 0.2593979f, -0.1068970f, -0.0010823f },\n", - "{ -0.0564048f, 0.0277004f, 0.4086671f, -0.1428425f, -0.1345516f, -0.0642777f, -1.4967046f, -0.0728123f, 0.1499694f, -0.0660839f, 1.1170599f, -0.0109156f, -0.0217163f, -0.1839852f, -0.0723716f, -0.2208258f, -2.1450739f, -0.7447581f, 0.2110604f, -0.6763240f, -0.0871866f, 0.1980208f, -0.3258589f, 0.3583415f, -1.5599525f, -0.9544728f, 0.3547978f, 0.2802322f, -0.3988611f, 1.6168940f, 0.1046868f, -0.0736501f },\n", - "{ 0.0401295f, 0.0301625f, 2.3904793f, 0.0874403f, -0.1706232f, 0.0243270f, 0.2055506f, 0.0659292f, 0.0279800f, -0.1013176f, -0.4468603f, -0.0820311f, 0.1084237f, -0.0980428f, 0.0153869f, 0.4169671f, 1.1679637f, -0.7452526f, 0.0690438f, -0.9760146f, -0.0150821f, -0.1838745f, 0.0022138f, 0.4647771f, 0.6666930f, -0.5373036f, -0.4382644f, 0.1287971f, 0.4949077f, -0.2705404f, -0.0644356f, -0.1727526f },\n", - "{ 0.1045402f, -0.0420851f, -0.0156088f, 0.1043605f, -0.0468452f, -0.1996713f, -0.1536377f, -0.0548137f, 0.0782699f, -0.1749692f, 0.0110849f, -0.0338493f, -0.0110409f, -0.1772420f, 0.1318522f, 0.0084175f, -0.2084620f, 0.3595731f, -0.2131062f, -0.0738994f, -0.1284403f, 0.0381397f, 0.0738881f, -0.0387961f, 0.0070784f, -0.0201305f, 0.0226532f, -0.0150129f, 0.1880418f, -0.1988496f, 0.0518940f, -0.0426258f },\n", - "{ 0.0555184f, -0.1434738f, 1.5241326f, -0.1319865f, -0.1433413f, 0.0522304f, -1.4563835f, 0.0005232f, 0.1506346f, -0.0265684f, -1.2287844f, -3.0413165f, 0.0025170f, 0.0599654f, -0.1306973f, 0.0784461f, 0.5079768f, -0.0244722f, -0.1015498f, 2.7846479f, -0.0642611f, -0.7250540f, 0.8876019f, 1.4510521f, -2.9753745f, -3.3278594f, 0.8736179f, -0.0000811f, 2.6898973f, 0.4387932f, -0.0675147f, 0.1717749f },\n", - "{ -0.2273859f, -0.0783074f, 0.1170714f, 0.1458802f, 0.1328210f, 0.0364441f, 1.4164704f, 0.1079648f, 0.1553876f, 0.0617190f, -0.7688578f, -0.5899147f, -0.0151878f, 0.0300152f, 0.1985418f, 0.1041053f, 0.2805509f, -0.3530872f, 0.1957988f, 0.0688579f, 0.1145971f, -0.1384836f, 0.7624677f, -0.7887678f, -1.1695358f, -2.7840865f, -0.0668691f, 0.1524266f, 0.9591433f, -0.9727835f, 0.1578966f, 0.1385039f },\n", - "{ -0.0925403f, 0.1268597f, 1.9899433f, -0.0697888f, -0.1969929f, -0.0471073f, -5.3116655f, -0.0091008f, -0.1728659f, -0.1753619f, -0.1478174f, 0.2951335f, 0.1457811f, -0.0851820f, 0.1457849f, 0.4183387f, -2.3975048f, -0.4645267f, 0.0937851f, -0.3560269f, -0.1869052f, 0.0136887f, 0.5827022f, 1.1965749f, 1.5047479f, -1.9283267f, 0.0473070f, 0.0353213f, 0.4265198f, -3.5288622f, 0.1322581f, -0.5738994f },\n", - "{ -0.1227338f, 0.0047513f, -0.1062419f, -0.1901390f, 0.0837805f, -0.1380210f, 0.0310710f, -0.2166184f, -0.0498982f, -0.0786184f, 0.0268683f, -0.1294491f, 0.0897407f, 0.0206377f, -0.1643501f, -0.1812480f, -0.2876152f, -0.0682887f, 0.0446657f, -0.1231280f, -0.0275647f, -0.1300283f, -0.1491375f, 0.0015875f, 0.0132433f, -0.0549486f, -0.2495581f, 0.2349717f, -0.1369720f, -0.3496855f, -0.0560258f, 0.1234621f },\n", - "{ -0.1468480f, 0.1923417f, -0.4420354f, -0.0975916f, 0.0984100f, 0.0142761f, -1.8042085f, 0.0177031f, 0.0466983f, 0.1904279f, 0.2313117f, 0.8943673f, -0.1027022f, -0.0123586f, -0.0259216f, 0.0260046f, 0.1497788f, 0.2727981f, 0.1562366f, -0.3712008f, 0.0626981f, 0.2122544f, 