From 43f7bf2c18942ea8256f503cccf9703de0c6e2df Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Tue, 26 Nov 2019 12:53:34 +0530 Subject: [PATCH 01/13] made changes --- .gitignore | 1 + DWDM .ipynb | 382 ++++++++++++++++++------------------- DWDM_dataset_Extraction.py | 108 +++++++++++ README.md | 2 - final.py.ipynb | 132 +++++++++++++ 5 files changed, 432 insertions(+), 193 deletions(-) create mode 100644 .gitignore create mode 100644 DWDM_dataset_Extraction.py delete mode 100644 README.md create mode 100644 final.py.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..723ef36 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.idea \ No newline at end of file diff --git a/DWDM .ipynb b/DWDM .ipynb index 1b5209d..181d00b 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -2,8 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 62, - "metadata": {}, + "execution_count": 1, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# create database in the paper\n", @@ -21,32 +25,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { - "scrolled": false + "scrolled": false, + "pycharm": { + "is_executing": false + } }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ - "\n", - "Input :\n", - "\n", - "1 2 269 4701 \n", - "\n", - "\n", - "\n", - "Time stamp\n", - "\n", - "[59.71594491384269, 620.9418924377105, 632.5470757282807, 766.0051342063122, 806.0901917840641, 962.0169388900936]\n", - "\n", - "\n", - "Sequence\n", - "\n", - "['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\n", - "dict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" - ] + "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[79.85811385478658, 192.27537410687833, 454.97031059755966, 488.458719620248, 627.4713543415553, 832.6980314536856]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + ], + "output_type": "stream" } ], "source": [ @@ -85,13 +77,11 @@ "\n", "\tfor j in range(num):\n", "\t\tval=(random.uniform(start, end))\n", - "\t#\tval=int(val)\n", "\t\tres.append(val)\n", "\n", "\tres.sort()\n", "\treturn res\n", "\n", - "\n", "text_file= open(\"sample.data\",\"r\")\n", "lines= text_file.readlines()\n", "# line1= lines[0]\n", @@ -101,60 +91,37 @@ "x = dict()\n", "for i in range(len(lines)):\n", "\tline1 = list(lines[i].split(\" \"))\n", - "\t# print(line1)\n", "\tline1.pop()\n", "\tlength = int(line1[0])\n", - "\t# print(length)\n", "\tline1.pop(0)\n", - "\t# x= dict()\n", - "\t# print(line1)\n", "\tlst = []\n", "\n", "\tfor j in range(length):\n", "\t\tsizeoflistoftime= int(line1[0])\n", - "\t\t# print(sizeoflistoftime)\n", "\t\tline1.pop(0)\n", - "\t\t# print(line1)\n", - "\n", - "# listoftime=Randlist(0,1000,sizeoflistoftime)\n", + "\t\t\n", "\t\tlistoftime=Randlist(0,1000,1)\n", "\t\tfor k in range(sizeoflistoftime):\n", "\t\t\tline1.pop(0)\n", - "\t\t# print(listoftime)\n", "\n", "\t\tlst.append(listoftime[0])\n", - "\t# print(\"The list is :\")\n", - "\t# print(lst)\n", + "\n", "\tlst.sort()\n", "\tx[i]=lst[:]\n", "\n", - "\n", - "# print(x)\n", - "\n", - "# print(undelted_lines)\n", "x2= dict()\n", "\n", - "# item_alpha=dict()\n", - "# alphabet='a'\n", - "\n", "for i in range(len(undelted_lines)):\n", "\tline1 = list(undelted_lines[i].split(\" \"))\n", - "\t# print(line1)\n", "\tline1.pop()\n", "\tlength = int(line1[0])\n", - "\t# print(length)\n", "\tline1.pop(0)\n", - "\t# print(line1)\n", "\tlistoftime=[]\n", "\tfor j in range(length):\n", "\t\tsizeoflistoftime= int(line1[0])\n", - "\t\t# print(sizeoflistoftime)\n", "\t\tline1.pop(0)\n", - "\t\t# print(line1)\n", "\t\ttemp=\"\"\n", - "\t\t# listoftime=Randlist(0,1000,sizeoflistoftime)\n", - "\t\tfor k in range(sizeoflistoftime):\n", - " \n", + "\t\tfor k in range(sizeoflistoftime): \n", "# \t\t\tif line1[0] in item_alpha.keys():\n", "# \t\t\t\tpass\n", "# \t\t\telse :\n", @@ -185,228 +152,164 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 3, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ - "0 2479 21545477 0440227660369838 6639792690549748 1247 1992 2535 509577449337 234347377188851892419486 15.469584262986613 75.6177321104664 155.78500287678798 173.64829834524076 455.4399385621556 500.484809781422 523.9953960957207 570.1059519605159 861.7382510468273 \n", - "1 076515427203 585473098827 5993 0991495663767993 060617988964 060610912787 40235242 245163928259 07033355589271699239 118.51177604665509 182.28992773802665 367.3632842084625 376.08000570181997 395.58320704113424 886.6666090937258 938.9197731893184 959.6817148375404 990.8099147450886 \n", - "2 9033 184577138778 128517057890 404969087443 07651739 36279693 59.71594491384269 620.9418924377105 632.5470757282807 766.0051342063122 806.0901917840641 962.0169388900936 \n", - "4 62737655 2654 478.2847102154313 911.4960984422834 \n", - "5 0149 35339471 5758 1491 5024 3812 155.00180545961194 163.6677920404198 617.7771644906031 633.6137864801188 676.8115396080481 942.4122063655578 \n", - "6 340447646487 521076377771 140555019708 357238757705 270941675713 0886287069187963857191259795 07236970 06255975 12.886202605772446 50.0837043321627 166.00945124360146 189.51248295548862 439.7787871131751 883.2450985837778 903.5302630767404 981.1772022893753 \n", - "7 7851 2053 58268595 1734 46.275544054731995 62.43536446145293 118.93634783391649 946.6968439733122 \n", - "8 72137771 5510 46529373 9928 67717774 31138769 45.93273726408731 95.04437502921293 161.27419795678634 365.551422010759 526.229583208764 854.5005573980293 \n", - "9 07656075 5735 3541 2715 2104 74.89404059066985 201.93216407003922 299.8990747881353 678.3754420434778 933.8630489489843 \n", - "[['2479', 15.469584262986613], ['21545477', 75.6177321104664], ['0440227660369838', 155.78500287678798], ['6639792690549748', 173.64829834524076], ['1247', 455.4399385621556], ['1992', 500.484809781422], ['2535', 523.9953960957207], ['509577449337', 570.1059519605159], ['234347377188851892419486', 861.7382510468273]]\n" - ] + "0 2479 21545477 0440227660369838 6639792690549748 1247 1992 2535 509577449337 234347377188851892419486 0.260102375349347 46.90453689697982 63.80695207000986 129.7800909532417 312.7573033409937 347.0395284002097 453.96261021946225 525.6725938824466 876.3749606676412 1 076515427203 585473098827 5993 0991495663767993 060617988964 060610912787 40235242 245163928259 07033355589271699239 21.08316422259737 30.122788853117477 190.08257059594592 300.76092039337 370.2440726572592 374.52142456752904 822.8115241081259 862.4209436895637 939.4089943147089 2 9033 184577138778 128517057890 404969087443 07651739 36279693 79.85811385478658 192.27537410687833 454.97031059755966 488.458719620248 627.4713543415553 832.6980314536856 4 62737655 2654 424.86667660826447 625.0036318547003 5 0149 35339471 5758 1491 5024 3812 22.38900738758842 187.73482478116955 531.6915979215606 588.1601118086935 662.1341033427515 858.3015442621355 6 340447646487 521076377771 140555019708 357238757705 270941675713 0886287069187963857191259795 07236970 06255975 155.36006455295126 325.5188946232157 349.95406195634195 497.1245141531623 587.9123868803858 695.2841964528285 949.8197955859782 973.0574969268339 7 7851 2053 58268595 1734 149.28237150423996 217.17370529349898 788.4888795387346 882.0355724656791 8 72137771 5510 46529373 9928 67717774 31138769 77.88294289561493 118.9039906764362 159.44556960877466 602.2308832773459 