0.3697388f, -0.2779360f, -0.3196765f, -1.3450428f, 0.3793195f, -0.0348413f, -0.1375810f, 0.7617420f, -0.0031145f, 0.1355706f },\n", - "{ 0.0679835f, 0.1254937f, -0.5262930f, 0.1686825f, 0.2174495f, -0.0218732f, -0.1299452f, 0.0997381f, 0.0435618f, 0.1211559f, -1.1586866f, -0.2731386f, 0.0748916f, -0.1903290f, 0.0807933f, 0.1435141f, -1.4356579f, 0.4171664f, 0.0434130f, -0.2107047f, 0.0788040f, -0.2853448f, 0.8026017f, -1.3761326f, 1.2578323f, -1.3311355f, -0.3247376f, -0.1237198f, 1.3546734f, 1.1058371f, 0.1375258f, 0.0693791f },\n", - "{ 0.1654906f, -0.1866392f, -2.5744381f, 0.1619695f, 0.0060606f, -0.0710510f, -4.9512372f, 0.1363884f, 0.0324911f, 0.0960657f, 0.0878271f, -0.0495669f, 0.0635484f, -0.2014371f, -0.1956824f, -0.2231105f, -0.5065056f, -0.2034401f, -0.1735438f, 0.4712444f, 0.0451609f, 0.2407379f, 1.5705422f, 0.8732644f, 0.5805451f, -0.9020337f, 0.1111905f, 0.0054292f, 2.0555997f, -3.0719264f, -0.1595502f, -0.3060012f },\n", - "{ 0.0130320f, -0.1412935f, 0.1939399f, 0.0957547f, -0.1142095f, -0.1203167f, 0.1188142f, 0.0639749f, -0.1311414f, -0.1461854f, -0.0772305f, -0.1230349f, -0.1671150f, -0.1981301f, -0.0226475f, -0.1619669f, 0.1555634f, -0.2390742f, -0.1453369f, -0.2721513f, -0.1140533f, -0.0536937f, 0.1709613f, -0.6350394f, -0.0099144f, -0.0350223f, 0.2447530f, 0.0993788f, -0.5841039f, 0.1384276f, -0.1107760f, -0.1433515f },\n", - "{ -0.0276203f, 0.0424715f, 0.4400442f, -0.1873313f, 0.1983742f, 0.0829819f, 0.6405745f, -0.1593508f, 0.1076834f, 0.1900531f, 1.4096446f, 0.2769921f, -0.1515984f, 0.0578545f, -0.0726278f, -0.0870836f, -3.2511303f, -0.7908387f, -0.0769899f, -1.2463226f, -0.0420534f, 1.5724318f, -0.8705097f, -2.5861135f, 3.8912401f, 4.0461211f, -1.5900346f, 0.3250549f, -2.2681830f, 2.1342385f, -0.1412408f, 0.0973785f },\n", - "{ -0.0631394f, 0.0809900f, -0.8641825f, 0.1149525f, -0.0955641f, -0.0403090f, -0.6845544f, -0.1285507f, -0.0701147f, 0.1852410f, 0.5936438f, 0.1158317f, -0.0838372f, -0.0165345f, -0.0889874f, -0.0779972f, -2.0281746f, 1.2746787f, 0.0807270f, -0.1737853f, -0.1318350f, 0.2905773f, -0.4960580f, 0.2812797f, 0.0395625f, 2.4000103f, -0.3797377f, -0.5155146f, -1.6649362f, 2.7477472f, 0.1732312f, 0.1769285f },\n", - "{ -0.0839652f, 0.1048384f, -2.6453409f, -0.1549998f, 0.0332292f, 0.1805567f, 1.5299331f, 0.1614436f, 0.1030123f, -0.1750580f, -0.0790422f, 0.1303435f, -0.0649011f, -0.1773880f, -0.0186087f, -0.4794982f, -6.5970545f, 0.9689565f, -0.0698152f, 0.2821720f, -0.0943349f, 0.6448916f, -0.5019226f, -2.0137179f, -1.9753901f, 2.3883131f, -0.6336402f, -0.2053137f, -0.1029351f, -3.6074843f, -0.0803197f, -0.3316223f },\n", - "{ -0.1005511f, -0.0575567f, -0.2696874f, -0.1879897f, -0.1549900f, -0.1585334f, -0.2431465f, 0.0235179f, -0.0953478f, -0.0292804f, -0.0737122f, 0.1184362f, -0.1744826f, -0.1343563f, -0.1822797f, 0.3344489f, 0.1855060f, -0.0245743f, 0.0700775f, 0.3525808f, -0.1741875f, 0.0696346f, 0.0178112f, 0.5294384f, -0.0518728f, 0.0456072f, -0.0179709f, -0.3133727f, -0.0621307f, 0.0000746f, -0.1714160f, 0.0660782f },\n", - "{ 0.1368161f, 0.1452735f, -1.0996736f, -0.1370980f, 0.0454142f, -0.2010279f, -0.3923539f, 0.0751393f, 0.1552261f, -0.1690396f, -0.7361290f, 2.2066221f, 0.1860844f, -0.0471949f, -0.1001091f, -0.0785898f, 