655.2596794254745 698.9598462226458 9 07656075 5735 3541 2715 2104 98.1110644017591 229.71982021876968 304.2368458308721 322.2352947071527 428.5969986205317 [['2479', 0.260102375349347], ['21545477', 46.90453689697982], ['0440227660369838', 63.80695207000986], ['6639792690549748', 129.7800909532417], ['1247', 312.7573033409937], ['1992', 347.0395284002097], ['2535', 453.96261021946225], ['509577449337', 525.6725938824466], ['234347377188851892419486', 876.3749606676412]]\n" + ], + "output_type": "stream" } ], "source": [ "# print database\n", - "\n", "for i in database.keys():\n", " print(i,end=' ')\n", " for j in database[i]:\n", " print(j[0],end=' ')\n", " for j in database[i]:\n", " print(j[1],end=' ')\n", - " print()\n", "print(database['0'])" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 4, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# function to return time-intervals\n", "def time_interval_itemsets(i,j):\n", - " \n", " time_i=i[1]\n", " time_j=j[1]\n", - " \n", - " return(abs(time_i-time_j))\n", - " " + " return abs(time_i-time_j)" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 5, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# itemset pair generation\n", "def itemset_pair_generator(seq):\n", - " \n", " itemset_pairs=[]\n", " n=len(seq)\n", " for i in range(n):\n", " for j in range(i+1,n):\n", " itemset_pairs.append([seq[i],seq[j]])\n", - " \n", " return(itemset_pairs)" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# function to generate the time intervals for a sequence\n", "def time_interval_sequence(itemset_pairs):\n", - " \n", " itemset_pairs_time=[]\n", " for i in itemset_pairs:\n", " time_interval=time_interval_itemsets(i[0],i[1])\n", " itemset_pairs_time.append(i+[time_interval])\n", - " \n", - " return(itemset_pairs_time)\n", - " \n", - " " + " return(itemset_pairs_time)" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# function to obtain the strength of a pair of itemsets\n", "def strength_pair(itemset_pairs_time):\n", - " \n", " itemset_pairs_time_strength=[]\n", " for i in itemset_pairs_time:\n", - " \n", " itemset1=i[0][0]\n", " itemset2=i[1][0]\n", - "# strength= len(itemset1)*len(itemset2)\n", + " # strength= len(itemset1)*len(itemset2)\n", " strength= len(itemset1)*len(itemset2)/16 # because each item has 4 characters\n", - " itemset_pairs_time_strength.append(i+[strength])\n", - " \n", - " return itemset_pairs_time_strength\n", - " \n", - " " + " itemset_pairs_time_strength.append(i + [strength])\n", + " return itemset_pairs_time_strength" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# def main(): main function\n", - " \n", - "for i in database.keys():\n", - " itemset_pairs=itemset_pair_generator(database[i])\n", - " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", - " itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", - " TiW=TiW_sequence(itemset_pairs_time_strength)\n", - " database[i].append(TiW)\n", - "\n", - "print()" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'float' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mitemset_pairs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mitemset_pair_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatabase\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mitemset_pairs_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtime_interval_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitemset_pairs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mitemset_pairs_time_strength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstrength_pair\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitemset_pairs_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mTiW\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTiW_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitemset_pairs_time_strength\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# print(itemset_pairs)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m\u001b[0m in \u001b[0;36mtime_interval_sequence\u001b[1;34m(itemset_pairs)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mitemset_pairs_time\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mitemset_pairs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mtime_interval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtime_interval_itemsets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mitemset_pairs_time\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtime_interval\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m\u001b[0m in \u001b[0;36mtime_interval_itemsets\u001b[1;34m(i, j)\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtime_i\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtime_j\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime_i\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mtime_j\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: 'float' object is not subscriptable" - ] + "pycharm": { + "is_executing": false } - ], - "source": [ - "# testing functions\n", - "\n", - "itemset_pairs=itemset_pair_generator(database['2'])\n", - "itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", - "itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", - "TiW=TiW_sequence(itemset_pairs_time_strength)\n", - "# print(itemset_pairs)\n", - "print(TiW)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, + }, "outputs": [], "source": [ "# function to calculate the weighting function of the time interval\n", - "def weighting_function(time_interval):\n", - " \n", - " delta=0.9\n", - " u=1\n", + "def weighting_function(time_interval, delta=0.9, u=1):\n", " w_TI=delta**(time_interval/u)\n", - " \n", " return(w_TI)" ] }, { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# function to calculate the time interval weight of the sequence\n", "def TiW_sequence(itemset_pairs_time_strength):\n", - " \n", " total_strength=0\n", " weighted_strength=0\n", " for i in itemset_pairs_time_strength:\n", - " \n", " w_TI=weighting_function(i[2])\n", " weighted_strength+=w_TI*i[3]\n", " total_strength+=i[3]\n", - " \n", " TiW=weighted_strength/total_strength\n", - " \n", - " return(TiW)\n", - " \n", - " \n", - " " + " return(TiW)" ] }, { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# function to check if a given sequence is a super sequence of the other \n", "# checks if seq1 is a super sequence of seq2\n", - "def is_super_seq(seq1,seq2):\n", + "def is_super_seq_test(seq1,seq2):\n", " i= 0\n", " j= 0\n", - " \n", " while i, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m def TiW_support()\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] + "name": "stdout", + "text": [ + "0.006363912445283291\n" + ], + "output_type": "stream" } ], "source": [ - "# testing function\n", - "# def TiW_support()\n", - "\n", - "count=0\n", - "for i in database.keys():\n", - " seq1=database[i]\n", - " seq2=[['ad'],['bc'],[]]\n", - " count+=is_super_seq(seq1,seq2)\n", - " \n", - "print(count/len(database.keys()))" - ] + "# testing functions\n", + "itemset_pairs=itemset_pair_generator(database['0'])\n", + "itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", + "itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", + "TiW=TiW_sequence(itemset_pairs_time_strength)\n", + "# print(itemset_pairs)\n", + "print(TiW)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } } ], "metadata": { @@ -465,8 +456,17 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/DWDM_dataset_Extraction.py b/DWDM_dataset_Extraction.py new file mode 100644 index 0000000..3c35170 --- /dev/null +++ b/DWDM_dataset_Extraction.py @@ -0,0 +1,108 @@ +# create database + +# database = dict() +# database['sid']=[10,20,30,40] +# database['sequence']=[['a','abc','ac','d'],['ad','c','bc','ae'],['ad','bc','df'],['a','abc','d']] +# database['timestamp']=[[0,1,2,3],[1,2,3,4],[1,3,5],[2,3,4]] +# +# database['10'] = [['a', 0], ['abc', 1], ['ac', 2], ['d', 3]] +# database['20'] = [['ad', 1], ['c', 2], ['bc', 3], ['ae', 4]] +# database['30'] = [['ad', 1], ['bc', 3], ['df', 5]] +# database['cd40'] = [['a', 2], ['abc', 3], ['d', 4]] +# +# # print database +# +# for i in database.keys(): +# print(i, end=' ') +# for j in database[i]: +# print(j[0], end=' ') +# for j in database[i]: +# print(j[1], end=' ') +# print() + +import random + +# lower = 111; upper = 999 +# random_float = random.uniform(lower, upper) +# print(random_float) +def Randlist(start, end, num): + res = [] + + for j in range(num): + val=(random.uniform(start, end)) + # val=int(val) + res.append(val) + + res.sort() + return res + + +text_file= open("sample.data","r") +lines= text_file.readlines() +# line1= lines[0] +undelted_lines= lines +print("\nInput :\n") +print(lines) +x = dict() +for i in range(len(lines)): + line1 = list(lines[i].split(" ")) + # print(line1) + line1.pop() + length = int(line1[0]) + # print(length) + line1.pop(0) + # x= dict() + # print(line1) + lst = [] + + for j in range(length): + sizeoflistoftime= int(line1[0]) + # print(sizeoflistoftime) + line1.pop(0) + # print(line1) + + listoftime=Randlist(0,1000,sizeoflistoftime) + for k in range(sizeoflistoftime): + line1.pop(0) + # print(listoftime) + + lst.append(listoftime) + # print("The list is :") + # print(lst) + x[i]=lst + + +# print(x) + +# print(undelted_lines) +x2= dict() + +for i in range(len(undelted_lines)): + line1 = list(undelted_lines[i].split(" ")) + # print(line1) + line1.pop() + length = int(line1[0]) + # print(length) + line1.pop(0) + # print(line1) + listoftime=[] + for j in range(length): + sizeoflistoftime= int(line1[0]) + # print(sizeoflistoftime) + line1.pop(0) + # print(line1) + temp="" + # listoftime=Randlist(0,1000,sizeoflistoftime) + for k in range(sizeoflistoftime): + temp=temp+line1[0] + line1.pop(0) + + + listoftime.append(temp) + # print(listoftime) + x2[i]=listoftime + +print("\n\nTime stamp\n") +print(x) +print("\n\nSequence\n") +print(x2) \ No newline at end of file diff --git a/README.md b/README.md deleted file mode 100644 index 76047a7..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Sequential-Pattern-mining-using-TiW -Implementation of Sequential Pattern mining using Time interval weights diff --git a/final.py.ipynb b/final.py.ipynb new file mode 100644 index 0000000..c97cfef --- /dev/null +++ b/final.py.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%", + "is_executing": false + } + }, + "outputs": [], + "source": [ + "import random\n", + "\n", + "def Randlist(start, end, num):\n", + "\tres = []\n", + "\n", + "\tfor j in range(num):\n", + "\t\tval=(random.uniform(start, end))\n", + "\t#\tval=int(val)\n", + "\t\tres.append(val)\n", + "\n", + "\tres.sort()\n", + "\treturn res" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "text": [ + "\nInput :\n\n1 2 269 4701 \n\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + ], + "output_type": "stream" + } + ], + "source": [ + "text_file= open(\"sample.data\",\"r\")\n", + "lines= text_file.readlines()\n", + "undelted_lines= lines\n", + "print(\"\\nInput :\\n\")\n", + "print(lines[3])\n", + "x = dict()\n", + "for i in range(len(lines)):\n", + "\tline1 = list(lines[i].split(\" \"))\n", + "\tline1.pop()\n", + "\tlength = int(line1[0])\n", + "\tline1.pop(0)\n", + "\tlst = []\n", + "\n", + "\tfor j in range(length):\n", + "\t\tsizeoflistoftime= int(line1[0])\n", + "\t\tline1.pop(0)\n", + "\t\tlistoftime=Randlist(0,1000,1)\n", + "\t\tfor k in range(sizeoflistoftime):\n", + "\t\t\tline1.pop(0)\n", + "\t\tlst.append(listoftime[0])\n", + "\tlst.sort()\n", + "\tx[i]=lst[:]\n", + "\n", + "x2 = dict()\n", + "\n", + "for i in range(len(undelted_lines)):\n", + "\tline1 = list(undelted_lines[i].split(\" \"))\n", + "\tline1.pop()\n", + "\tlength = int(line1[0])\n", + "\tline1.pop(0)\n", + "\tlistoftime=[]\n", + "\tfor j in range(length):\n", + "\t\tsizeoflistoftime= int(line1[0])\n", + "\t\tline1.pop(0)\n", + "\t\ttemp=\"\"\n", + "\t\tfor k in range(sizeoflistoftime):\n", + "\t\t\ttemp=temp+line1[0].zfill(4)\n", + "\t\t\tline1.pop(0)\n", + "\t\tlistoftime.append(temp)\n", + "\tx2[i]=listoftime\n", + "\n", + "database=dict()\n", + "for i in x.keys():\n", + " if len(x[i])>1:\n", + " database[str(i)]=[]\n", + " for j in range(len(x[i])):\n", + " database[str(i)].append([x2[i][j],x[i][j]])\n", + " \n", + "print(database.keys())\n", + " " + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%s\n", + "is_executing": false + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 7a6eef93314a1801d2573a14c9c7ce0b61756664 Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Tue, 26 Nov 2019 17:10:28 +0530 Subject: [PATCH 02/13] all done --- DWDM .ipynb | 77 ++++++++++++++++++++++++++++++++++++----------------- plotting.py | 25 +++++++++++++++++ 2 files changed, 78 insertions(+), 24 deletions(-) create mode 100644 plotting.py diff --git a/DWDM .ipynb b/DWDM .ipynb index 181d00b..f17d0c3 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "pycharm": { "is_executing": false @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "scrolled": false, "pycharm": { @@ -36,7 +36,7 @@ { "name": "stdout", "text": [ - "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[79.85811385478658, 192.27537410687833, 454.97031059755966, 488.458719620248, 627.4713543415553, 832.6980314536856]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[127.99943115266844, 178.30045841307884, 602.9104236920415, 616.5856578475126, 654.6502973608117, 747.1828177459225]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" ], "output_type": "stream" } @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "pycharm": { "is_executing": false @@ -162,7 +162,7 @@ { "name": "stdout", "text": [ - "0 2479 21545477 0440227660369838 6639792690549748 1247 1992 2535 509577449337 234347377188851892419486 0.260102375349347 46.90453689697982 63.80695207000986 129.7800909532417 312.7573033409937 347.0395284002097 453.96261021946225 525.6725938824466 876.3749606676412 1 076515427203 585473098827 5993 0991495663767993 060617988964 060610912787 40235242 245163928259 07033355589271699239 21.08316422259737 30.122788853117477 190.08257059594592 300.76092039337 370.2440726572592 374.52142456752904 822.8115241081259 862.4209436895637 939.4089943147089 2 9033 184577138778 128517057890 404969087443 07651739 36279693 79.85811385478658 192.27537410687833 454.97031059755966 488.458719620248 627.4713543415553 832.6980314536856 4 62737655 2654 424.86667660826447 625.0036318547003 5 0149 35339471 5758 1491 5024 3812 22.38900738758842 187.73482478116955 531.6915979215606 588.1601118086935 662.1341033427515 858.3015442621355 6 340447646487 521076377771 140555019708 357238757705 