0.3381744f, 0.2242116f, 0.0059051f, -1.1038984f, -0.0776049f, -0.9301085f, -0.2579322f, 1.0752188f, 0.0572336f, -0.4376161f, 0.4391319f, -0.0920684f, -0.3661392f, -0.0926421f, 0.1151504f, 0.0568435f },\n", - "{ 0.1527036f, 0.0719917f, 1.6668177f, 0.1541860f, 0.1094950f, -0.1874002f, 0.2204740f, 0.0663509f, 0.0846452f, 0.0338055f, 2.6957841f, 2.2497787f, -0.1595391f, 0.1440347f, -0.0457316f, 3.2653592f, 0.3555845f, 0.0221110f, 0.1668054f, 1.9366297f, 0.0111978f, -3.0021029f, 2.4726274f, -1.7558607f, -1.1594486f, 0.0734959f, -2.3178720f, -0.0162731f, 1.3418909f, 0.5486869f, -0.1103047f, -0.0437041f },\n", - "{ 0.0302621f, -0.2280054f, -1.1536891f, 0.0624857f, -0.1923327f, 0.0903575f, 2.9342484f, -0.1453349f, -0.1462534f, 0.1626090f, -2.3898613f, -2.0969083f, 0.1033970f, -0.0779580f, 0.1544486f, 0.0079555f, -0.4402241f, 2.7067254f, -0.0814941f, -2.0344024f, -0.1009802f, 2.3716676f, -2.8162944f, 1.7359691f, 1.1204399f, -0.0530971f, 3.1334810f, 2.6716938f, -1.5494148f, -0.1560390f, 0.1592183f, -0.5042306f },\n", - "{ -0.1881559f, 0.1079059f, 0.8104222f, 0.0100862f, 0.0803289f, -0.1001732f, -3.0945404f, -0.1863656f, 0.1092684f, -0.0122846f, 0.0211765f, 0.0882070f, 0.0887852f, 0.1788233f, 0.1035344f, -3.2046664f, 0.1207470f, -2.7201638f, -0.1267492f, 0.3226216f, 0.0849743f, 0.7428753f, 0.0196231f, 0.0904721f, 0.3495182f, -0.2576671f, -0.4849421f, -2.6337876f, 0.4418600f, -0.0151254f, 0.1613163f, -0.0374872f },\n", + "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_0[18][32] = {\n", + "{ 0.0452029f, -0.2047395f, -0.2531095f, 0.8308914f, 0.1026031f, -0.5966103f, 1.4759157f, -0.0898649f, -0.1962480f, -0.1148645f, 0.1196134f, 0.0548274f, -0.1407367f, -1.4861078f, -0.0349070f, -1.1813899f, -0.2423109f, 0.0454410f, -0.0287287f, -0.0014218f, 0.2253178f, -1.6985301f, 3.8701379f, -0.9332715f, -1.6066414f, 0.7657530f, -0.1616500f, 3.7211533f, -0.4365099f, -0.5467676f, 0.0612534f, -0.1052311f },\n", + "{ -0.2059304f, 0.0514157f, 1.5633677f, 0.1810661f, -0.0710148f, -1.3003422f, -0.0254397f, -0.2410158f, -0.2113242f, -0.2738193f, 0.0433658f, -0.0664363f, -0.7619049f, -0.6734435f, -0.2305467f, 0.7696750f, -0.3101756f, -0.0359285f, 0.1335582f, -1.5694221f, 0.9542036f, -0.6244334f, 2.4346864f, -0.7677089f, -1.1617776f, -3.1731126f, -0.0121344f, 0.9073577f, -0.2181434f, -0.3400409f, -0.1639360f, -0.1658078f },\n", + "{ 0.1472459f, -0.0126969f, 3.1386833f, 0.4109284f, 0.1190476f, 0.3891498f, -0.1224196f, -0.0641578f, 0.0702143f, -0.1379193f, -0.1345438f, 0.0194056f, 0.1905062f, 2.0310915f, -0.1589341f, 0.7496570f, -0.1105359f, -0.0770280f, 0.0943906f, -0.5389218f, 0.0715920f, 0.6358653f, 1.0064040f, 0.4764405f, 0.9542342f, 0.2242897f, 0.1616169f, -1.3778440f, -0.2705311f, -1.2358010f, -0.1538814f, -0.0438948f },\n", + "{ -0.1763135f, -0.0535656f, 0.5643759f, -1.5390383f, -0.2005513f, 0.6275172f, -0.0381056f, 0.1703591f, 0.0655765f, 0.1213387f, 0.0543906f, -0.0061972f, 0.5066937f, -0.1915186f, 0.4624113f, 0.7285631f, 1.9588530f, -0.1498045f, 0.2705339f, 0.4348728f, -0.8126648f, -0.5962468f, 0.2308548f, 0.5948858f, 1.7573367f, 0.3198567f, 0.0890794f, -1.0203332f, 0.9259652f, 1.9378320f, -0.0893076f, -0.1205216f },\n", + "{ 0.0800774f, -0.3748720f, -1.4099081f, -0.7579946f, 