270941675713 0886287069187963857191259795 07236970 06255975 155.36006455295126 325.5188946232157 349.95406195634195 497.1245141531623 587.9123868803858 695.2841964528285 949.8197955859782 973.0574969268339 7 7851 2053 58268595 1734 149.28237150423996 217.17370529349898 788.4888795387346 882.0355724656791 8 72137771 5510 46529373 9928 67717774 31138769 77.88294289561493 118.9039906764362 159.44556960877466 602.2308832773459 655.2596794254745 698.9598462226458 9 07656075 5735 3541 2715 2104 98.1110644017591 229.71982021876968 304.2368458308721 322.2352947071527 428.5969986205317 [['2479', 0.260102375349347], ['21545477', 46.90453689697982], ['0440227660369838', 63.80695207000986], ['6639792690549748', 129.7800909532417], ['1247', 312.7573033409937], ['1992', 347.0395284002097], ['2535', 453.96261021946225], ['509577449337', 525.6725938824466], ['234347377188851892419486', 876.3749606676412]]\n" + "0 2479 21545477 0440227660369838 6639792690549748 1247 1992 2535 509577449337 234347377188851892419486 11.162250234170944 109.3573196309987 135.66772335772092 539.7583669480725 590.3977680105511 645.4766838205031 678.3835755984883 784.4179388385186 980.5171502576593 1 076515427203 585473098827 5993 0991495663767993 060617988964 060610912787 40235242 245163928259 07033355589271699239 75.42688989169666 79.37004235321598 91.71694426042399 189.07080580907353 248.30120548344314 395.4730123963461 678.2124872805053 858.0763930147548 987.2802850654488 2 9033 184577138778 128517057890 404969087443 07651739 36279693 127.99943115266844 178.30045841307884 602.9104236920415 616.5856578475126 654.6502973608117 747.1828177459225 4 62737655 2654 664.4914709597618 733.3543480158265 5 0149 35339471 5758 1491 5024 3812 178.81860127671712 198.0875206423155 506.8925163933543 515.5547444518129 542.284690285611 736.5890112718595 6 340447646487 521076377771 140555019708 357238757705 270941675713 0886287069187963857191259795 07236970 06255975 139.92629184031446 212.14962131245207 284.66438531943015 305.33074032015963 461.49881465989364 753.5015311247081 842.7005046662232 936.0937361203426 7 7851 2053 58268595 1734 124.08185672260474 351.06636277749635 358.44237737427966 586.5930488715281 8 72137771 5510 46529373 9928 67717774 31138769 150.91336775480124 161.89529105523494 546.4272948565249 597.647472489338 737.5166726800785 867.3597306600046 9 07656075 5735 3541 2715 2104 324.55516796052365 444.02438245475093 462.39175073968863 526.9502050016796 598.2123265695432 [['2479', 11.162250234170944], ['21545477', 109.3573196309987], ['0440227660369838', 135.66772335772092], ['6639792690549748', 539.7583669480725], ['1247', 590.3977680105511], ['1992', 645.4766838205031], ['2535', 678.3835755984883], ['509577449337', 784.4179388385186], ['234347377188851892419486', 980.5171502576593]]\n" ], "output_type": "stream" } @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "pycharm": { "is_executing": false @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "pycharm": { "is_executing": false @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "pycharm": { "is_executing": false @@ -236,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "pycharm": { "is_executing": false @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "pycharm": { "is_executing": false @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "pycharm": { "is_executing": false @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "pycharm": { "is_executing": false @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "outputs": [], "source": [ "def chunkstring(string, length=4):\n", @@ -343,8 +343,11 @@ " return len(el) \n", " \n", "def is_super_seq(s1, s2):\n", - " seq_s1 = [chunkstring(s1[i][0]) for i in range(len(s1))].sort(key=key, reverse=True)\n", - " seq_s2 = [chunkstring(s2[i][0]) for i in range(len(s2))].sort(key=key)\n", + " seq_s1 = [chunkstring(s1[i][0]) for i in range(len(s1))]\n", + " seq_s1.sort(key=key, reverse=True)\n", + " seq_s2 = [chunkstring(s2[i][0]) for i in range(len(s2))]\n", + " print(seq_s1, seq_s2)\n", + " seq_s2.sort(key=key)\n", " super_seq_matrix = [0 for j in range(len(s2))]\n", " for ii in range(len(seq_s1)):\n", " for jj in range(len(seq_s2)):\n", @@ -353,14 +356,17 @@ " for i in seq_s1[ii]:\n", " if i in seq_s2[jj]:\n", " count+=1\n", - " if count == len(ii):\n", + " if count == len(seq_s1[ii]):\n", " super_seq_matrix[jj] = 1\n", " \n", " if sum(super_seq_matrix) == len(seq_s1):\n", " return True\n", - " \n", + " \n", " return False\n", - " " + "\n", + "# s1=[['12'], ['123'], ['35']]\n", + "# s2 = [['1234123'], ['12'], ['345435']] \n", + "# is_super_seq(s1, s2)" ], "metadata": { "collapsed": false, @@ -372,8 +378,16 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "text": [ + "\n" + ], + "output_type": "stream" + } + ], "source": [ "print()\n", "\n", @@ -384,17 +398,19 @@ "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%%\n", + "is_executing": false } } }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "outputs": [], "source": [ "# def main(): main function\n", "TiW_db = dict()\n", + "\n", "for i in database.keys():\n", " itemset_pairs=itemset_pair_generator(database[i])\n", " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", @@ -411,12 +427,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "outputs": [ { "name": "stdout", "text": [ - "0.006363912445283291\n" + "0.0024956012398205936\n" ], "output_type": "stream" } @@ -437,6 +453,19 @@ "is_executing": false } } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n", + "is_executing": false + } + } } ], "metadata": { diff --git a/plotting.py b/plotting.py new file mode 100644 index 0000000..2e6dd0f --- /dev/null +++ b/plotting.py @@ -0,0 +1,25 @@ +import matplotlib.pyplot as plt + +def is_none(l, length): + if l is None: + return [None for ii in range(length)] + else: + assert len(l), length + return l + +# no. pattern vs threshold for delta = 0.5 0.7 0.9 +def plt_graphs(xss, yss, labels=None, colors=None, markers=None, x_label="x - axis", y_label="y - axis"): + l = len(xss) + assert l, len(yss) + + labels = is_none(labels, l) + markers = is_none(markers, l) + colors = is_none(colors, l) + + for ll in range(l): + plt.plot(xss[ll], yss[ll], label=labels[ll], color=colors[ll], marker=markers[ll]) + plt.legend() + plt.show() + + +plt_graphs([[1, 2]], [[2, 2]]) From e11d1fff1001988746a579aade2121459285dabb Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 04:02:27 +0530 Subject: [PATCH 03/13] added changes --- .gitignore | 3 +- DWDM .ipynb | 324 ++++++++++++++++++++++++++++------------------------ 2 files changed, 177 insertions(+), 150 deletions(-) diff --git a/.gitignore b/.gitignore index 723ef36..931fc08 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ -.idea \ No newline at end of file +.idea +venv/Lib \ No newline at end of file diff --git a/DWDM .ipynb b/DWDM .ipynb index f17d0c3..c16b749 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -2,12 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "# create database in the paper\n", @@ -25,20 +21,13 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false, - "pycharm": { - "is_executing": false - } - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", - "text": [ - "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[127.99943115266844, 178.30045841307884, 602.9104236920415, 616.5856578475126, 654.6502973608117, 747.1828177459225]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" - ], - "output_type": "stream" + "output_type": "stream", + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[18.71148011698176, 21.08866492827954, 98.47286727914306, 948.7654104051413, 971.058175488234, 980.2666847459581]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" } ], "source": [ @@ -152,40 +141,31 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", - "text": [ - "0 2479 21545477 0440227660369838 6639792690549748 1247 1992 2535 509577449337 234347377188851892419486 11.162250234170944 109.3573196309987 135.66772335772092 539.7583669480725 590.3977680105511 645.4766838205031 678.3835755984883 784.4179388385186 980.5171502576593 1 076515427203 585473098827 5993 0991495663767993 060617988964 060610912787 40235242 245163928259 07033355589271699239 75.42688989169666 79.37004235321598 91.71694426042399 189.07080580907353 248.30120548344314 395.4730123963461 678.2124872805053 858.0763930147548 987.2802850654488 2 9033 184577138778 128517057890 404969087443 07651739 36279693 127.99943115266844 178.30045841307884 602.9104236920415 616.5856578475126 654.6502973608117 747.1828177459225 4 62737655 2654 664.4914709597618 733.3543480158265 5 0149 35339471 5758 1491 5024 3812 178.81860127671712 198.0875206423155 506.8925163933543 515.5547444518129 542.284690285611 736.5890112718595 6 340447646487 521076377771 140555019708 357238757705 270941675713 0886287069187963857191259795 07236970 06255975 139.92629184031446 212.14962131245207 284.66438531943015 305.33074032015963 461.49881465989364 753.5015311247081 842.7005046662232 936.0937361203426 7 7851 2053 58268595 1734 124.08185672260474 351.06636277749635 358.44237737427966 586.5930488715281 8 72137771 5510 46529373 9928 67717774 31138769 150.91336775480124 161.89529105523494 546.4272948565249 597.647472489338 737.5166726800785 867.3597306600046 9 07656075 5735 3541 2715 2104 324.55516796052365 444.02438245475093 462.39175073968863 526.9502050016796 598.2123265695432 [['2479', 11.162250234170944], ['21545477', 109.3573196309987], ['0440227660369838', 135.66772335772092], ['6639792690549748', 539.7583669480725], ['1247', 590.3977680105511], ['1992', 645.4766838205031], ['2535', 678.3835755984883], ['509577449337', 784.4179388385186], ['234347377188851892419486', 980.5171502576593]]\n" - ], - "output_type": "stream" + "output_type": "stream", + "text": "[['2479', 87.0294811398571], ['21545477', 144.80374533004536], ['0440227660369838', 239.53135562861627], ['6639792690549748', 356.96087644275707], ['1247', 434.51060050020817], ['1992', 592.2727653361246], ['2535', 609.8641899810636], ['509577449337', 674.9647278296911], ['234347377188851892419486', 995.1919612584179]]\n" } ], "source": [ - "# print database\n", - "for i in database.keys():\n", - " print(i,end=' ')\n", - " for j in database[i]:\n", - " print(j[0],end=' ')\n", - " for j in database[i]:\n", - " print(j[1],end=' ')\n", + "# # print database\n", + "# for i in database.keys():\n", + "# print(i,end=' ')\n", + "# for j in database[i]:\n", + "# print(j[0],end=' ')\n", + "# for j in database[i]:\n", + "# print(j[1],end=' ')\n", + "# print(database['0'])\n", "print(database['0'])" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# function to return time-intervals\n", @@ -197,12 +177,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "# itemset pair generation\n", @@ -217,12 +193,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# function to generate the time intervals for a sequence\n", @@ -236,12 +208,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "# function to obtain the strength of a pair of itemsets\n", @@ -258,36 +226,44 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# function to calculate the weighting function of the time interval\n", - "def weighting_function(time_interval, delta=0.9, u=1):\n", + "import math\n", + "\n", + "def weighting_function_1(time_interval, delta=0.9, u=1):\n", " w_TI=delta**(time_interval/u)\n", + " return(w_TI)\n", + "\n", + "def weighting_function_2(time_interval, delta=0.9, u=1):\n", + " w_TI=delta**math((1 + (time_interval/u)), 2)\n", + " return(w_TI)\n", + "\n", + "def weighting_function_3(time_interval, delta=0.9, u=1):\n", + " w_TI=delta**math.ceil(time_interval/u)\n", " return(w_TI)" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "# function to calculate the time interval weight of the sequence\n", - "def TiW_sequence(itemset_pairs_time_strength):\n", + "def TiW_sequence(itemset_pairs_time_strength, delta=0.9, u=1, wf = 1):\n", + " if wf == 1:\n", + " weighting_function = weighting_function_1\n", + " elif wf == 2:\n", + " weighting_function = weighting_function_2\n", + " else:\n", + " weighting_function = weighting_function_3\n", " total_strength=0\n", " weighted_strength=0\n", " for i in itemset_pairs_time_strength:\n", - " w_TI=weighting_function(i[2])\n", + " w_TI=weighting_function(i[2], delta, u)\n", " weighted_strength+=w_TI*i[3]\n", " total_strength+=i[3]\n", " TiW=weighted_strength/total_strength\n", @@ -296,12 +272,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "is_executing": false - } - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# function to check if a given sequence is a super sequence of the other \n", @@ -334,6 +306,7 @@ { "cell_type": "code", "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "def chunkstring(string, length=4):\n", @@ -346,7 +319,6 @@ " seq_s1 = [chunkstring(s1[i][0]) for i in range(len(s1))]\n", " seq_s1.sort(key=key, reverse=True)\n", " seq_s2 = [chunkstring(s2[i][0]) for i in range(len(s2))]\n", - " print(seq_s1, seq_s2)\n", " seq_s2.sort(key=key)\n", " super_seq_matrix = [0 for j in range(len(s2))]\n", " for ii in range(len(seq_s1)):\n", @@ -362,79 +334,59 @@ " if sum(super_seq_matrix) == len(seq_s1):\n", " return True\n", " \n", - " return False\n", - "\n", - "# s1=[['12'], ['123'], ['35']]\n", - "# s2 = [['1234123'], ['12'], ['345435']] \n", - "# is_super_seq(s1, s2)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + " return False" + ] }, { "cell_type": "code", "execution_count": 13, - "outputs": [ - { - "name": "stdout", - "text": [ - "\n" - ], - "output_type": "stream" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "print()\n", + "def TiW_support(TiWs, db):\n", + " TiW_supports = dict()\n", + " TiW_normalise = 0\n", + " for x in db.keys():\n", + " selected_sum = 0\n", + " TiW_normalise += TiWs[x]\n", + " for s in db.keys():\n", + " if is_super_seq(db[x], db[s]):\n", + " selected_sum += TiWs[s]\n", + " TiW_supports[x] = (selected_sum)\n", "\n", - "def TiW_support(x, db):\n", - " pass\n", + " for x in db.keys():\n", + " TiW_supports[x] /= TiW_normalise\n", + " return TiW_supports \n", " " - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "code", "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# def main(): main function\n", - "TiW_db = dict()\n", - "\n", - "for i in database.keys():\n", - " itemset_pairs=itemset_pair_generator(database[i])\n", - " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", - " itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", - " TiW_db[i]=TiW_sequence(itemset_pairs_time_strength)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + "def get_supports(delta, u):\n", + " TiW_db = dict()\n", + " for i in database.keys():\n", + " itemset_pairs=itemset_pair_generator(database[i])\n", + " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", + " itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", + " TiW_db[i]=TiW_sequence(itemset_pairs_time_strength, delta, u)\n", + " sups = TiW_support(TiW_db,database)\n", + " return [sups[k] for k in sups.keys()]" + ] }, { "cell_type": "code", "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", - "text": [ - "0.0024956012398205936\n" - ], - "output_type": "stream" + "output_type": "stream", + "text": "0.000749793044505429\n" } ], "source": [ @@ -445,27 +397,101 @@ "TiW=TiW_sequence(itemset_pairs_time_strength)\n", "# print(itemset_pairs)\n", "print(TiW)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false - } - } + ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, + "metadata": {}, "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n", - "is_executing": false + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def is_none(l, length):\n", + "\tif l is None:\n", + "\t\treturn [None for ii in range(length)]\n", + "\telse:\n", + "\t\tassert len(l), length\n", + "\t\treturn l\n", + "\n", + "# no. pattern vs threshold for delta = 0.5 0.7 0.9\n", + "def plt_graphs(xss, yss, labels=None, colors=None, markers=None, x_label=\"x - axis\", y_label=\"y - axis\"):\n", + " l = len(xss)\n", + " assert l, len(yss)\n", + " labels = is_none(labels, l)\n", + " markers = is_none(markers, l)\n", + " colors = is_none(colors, l)\n", + " for ll in range(l):\n", + " plt.plot(xss[ll], yss[ll], label=labels[ll], color=colors[ll], marker=markers[ll])\n", + " plt.xlabel(x_label)\n", + " plt.ylabel(y_label)\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } - } + ], + "source": [ + "# For graph 1\n", + "gs = []\n", + "dels = [0.9, 0.7, 0.5]\n", + "pers = [[0.3, 0.5, 0.7] for _ in range(len(dels))]\n", + "counts = [[0, 0, 0] for _ in range(len(dels))]\n", + "for ds in dels:\n", + " gs.append(get_supports(ds, 0.5))\n", + "\n", + "# print(get_supports(0.5, 1))\n", + "\n", + "for i in range(3):\n", + " for j in range(3):\n", + " percent = pers[i][j]\n", + " for k in gs[i]:\n", + " if k > percent:\n", + " counts[i][j] += 1\n", + "\n", + "plt_graphs(pers, counts, labels=[\"del 0.3\", \"del 0.5\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For graph 2\n", + "gs = []\n", + "dels = [0.9, 0.7, 0.5]\n", + "pers = [[0.3, 0.5, 0.7] for _ in range(len(dels))]\n", + "counts = [[0, 0, 0] for _ in range(len(dels))]\n", + "for ds in dels:\n", + " gs.append(get_supports(ds, 0.5))\n", + "\n", + "# print(get_supports(0.5, 1))\n", + "\n", + "for i in range(3):\n", + " for j in range(3):\n", + " percent = pers[i][j]\n", + " for k in gs[i]:\n", + " if k > percent:\n", + " counts[i][j] += 1\n", + "\n", + "plt_graphs(pers, counts, labels=[\"del 0.3\", \"del 0.5\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + ] } ], "metadata": { @@ -484,7 +510,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.5" }, "pycharm": { "stem_cell": { From 43d65134e1e6a5c5b8993cded9108985701e4575 Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 04:06:47 +0530 Subject: [PATCH 04/13] all done --- DWDM .ipynb | 67 +++++++++++++++++++++++++++++++++++------------------ 1 file changed, 45 insertions(+), 22 deletions(-) diff --git a/DWDM .ipynb b/DWDM .ipynb index c16b749..208b0b4 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -21,13 +21,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[18.71148011698176, 21.08866492827954, 98.47286727914306, 948.7654104051413, 971.058175488234, 980.2666847459581]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[8.26190007992944, 231.43377796807852, 288.57207376228035, 393.20026101373907, 555.762816492401, 849.1041639045409]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" } ], "source": [ @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "[['2479', 87.0294811398571], ['21545477', 144.80374533004536], ['0440227660369838', 239.53135562861627], ['6639792690549748', 356.96087644275707], ['1247', 434.51060050020817], ['1992', 592.2727653361246], ['2535', 609.8641899810636], ['509577449337', 674.9647278296911], ['234347377188851892419486', 995.1919612584179]]\n" + "text": "[['2479', 15.492770154312607], ['21545477', 23.391842015568653], ['0440227660369838', 79.14141934541364], ['6639792690549748', 168.69894222291003], ['1247', 359.0942846017272], ['1992', 423.60767452783097], ['2535', 729.7298702352183], ['509577449337', 773.8626211377941], ['234347377188851892419486', 777.847023671456]]\n" } ], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -248,12 +248,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# function to calculate the time interval weight of the sequence\n", "def TiW_sequence(itemset_pairs_time_strength, delta=0.9, u=1, wf = 1):\n", + " print(wf)\n", " if wf == 1:\n", " weighting_function = weighting_function_1\n", " elif wf == 2:\n", @@ -272,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -362,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -380,13 +381,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "0.000749793044505429\n" + "text": "1\n0.0576375955011454\n" } ], "source": [ @@ -401,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -431,13 +432,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 36, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n[0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08]\n" + }, { "data": { - "image/png": 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" }, "metadata": { @@ -455,7 +461,7 @@ "for ds in dels:\n", " gs.append(get_supports(ds, 0.5))\n", "\n", - "# print(get_supports(0.5, 1))\n", + "print(get_supports(0.9, 0.5))\n", "\n", "for i in range(3):\n", " for j in range(3):\n", @@ -469,9 +475,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n" + }, + { + "data": { + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# For graph 2\n", "gs = []\n", From 95138ad76b05076f1c5c55b3176ea43157e01aba Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 04:21:04 +0530 Subject: [PATCH 05/13] almost set and done --- DWDM .ipynb | 69 ++++++++++++++++++++++++++++++++++------------------- 1 file changed, 45 insertions(+), 24 deletions(-) diff --git a/DWDM .ipynb b/DWDM .ipynb index 208b0b4..bb8b125 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +238,7 @@ " return(w_TI)\n", "\n", "def weighting_function_2(time_interval, delta=0.9, u=1):\n", - " w_TI=delta**math((1 + (time_interval/u)), 2)\n", + " w_TI=delta**math.log((1 + (time_interval/u)), 2)\n", " return(w_TI)\n", "\n", "def weighting_function_3(time_interval, delta=0.9, u=1):\n", @@ -254,7 +254,6 @@ "source": [ "# function to calculate the time interval weight of the sequence\n", "def TiW_sequence(itemset_pairs_time_strength, delta=0.9, u=1, wf = 1):\n", - " print(wf)\n", " if wf == 1:\n", " weighting_function = weighting_function_1\n", " elif wf == 2:\n", @@ -363,18 +362,40 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def nominal(TiWs, db):\n", + " n_supports = dict()\n", + " n_normalise = 0\n", + " for x in db.keys():\n", + " selected_sum = 0\n", + " n_normalise += 1\n", + " for s in db.keys():\n", + " if is_super_seq(db[x], db[s]):\n", + " selected_sum += 