0.1303505f, 1.2617210f, -0.4090959f, -0.2189664f, 0.1956004f, -0.2206552f, -0.0664704f, -0.1259056f, 0.5783974f, 0.2635866f, -0.4015432f, -0.5615420f, 1.1665078f, 0.3563137f, -0.4841422f, 0.0495584f, -1.0221890f, -2.2138078f, 0.1603903f, -0.2881177f, 0.3440269f, -1.4136810f, -0.1204567f, -0.2976407f, -0.3660032f, 0.6511291f, -0.0036185f, -0.1103064f },\n", + "{ -0.1190768f, 0.1079465f, -1.6465895f, 0.8783018f, 0.0417785f, -0.4893148f, 0.5675116f, -0.0493726f, 0.0923501f, -0.3304987f, -0.2314915f, -0.0379382f, 0.3644635f, 1.6061251f, 0.0743982f, -1.7036310f, 0.0469393f, -0.0169076f, -0.0846457f, 0.3039347f, 1.5323132f, 1.1516945f, -0.4392051f, 0.0519393f, -0.8842877f, 0.0210567f, 0.0042385f, 0.3631802f, 0.0299365f, -0.1161491f, -0.2673467f, 0.0801027f },\n", + "{ -0.1606232f, -0.1174612f, 1.1393912f, -3.1626005f, 0.2020190f, 0.3364365f, -1.4127800f, -0.2035749f, 0.0290988f, 0.0998231f, -0.0858107f, -0.0822701f, 0.4266679f, 1.1170434f, -0.2634015f, 2.6879389f, 3.5829473f, -0.0074217f, -0.2283435f, -0.4018083f, -0.5503482f, -1.4264138f, -1.8605294f, 1.9073273f, 2.7757249f, 1.0840679f, -0.1608723f, -4.1708899f, 0.5306313f, 3.3872285f, 0.0317254f, 0.0453062f },\n", + "{ 0.1028529f, -0.1862935f, -0.3514106f, -1.2896683f, -0.2075851f, 1.7440273f, -0.0317084f, -0.0256567f, 0.0827107f, 0.0535544f, -0.0810672f, 0.1013722f, 0.2883120f, -1.0009999f, 0.2229198f, 1.1056199f, 0.9040332f, -0.2615984f, 0.2158209f, -0.9291855f, 0.3614044f, -2.4015853f, -2.1240189f, 0.7009790f, 0.9322513f, 0.6083323f, 0.1290441f, -1.7820632f, 0.5162377f, 1.6934893f, 0.1093729f, 0.1678278f },\n", + "{ -0.0738688f, -0.2484219f, -2.2049072f, 0.6735009f, 0.0845890f, -0.4458043f, 1.6374553f, -0.1284482f, -0.0363122f, -0.2654149f, -0.0454312f, -0.0264876f, -0.1786719f, -2.2485936f, -0.0132505f, -2.4829731f, -1.0582074f, 0.0639074f, -0.0570137f, 0.2790116f, 1.2043806f, 0.6573969f, 0.2257148f, -0.7038473f, -1.1433469f, -0.2824484f, -0.1686299f, 0.6064975f, -0.1482748f, 0.9308873f, 0.0353047f, -0.2331351f },\n", + "{ -0.0381717f, 0.0235109f, 1.2123448f, 1.5028327f, -0.0427347f, -0.6673427f, -1.3916098f, -0.2319166f, -0.0112034f, -0.0724124f, -0.0266188f, 0.0489188f, -2.5781660f, 0.9075448f, -0.2781274f, -0.5351671f, -1.4783920f, 0.2318245f, 0.1258357f, 1.6879724f, 2.2178104f, 2.5865467f, -2.9686368f, 2.0748942f, -1.0530401f, -1.1760957f, -0.1100548f, 0.7415332f, -1.2369473f, -2.2372057f, -0.1560877f, -0.2036909f },\n", + "{ -0.0767220f, -0.3813806f, -0.6692066f, 1.1171360f, 0.1955123f, 0.0927913f, -0.6164231f, -0.1936815f, -0.2253457f, 0.2098780f, -0.2356415f, -0.0879891f, -2.2692015f, -0.7018560f, 0.2930437f, 1.4553533f, -1.0976124f, -0.2235222f, -0.5320002f, 0.6203124f, 2.2931771f, -0.4128396f, -2.1847551f, 1.8210577f, -1.0626109f, 0.4428261f, -0.1638004f, 0.5285351f, -0.1491041f, -0.0726089f, -0.1858867f, 0.1690394f },\n", + "{ 0.1563593f, -0.1862237f, -1.8475114f, -0.7378953f, 0.0983567f, 0.1551867f, 2.2162786f, -0.0932272f, -0.1106849f, -0.1326451f, 0.0376333f, 0.0157612f, 2.8148468f, -1.8456433f, 0.0834190f, 2.4704628f, 0.1500785f, -0.0245777f, 0.0363232f, -2.9865775f, -1.7058473f, 0.6917011f, 1.1903836f, -1.1306528f, 1.4213639f, 1.8961535f, -0.0105329f, -0.2176351f, 0.4710608f, 1.1942669f, -0.0887468f, 0.2132694f },\n", + "{ 0.2071738f, -0.2212865f, 0.7850192f, 2.1796567f, -0.0984315f, 0.0805375f, 2.7735672f, -0.1555860f, 0.0239974f, 0.0246591f, 0.1187868f, -0.0409408f, 2.2348316f, 1.5618666f, 0.0371693f, -1.9186479f, -2.7904696f, -0.0837378f, -0.1520312f, 1.2995666f, -1.4488002f, 2.6741729f, 1.2172064f, -3.4318860f, -0.3286010f, 1.1488677f, -0.1920759f, 1.0220169f, 0.0960360f, -2.2466736f, -0.1439289f, -0.2038657f },\n", + "{ 0.0203539f, -0.0770830f, -0.0548076f, 0.3161826f, -0.1546766f, 1.5058581f, 0.3716706f, -0.0279853f, -0.0396944f, 0.1678968f, -0.0523217f, 0.1077326f, 1.3584960f, 0.5697956f, -0.0556758f, -1.0488331f, -0.9394681f, 0.1332565f, 0.4668984f, 0.1539229f, -1.3861572f, 0.1012657f, 1.3388863f, -1.5068510f, -0.1488119f, 2.1504519f, 0.0248965f, 0.1813536f, 0.1426364f, -2.6684127f, 0.1982870f, 0.1384249f },\n", + "{ -0.1769641f, -0.0247985f, -0.1647689f, -1.3870394f, -0.1162030f, 0.3048662f, -2.8828626f, 0.1057448f, 0.1339286f, 0.0585208f, 0.1083090f, 0.0354991f, -2.4567196f, -2.1254835f, 0.0228132f, -0.1640477f, 0.6994863f, 0.0368678f, 0.0509801f, -2.8892326f, 0.9703321f, 0.0786054f, 0.0938517f, 1.1638378f, 1.1636230f, -0.1186888f, 0.0215163f, 0.1806985f, -0.1176552f, -1.1696986f, 0.1148661f, 0.2213662f },\n", + "{ 0.1038774f, -0.1047234f, 1.4868294f, 0.2761928f, 0.0134923f, -1.4871290f, -0.2099304f, -0.1526921f, -0.1791854f, 0.0421853f, -0.2292036f, -0.0114745f, -1.5435252f, -1.5150722f, -1.7114086f, 0.0551655f, 0.1734200f, 1.7328296f, -0.0102979f, -0.0923821f, 1.5666803f, -0.1455855f, -0.1308604f, -0.0883516f, 0.0417202f, 0.0224278f, -0.1899172f, -0.0607252f, 1.7729037f, -0.7333850f, 0.1708566f, -0.1185266f },\n", + "{ -0.1968291f, 0.1226442f, -1.4931865f, 0.2072496f, 0.0330375f, 1.5259913f, 0.2502024f, 0.1059954f, -0.2105065f, 0.0289575f, -0.1410347f, -1.6446069f, 0.4217086f, 0.9671080f, 1.6962469f, -0.2187338f, -0.1057917f, 0.1871334f, 2.0668314f, 0.1059684f, -0.7089236f, 0.1020357f, -0.0763040f, 0.3453935f, -1.0794582f, 0.6731552f, -0.2174709f, -0.1014975f, -1.8536516f, 0.4382406f, -0.1931745f, -0.1499902f },\n", + "{ -0.0451520f, -0.0075974f, -0.0751040f, -0.0006091f, -0.0962802f, -0.0943780f, 0.0421998f, -0.0221936f, 0.0959424f, -0.0376601f, -0.2136606f, 1.6487268f, 1.1979698f, 0.6900949f, -0.0029702f, 0.1591523f, 0.1682678f, -1.9215510f, -2.0697236f, 0.0993362f, -0.7588745f, 0.1796909f, 0.2314903f, -0.2894705f, -0.7456803f, -0.5973564f, -0.2129087f, 0.1293475f, 0.0351008f, 0.2747299f, -0.2403971f, -0.2218651f },\n", "};\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_2[32] = {\n", - "-0.1445483f, -0.7468296f, 2.0087461f, 0.9193060f, 0.1838548f, -0.2429122f, -0.3490699f, 0.0500740f, -2.4078236f, -0.1702676f, 0.2733943f, 0.2180888f, -0.2089010f, 1.3755207f, 0.5235071f, -0.4656681f, -0.3255171f, 0.0164221f, 1.8814882f, 0.3419669f, -0.1090209f, 1.3948971f, 0.0137390f, -1.0124286f, -0.5950485f, 0.8902460f, 0.9638857f, -3.0627401f, 1.3535937f, 0.7002685f, -0.3063155f, 0.1972668f };\n", + "0.7934635f, -0.9544018f, 0.8020973f, -0.0911000f, -0.2036114f, -0.6670265f, -2.4799440f, -0.3794742f, -0.9009696f, 0.3158563f, 0.6310644f, 0.0615856f, -0.0852578f, 0.2415757f, 0.9112729f, 0.4271888f, 0.4842319f, -0.2101376f, 0.0233746f, 0.4788695f, -0.1818675f, 0.2429213f, -0.1633921f, -0.1643308f, -0.1243287f, 0.8031887f, -0.2019781f, -0.0105518f, -0.2115961f, -0.0436087f, -1.2556835f, 0.4181046f };\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_2[32][32] = {\n", - "{ 0.1387030f, 0.0578285f, 0.0290207f, 0.1366628f, -0.0933111f, -0.1532757f, 0.0912275f, 0.0719271f, -0.1804712f, 0.0502727f, 0.0857729f, -0.1440653f, 0.1051157f, -0.0418550f, -0.0386804f, 0.1002010f, 0.1612325f, -0.0438476f, -0.1817690f, 0.0312359f, 0.0725582f, -0.0386392f, 0.0083110f, 0.0938581f, 0.0128068f, -0.1367846f, 0.0775078f, 0.1099567f, 0.1366342f, -0.1446508f, -0.1838375f, 0.1260892f },\n", - "{ -0.0889172f, -0.1109058f, -0.0090499f, -0.0161888f, 0.1067007f, 0.0519907f, 0.0063411f, -0.1817823f, -0.0761359f, -0.1343981f, -0.2255760f, 0.0553785f, 0.0583183f, 0.1439044f, -0.0505752f, 0.0720810f, 0.1430652f, -0.1004833f, 0.0520413f, -0.0544464f, 0.0066742f, 0.0136002f, 0.1225696f, -0.1489387f, -0.0167017f, 0.0657442f, 0.0523059f, 0.0774479f, 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-0.0057372f, -2.5233526f, 1.4855275f, -0.8395525f, -0.0542362f, 0.3147244f, 0.5668546f, 0.1128324f },\n", + "{ 0.2185126f, -4.1735148f, -0.0584993f, -6.9970717f, -0.1872474f, 3.6046195f, -0.3391172f, -0.2528733f, -1.8540699f, -3.6080678f, -6.8880978f, 2.8173163f, 1.1419358f, -0.5156693f, 0.6379013f, -0.8689519f, -0.5731303f, -0.0135396f, -3.8898597f, 3.9107504f, -0.0314495f, 3.7597058f, -0.1313266f, -0.0638606f, -0.1112971f, 0.5994733f, -2.0880733f, -3.4605267f, -0.0657795f, 2.9317980f, -4.0776825f, -4.6187515f },\n", + "{ 1.4306588f, -0.0924573f, -0.0255171f, -0.4936610f, -0.1691499f, 0.0794270f, -0.4408483f, 0.1652155f, -0.3639386f, 0.6207126f, 0.2110025f, -1.1174132f, 0.3868589f, -0.3762612f, -0.2353463f, -0.7807826f, 1.1994385f, 0.0715448f, 2.5127218f, 1.3676612f, -0.1004328f, -0.9425803f, -0.0994552f, -0.3417697f, -0.1076159f, 0.9109147f, -0.8347710f, 0.3245273f, 0.0240645f, 0.0364262f, 0.7244037f, 0.1504523f },\n", + "{ -0.0121835f, -0.0466975f, 0.1144113f, 0.0201244f, -0.0245609f, 0.2358061f, -0.0764014f, -0.2775822f, -0.2624222f, -0.0493765f, -0.1292863f, -0.1509897f, 0.0240071f, 0.0579032f, -0.0009308f, 0.0350776f, -0.1369558f, 0.0210988f, 0.1830976f, -0.0220976f, -0.1343361f, 0.1934764f, -0.0788890f, 0.1397982f, -0.0866419f, -0.0710399f, -0.1467413f, -0.1701790f, 0.0331260f, -0.0187575f, 0.0254800f, 0.1567311f },\n", + "{ 0.0645845f, -0.0035480f, 0.1547592f, -0.1243696f, -0.0513117f, -0.0051543f, 0.0720794f, 0.0330604f, -0.0983970f, -0.0621364f, 0.1500005f, -0.0834217f, 0.0614519f, -0.1354975f, -0.1619547f, 0.1409725f, 0.0976805f, 0.0951235f, -0.1414357f, 0.0502984f, -0.0352365f, -0.0664824f, -0.1300988f, -0.0588439f, -0.0466940f, -0.1232816f, 0.0044184f, 0.1664057f, 0.0861590f, -0.0289132f, -0.1097487f, -0.0449765f },\n", "};\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float bias_4[1] = {\n", - "-1.0653121f };\n", + "0.2672311f };\n", "\n", "ALPAKA_STATIC_ACC_MEM_GLOBAL const float wgtT_4[32][1] = {\n", - "{ 0.0599457f },\n", - "{ 2.4589522f },\n", - "{ 0.5886621f },\n", - "{ 0.5058215f },\n", - "{ -1.1172724f },\n", - "{ 1.8007317f },\n", - "{ 0.5281580f },\n", - "{ 0.5044137f },\n", - "{ 0.5756003f },\n", - "{ 0.1070907f },\n", - "{ 0.2529266f },\n", - "{ -1.0334966f },\n", - "{ 0.0026251f },\n", - "{ -0.9314435f },\n", - "{ -1.6016932f },\n", - "{ 0.3742798f },\n", - "{ -0.0195212f },\n", - "{ -2.0840223f },\n", - "{ 0.9543520f },\n", - "{ -3.1566474f },\n", - "{ 0.1217308f },\n", - "{ 1.1695908f },\n", - "{ 0.8822579f },\n", - "{ -1.2197880f },\n", - "{ -2.0250206f },\n", - "{ 0.7175051f },\n", - "{ 0.2349268f },\n", - "{ 0.5229219f },\n", - "{ 0.5292190f },\n", - "{ -1.7123013f },\n", - "{ 0.8434106f },\n", - "{ -1.0713193f },\n", + "{ -2.1982157f },\n", + "{ 1.2918848f },\n", + "{ 2.5765901f },\n", + "{ 1.3302900f },\n", + "{ 0.0534258f },\n", + "{ 1.6870886f },\n", + "{ -1.5375290f },\n", + "{ -1.7200431f },\n", + "{ -1.6248944f },\n", + "{ 1.0983198f },\n", + "{ 0.9794226f },\n", + "{ -3.2129023f },\n", + "{ 1.6483670f },\n", + "{ 0.7734902f },\n", + "{ -0.9205871f },\n", + "{ -0.9046884f },\n", + "{ -0.9422176f },\n", + "{ 0.0940378f },\n", + "{ 2.0178165f },\n", + "{ -2.5040693f },\n", + "{ 0.0147824f },\n", + "{ 1.8138467f },\n", + "{ 0.0226220f },\n", + "{ 0.0023397f },\n", + "{ 0.0879065f },\n", + "{ 1.4868226f },\n", + "{ 1.2005153f },\n", + "{ -1.0860479f },\n", + "{ 0.0119623f },\n", + "{ 1.2583143f },\n", + "{ 1.1449329f },\n", + "{ 1.2751638f },\n", "};\n", "\n" ] From b9c9b574103c2a4d01575b1080715849a8ae06d4 Mon Sep 17 00:00:00 2001 From: GNiendorf Date: Wed, 2 Oct 2024 13:56:06 -0400 Subject: [PATCH 4/5] add matched percent for t5 --- .../LSTCore/standalone/code/core/trkCore.cc | 14 ++++++++++++-- RecoTracker/LSTCore/standalone/code/core/trkCore.h | 3 ++- .../standalone/code/core/write_lst_ntuple.cc | 5 ++++- 3 files changed, 18 insertions(+), 4 deletions(-) diff --git a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc index b6db9093bbc61..bcda760ae7c72 100644 --- a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc +++ b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc @@ -285,7 +285,8 @@ std::vector matchedSimTrkIdxs(std::vector hitidxs, std::vector hi //___________________________________________________________________________________________________________________________________________________________________________________________ std::vector matchedSimTrkIdxs(std::vector hitidxs, std::vector hittypes, - bool verbose) { + bool verbose, + float *pmatched) { if (hitidxs.size() != hittypes.size()) { std::cout << "Error: matched_sim_trk_idxs() hitidxs and hittypes have different lengths" << std::endl; std::cout << "hitidxs.size(): " << hitidxs.size() << std::endl; @@ -419,6 +420,7 @@ std::vector matchedSimTrkIdxs(std::vector hitidxs, } int maxHitMatchCount = 0; // ultimate maximum of the number of matched hits std::vector matched_sim_trk_idxs; + float max_percent_matched = 0.0f; for (auto &trkidx_perm : allperms) { std::vector counts; for (auto &unique_idx : unique_idxs) { @@ -430,10 +432,18 @@ std::vector matchedSimTrkIdxs(std::vector hitidxs, int trkidx = unique_idxs[rawidx]; if (trkidx < 0) continue; - if (counts[rawidx] > (((float)nhits_input) * 0.75)) + float percent_matched = static_cast(counts[rawidx]) / nhits_input; + if (percent_matched > 0.75f) matched_sim_trk_idxs.push_back(trkidx); maxHitMatchCount = std::max(maxHitMatchCount, *std::max_element(counts.begin(), counts.end())); + max_percent_matched = std::max(max_percent_matched, percent_matched); } + + // If pmatched is provided, set its value + if (pmatched != nullptr) { + *pmatched = max_percent_matched; + } + std::set s; unsigned size = matched_sim_trk_idxs.size(); for (unsigned i = 0; i < size; ++i) diff --git a/RecoTracker/LSTCore/standalone/code/core/trkCore.h b/RecoTracker/LSTCore/standalone/code/core/trkCore.h index 0a2fddaba9d5c..ad12232eb1678 100644 --- a/RecoTracker/LSTCore/standalone/code/core/trkCore.h +++ b/RecoTracker/LSTCore/standalone/code/core/trkCore.h @@ -32,7 +32,8 @@ float runpT3(LSTEvent *event); std::vector matchedSimTrkIdxs(std::vector hitidxs, std::vector hittypes, - bool verbose = false); + bool verbose = false, + float *pmatched = nullptr); std::vector matchedSimTrkIdxs(std::vector hitidxs, std::vector hittypes, bool verbose = false); int getDenomSimTrkType(int isimtrk); int getDenomSimTrkType(std::vector simidxs); diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc index 00a15b85a8f67..fc6630310f9ee 100644 --- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc +++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc @@ -118,6 +118,7 @@ void createOptionalOutputBranches() { ana.tx->createBranch>("t5_isDuplicate"); ana.tx->createBranch>("t5_foundDuplicate"); ana.tx->createBranch>("t5_pt"); + ana.tx->createBranch>("t5_pMatched"); ana.tx->createBranch>("t5_eta"); ana.tx->createBranch>("t5_phi"); ana.tx->createBranch>("t5_score_rphisum"); @@ -504,10 +505,12 @@ void setQuintupletOutputBranches(lst::Event* event) { moduleType_binary |= (modules->moduleType[module_idx[i]] << i); } - std::vector simidx = matchedSimTrkIdxs(hit_idx, hit_type); + float percent_matched; + std::vector simidx = matchedSimTrkIdxs(hit_idx, hit_type, false, &percent_matched); ana.tx->pushbackToBranch("t5_isFake", static_cast(simidx.size() == 0)); ana.tx->pushbackToBranch("t5_pt", pt); + ana.tx->pushbackToBranch("t5_pMatched", percent_matched); ana.tx->pushbackToBranch("t5_eta", eta); ana.tx->pushbackToBranch("t5_phi", phi); ana.tx->pushbackToBranch("t5_innerRadius", __H2F(quintuplets->innerRadius[quintupletIndex])); From 6267f0f07ce32385856b8bb0a1f43af946c7c7ce Mon Sep 17 00:00:00 2001 From: GNiendorf Date: Wed, 2 Oct 2024 16:02:49 -0400 Subject: [PATCH 5/5] add t5 non anchor chisquared --- RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc index fc6630310f9ee..4737fab4e5267 100644 --- a/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc +++ b/RecoTracker/LSTCore/standalone/code/core/write_lst_ntuple.cc @@ -518,6 +518,7 @@ void setQuintupletOutputBranches(lst::Event* event) { ana.tx->pushbackToBranch("t5_outerRadius", __H2F(quintuplets->outerRadius[quintupletIndex])); ana.tx->pushbackToBranch("t5_chiSquared", quintuplets->chiSquared[quintupletIndex]); ana.tx->pushbackToBranch("t5_rzChiSquared", quintuplets->rzChiSquared[quintupletIndex]); + ana.tx->pushbackToBranch("t5_nonAnchorChiSquared", quintuplets->nonAnchorChiSquared[quintupletIndex]); ana.tx->pushbackToBranch("t5_layer_binary", layer_binary); ana.tx->pushbackToBranch("t5_moduleType_binary", moduleType_binary);