1\n", + " n_supports[x] = (selected_sum)\n", + "\n", + " for x in db.keys():\n", + " n_supports[x] /= n_normalise\n", + " return n_supports" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# def main(): main function\n", - "def get_supports(delta, u):\n", + "def get_supports(delta, u, wf = 1):\n", " TiW_db = dict()\n", " for i in database.keys():\n", " itemset_pairs=itemset_pair_generator(database[i])\n", " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", " itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", - " TiW_db[i]=TiW_sequence(itemset_pairs_time_strength, delta, u)\n", + " TiW_db[i]=TiW_sequence(itemset_pairs_time_strength, delta, u, wf)\n", " sups = TiW_support(TiW_db,database)\n", " return [sups[k] for k in sups.keys()]" ] @@ -432,18 +453,18 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n[0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08]\n" + "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n[0.6295623401489693, 0.06400334431495451, 1.1382365893827026e-05, 2.4106937423125545e-26, 0.06980274352729837, 0.16814142609183225, 9.173387044906773e-14, 6.018367177955828e-08, 0.06847870336728823]\n" }, { "data": { "image/png": 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" }, "metadata": { @@ -498,22 +519,22 @@ "source": [ "# For graph 2\n", "gs = []\n", - "dels = [0.9, 0.7, 0.5]\n", - "pers = [[0.3, 0.5, 0.7] for _ in range(len(dels))]\n", - "counts = [[0, 0, 0] for _ in range(len(dels))]\n", - "for ds in dels:\n", - " gs.append(get_supports(ds, 0.5))\n", + "WF = [1, 2, 3]\n", + "dels = [[1, 0.9, 0.7, 0.5] for _ in range(3)]\n", + "counts = [[0, 0, 0, 0] for _ in range(3)]\n", + "for wf in WF:\n", + " for dl in dels[0]:\n", + " gs.append(get_supports(ds, 0.5, wf))\n", "\n", - "# print(get_supports(0.5, 1))\n", + "print(gs)\n", "\n", "for i in range(3):\n", - " for j in range(3):\n", - " percent = pers[i][j]\n", - " for k in gs[i]:\n", - " if k > percent:\n", + " for j in range(4):\n", + " for k in gs[3*i + j]:\n", + " if k > 0.3:\n", " counts[i][j] += 1\n", "\n", - "plt_graphs(pers, counts, labels=[\"del 0.3\", \"del 0.5\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + "plt_graphs(dels, counts, labels=[\"WF_1\", \"WF_2\", \"WF_3\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" ] } ], From 20012a464bdf1e675b855373e2382fd5e55dae1c Mon Sep 17 00:00:00 2001 From: Sohamraje137 Date: Wed, 27 Nov 2019 10:29:49 +0530 Subject: [PATCH 06/13] Datasets added --- Datasets/eighty.data | 77 ++++++++++++++++++++++++++++++++++ Datasets/eighty.pat | 19 +++++++++ Datasets/fourty.data | 39 +++++++++++++++++ Datasets/fourty.pat | 19 +++++++++ Datasets/hundred.data | 97 +++++++++++++++++++++++++++++++++++++++++++ Datasets/hundred.pat | 19 +++++++++ Datasets/sixty.data | 59 ++++++++++++++++++++++++++ Datasets/sixty.pat | 19 +++++++++ Datasets/ten.data | 10 +++++ Datasets/ten.pat | 19 +++++++++ 10 files changed, 377 insertions(+) create mode 100644 Datasets/eighty.data create mode 100644 Datasets/eighty.pat create mode 100644 Datasets/fourty.data create mode 100644 Datasets/fourty.pat create mode 100644 Datasets/hundred.data create mode 100644 Datasets/hundred.pat create mode 100644 Datasets/sixty.data create mode 100644 Datasets/sixty.pat create mode 100644 Datasets/ten.data create mode 100644 Datasets/ten.pat diff --git a/Datasets/eighty.data b/Datasets/eighty.data new file mode 100644 index 0000000..d95ff2f --- /dev/null +++ b/Datasets/eighty.data @@ -0,0 +1,77 @@ +9 1 2479 2 2154 5477 4 440 2276 6036 9838 4 6639 7926 9054 9748 1 1247 1 1992 1 2535 3 5095 7744 9337 6 2343 4737 7188 8518 9241 9486 +9 3 765 1542 7203 3 5854 7309 8827 1 5993 4 991 4956 6376 7993 3 606 1798 8964 3 606 1091 2787 2 4023 5242 3 2451 6392 8259 5 703 3355 5892 7169 9239 +6 1 9033 3 1845 7713 8778 3 1285 1705 7890 3 4049 6908 7443 2 765 1739 2 3627 9693 +1 2 269 4701 +2 2 6273 7655 1 2654 +6 1 149 2 3533 9471 1 5758 1 1491 1 5024 1 3812 +8 3 3404 4764 6487 3 5210 7637 7771 3 1405 5501 9708 3 3572 3875 7705 3 2709 4167 5713 7 886 2870 6918 7963 8571 9125 9795 2 723 6970 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+Variation in repetition-level = 0.1 +Large Itemsets: + Number of patterns = 25000 + Average length of pattern = 1.25 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 +Large Sequences: + Number of patterns = 5000 + Average length of pattern = 4 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 + diff --git a/Datasets/fourty.data b/Datasets/fourty.data new file mode 100644 index 0000000..c6cbf6f --- /dev/null +++ b/Datasets/fourty.data @@ -0,0 +1,39 @@ +9 1 2479 2 2154 5477 4 440 2276 6036 9838 4 6639 7926 9054 9748 1 1247 1 1992 1 2535 3 5095 7744 9337 6 2343 4737 7188 8518 9241 9486 +9 3 765 1542 7203 3 5854 7309 8827 1 5993 4 991 4956 6376 7993 3 606 1798 8964 3 606 1091 2787 2 4023 5242 3 2451 6392 8259 5 703 3355 5892 7169 9239 +6 1 9033 3 1845 7713 8778 3 1285 1705 7890 3 4049 6908 7443 2 765 1739 2 3627 9693 +1 2 269 4701 +2 2 6273 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rule = 0.75 + Variation in the confidence = 0.1 + diff --git a/Datasets/hundred.data b/Datasets/hundred.data new file mode 100644 index 0000000..d213030 --- /dev/null +++ b/Datasets/hundred.data @@ -0,0 +1,97 @@ +9 1 2479 2 2154 5477 4 440 2276 6036 9838 4 6639 7926 9054 9748 1 1247 1 1992 1 2535 3 5095 7744 9337 6 2343 4737 7188 8518 9241 9486 +9 3 765 1542 7203 3 5854 7309 8827 1 5993 4 991 4956 6376 7993 3 606 1798 8964 3 606 1091 2787 2 4023 5242 3 2451 6392 8259 5 703 3355 5892 7169 9239 +6 1 9033 3 1845 7713 8778 3 1285 1705 7890 3 4049 6908 7443 2 765 1739 2 3627 9693 +1 2 269 4701 +2 2 6273 7655 1 2654 +6 1 149 2 3533 9471 1 5758 1 1491 1 5024 1 3812 +8 3 3404 4764 6487 3 5210 7637 7771 3 1405 5501 9708 3 3572 3875 7705 3 2709 4167 5713 7 886 2870 6918 7963 8571 9125 9795 2 723 6970 2 625 5975 +4 1 7851 1 2053 2 5826 8595 1 1734 +6 2 7213 7771 1 5510 2 4652 9373 1 9928 2 6771 7774 2 3113 8769 +5 2 765 6075 1 5735 1 3541 1 2715 1 2104 +3 1 1956 1 7093 2 298 2017 +7 1 3297 3 5577 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4652 5693 8796 9799 2 405 8595 3 5471 6673 9722 4 1092 1731 8051 9979 5 1922 2237 3483 5789 9925 +9 4 397 4956 5041 6677 6 1185 2249 5568 8475 8685 9185 4 612 1145 2052 9757 2 5288 8696 5 1130 5680 7275 9288 9775 2 1868 8289 3 2564 4702 6531 5 1630 1983 2475 3134 4594 5 265 1022 4219 5050 9593 +3 1 7832 1 1184 1 3068 +11 2 440 5650 3 4246 5311 8760 4 341 5252 6879 9082 2 161 2155 1 3718 4 1452 2137 4160 4976 1 7167 1 4047 1 8515 3 1887 5758 8816 2 4963 8884 +10 3 3278 3338 6685 3 2017 3572 4013 1 6491 2 2164 5096 4 2004 3278 9532 9590 2 299 2804 4 7992 8739 9886 9915 2 6861 8544 3 6657 8829 9030 3 3204 8269 9384 diff --git a/Datasets/hundred.pat b/Datasets/hundred.pat new file mode 100644 index 0000000..e9af54f --- /dev/null +++ b/Datasets/hundred.pat @@ -0,0 +1,19 @@ +Number of customers in database = 100 +Average sequence length = 10 +Average transaction length = 2.5 +Number of items = 10000 +Repetition-level = 0 +Variation in repetition-level = 0.1 +Large Itemsets: + Number of patterns = 25000 + Average length of pattern = 1.25 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 +Large Sequences: + Number of patterns = 5000 + Average length of pattern = 4 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 + diff --git a/Datasets/sixty.data b/Datasets/sixty.data new file mode 100644 index 0000000..f96c83e --- /dev/null +++ b/Datasets/sixty.data @@ -0,0 +1,59 @@ +9 1 2479 2 2154 5477 4 440 2276 6036 9838 4 6639 7926 9054 9748 1 1247 1 1992 1 2535 3 5095 7744 9337 6 2343 4737 7188 8518 9241 9486 +9 3 765 1542 7203 3 5854 7309 8827 1 5993 4 991 4956 6376 7993 3 606 1798 8964 3 606 1091 2787 2 4023 5242 3 2451 6392 8259 5 703 3355 5892 7169 9239 +6 1 9033 3 1845 7713 8778 3 1285 1705 7890 3 4049 6908 7443 2 765 1739 2 3627 9693 +1 2 269 4701 +2 2 6273 7655 1 2654 +6 1 149 2 3533 9471 1 5758 1 1491 1 5024 1 3812 +8 3 3404 4764 6487 3 5210 7637 7771 3 1405 5501 9708 3 3572 3875 7705 3 2709 4167 5713 7 886 2870 6918 7963 8571 9125 9795 2 723 6970 2 625 5975 +4 1 7851 1 2053 2 5826 8595 1 1734 +6 2 7213 7771 1 5510 2 4652 9373 1 9928 2 6771 7774 2 3113 8769 +5 2 765 6075 1 5735 1 3541 1 2715 1 2104 +3 1 1956 1 7093 2 298 2017 +7 1 3297 3 5577 5612 8526 3 3337 3692 8210 2 3279 8497 2 6052 7708 1 1258 1 6150 +9 1 4632 3 7612 8099 9393 4 1327 2055 5783 7642 5 4581 5060 6055 6901 8900 1 116 3 1510 3768 4435 1 140 4 4464 4664 6228 8283 3 2575 4565 6039 +8 7 2123 3096 3235 5029 8444 8567 8708 3 5550 5553 8445 4 3560 4804 7879 8034 2 4204 9124 4 327 3315 7615 7832 5 959 3023 6318 7616 8665 4 157 646 5661 8723 4 46 2051 2729 6037 +10 3 5869 5872 8093 2 312 4650 2 5783 6558 1 6116 3 494 2540 6711 1 3278 2 5193 5852 3 1917 4144 4474 4 1788 3629 4386 9542 4 2958 6708 7960 8983 +8 2 148 689 2 8663 9840 2 5675 7421 3 281 4646 5886 1 7924 3 1186 3023 7960 1 876 1 9731 +11 1 6201 1 3928 2 1345 4035 2 3811 9780 4 2177 5436 6150 7741 2 1710 8789 2 3708 6654 1 2702 2 3400 6061 4 2343 4627 4737 9487 1 4861 +4 1 1483 1 7835 1 8185 1 7218 +13 1 2217 3 5116 7988 9601 1 7211 3 1146 5819 9517 1 5724 1 611 1 9715 3 3441 8566 9551 2 7953 8373 2 1714 2410 1 9916 2 4149 7187 3 767 2285 3023 +7 2 617 9118 1 8204 3 1258 3825 7991 2 9387 9928 3 1504 6771 9845 2 8 381 1 7960 +6 4 2431 6365 9355 9567 1 4548 2 3592 4688 2 5309 6481 2 1805 3691 3 70 3613 4895 +7 1 5089 2 4177 7067 1 1051 2 3973 8280 3 1323 3697 9049 1 5982 2 3812 9296 +14 3 764 5329 5899 2 3386 6186 4 3342 5945 9075 9293 3 3143 5937 6944 3 2756 3331 4049 2 6034 9023 2 1098 5561 3 2375 8785 9983 5 1367 3927 4549 8724 9799 2 1279 5759 2 6376 7759 2 715 9674 2 765 2155 4 1521 3162 6392 8381 +6 3 3819 7192 9827 2 2547 2839 1 6529 3 3473 5357 7927 3 33 2341 5907 1 6537 +10 6 3428 3592 8911 9195 9238 9752 7 666 1767 4316 5598 7472 8404 9841 4 449 3923 5089 5535 4 1040 2634 5728 6674 3 173 1638 6305 4 647 4838 4901 6712 5 1464 5591 5710 7379 8258 3 2573 5777 8303 5 743 814 3586 4274 6773 5 70 3117 3125 4375 5689 +11 2 1060 6944 4 281 2991 6775 9198 5 188 1829 5649 5724 9114 2 3927 7455 4 1190 1513 3122 7450 4 619 1759 5779 7721 3 827 4252 7786 3 1566 3154 3342 5 562 1074 1386 5470 8625 3 365 6337 6672 3 2500 4613 4905 +8 3 2712 6256 8279 2 4085 5280 2 5885 6534 2 1018 5171 1 6640 2 7960 8774 1 5193 1 8865 +9 3 210 6045 8259 4 2967 3872 6147 7804 2 1779 8605 1 8457 4 1011 1038 4905 5052 3 3026 4488 9400 1 5365 4 647 2063 4642 8763 5 365 1826 2155 5278 9566 +6 5 514 833 1127 3789 6495 3 6244 6293 9128 6 1269 3790 5216 6564 8671 8773 6 3018 3292 4213 7661 8332 8492 4 249 2874 4098 5146 3 4770 5724 7379 +11 4 407 3874 4419 6962 4 3264 5843 6195 7269 6 91 145 276 6063 8096 9556 4 3795 3927 5199 8698 6 1704 3195 3461 4282 6503 7962 2 6317 8440 1 3559 2 1991 7101 7 8 738 3910 4626 4895 7524 8356 3 1146 3334 5199 3 62 4828 7516 +5 2 7146 7354 1 1610 1 3781 1 5478 1 2191 +9 2 2991 9841 2 2123 8567 2 2573 8068 1 7832 3 1759 7879 8278 2 5031 5974 2 3342 5329 2 4438 7980 2 5748 6086 +15 3 68 3696 5111 3 872 1709 8372 5 318 502 5427 5967 6990 6 1813 1985 2190 3665 3889 4751 4 881 2117 2979 9562 3 1767 4066 5907 2 1334 9952 5 1006 2938 4777 6784 9556 5 1038 2429 4905 5687 8825 2 3188 6754 3 986 1630 7313 3 841 1369 2599 5 253 5312 6084 9257 9575 3 1355 1725 4741 4 7827 8126 9482 9845 +5 2 7389 9657 2 2991 6225 2 1387 7938 1 2636 1 3696 +13 7 1367 2233 3099 5502 5854 8827 9184 5 244 4135 4464 7715 9863 5 2997 6393 7145 7804 9842 4 1500 5278 7309 7669 7 751 5851 6179 8052 8732 8990 9916 2 3064 5877 5 899 1339 1746 2019 3922 3 7867 9100 9475 8 430 767 1274 4654 6397 6513 7826 8512 5 2051 2769 3718 4333 6355 6 5964 6657 7293 7342 7985 9867 4 872 1338 6043 7909 6 624 3130 3270 3600 5889 9928 +5 3 1269 1692 8512 2 5759 8540 1 8578 3 1629 6286 7124 2 1216 8573 +5 1 551 2 1507 7615 3 2250 3045 9363 3 447 710 9982 2 4552 7920 +11 3 1724 7594 7696 3 1620 2928 6195 1 962 3 4588 5693 8785 1 2980 2 510 7510 3 2 6640 7537 4 469 2731 3879 6513 2 4555 6032 3 1404 5697 6106 2 744 5322 +16 2 2967 6147 4 1490 3010 7744 8303 1 1255 3 1011 4115 4459 2 4135 7613 1 3467 1 6711 2 1537 6643 2 3629 9542 1 6734 2 2132 7396 3 4508 5573 9488 2 3246 7317 2 3711 7635 1 4876 2 3812 9296 +10 2 5835 6804 2 5650 8723 1 7080 2 4246 8760 2 341 1805 4 827 3300 4976 7473 2 3771 4432 3 467 2485 6353 2 1067 4047 2 4056 5461 +10 2 3134 9119 3 4533 5261 5619 3 198 2988 6879 3 5559 6356 7534 3 3577 8032 9614 2 1669 7090 1 4001 2 6084 7220 2 7698 9662 1 229 +10 1 2963 5 1065 3331 4897 8285 8618 4 467 1304 2272 5479 4 127 5162 5308 7572 4 3093 3284 3627 7406 5 414 2065 6668 6914 9300 5 138 735 3345 4197 4594 4 2117 4085 5679 6508 4 45 1199 2654 9845 5 1816 2393 2977 7938 8080 +7 2 6962 9448 1 3726 2 8096 9556 1 1981 2 3927 5321 3 1704 2424 3461 3 3398 3559 6241 +6 1 7705 1 744 1 70 1 75 2 9705 9978 1 5270 +5 3 3491 3572 6810 3 1844 4570 9402 2 1339 5096 1 1801 1 1397 +13 3 6495 7101 8273 2 6228 8341 6 2505 2998 3212 5449 6131 7455 4 1312 3351 4098 7101 3 3790 4922 7375 4 4213 4654 5110 6771 8 1334 2031 2375 7227 7622 7708 8905 9463 4 5893 6092 8672 8837 4 681 1537 2082 3491 5 163 414 2182 3390 9229 8 138 272 3093 3279 4372 7455 8578 9062 5 117 293 1781 5893 8274 4 1563 5605 6879 7958 +12 5 1279 1792 3467 3986 9728 4 684 1397 1591 4953 3 7548 7823 8375 4 1917 5004 6119 8436 4 1831 2817 3851 5928 6 946 1338 2774 4386 5605 9764 3 2264 6150 7671 4 6918 7796 9288 9591 4 57 1345 4497 6725 4 1079 1491 2117 6804 6 303 2880 6228 8026 8181 8672 4 1005 2211 5499 7353 +7 2 6758 7101 2 2512 2998 4 1880 2880 3279 7930 2 3576 3661 3 3338 6614 8905 2 1844 2155 2 8274 8783 +6 3 6674 9287 9623 2 6761 7192 4 3315 4328 9193 9627 6 27 2395 3204 4534 6840 7926 1 6220 2 6804 9066 +8 1 9527 2 7440 7845 1 4908 2 1998 7269 1 1816 2 6092 8169 1 4477 1 6749 +10 6 1979 2421 6315 7594 7598 9913 6 212 294 845 3122 3934 7031 7 155 1276 2066 4737 4871 5024 9461 3 966 4961 7528 5 745 2164 3282 3668 5862 5 3022 3298 3341 5938 6045 5 271 1813 3226 3826 4929 6 1327 2052 4168 6924 8196 9532 4 116 2961 5787 6640 5 265 687 5729 7189 8488 +7 2 207 8595 3 2431 9355 9567 2 606 6960 2 265 8447 2 1304 2424 1 3613 1 8279 +16 2 6011 9364 3 3134 9119 9548 3 1337 5271 9752 2 1767 9695 1 4901 2 4905 8982 2 6499 7175 1 3577 1 4001 4 1301 1491 2903 3441 1 6919 2 3281 7220 1 6042 2 4419 9662 1 70 1 9054 +6 1 874 1 9934 1 3984 1 8285 1 1877 1 5471 +2 1 1362 1 8404 +11 5 1345 2766 7696 8617 9511 5 2967 4734 5826 6147 6195 9 542 1011 1859 3066 3134 4773 6241 8605 9119 5 2980 4463 6886 7136 9000 4 1150 5311 6743 8788 5 872 2181 4533 6225 7510 8 365 2071 3289 3577 3708 5422 6654 7771 4 2 1867 4001 9889 5 1828 3899 6106 6818 7123 3 5935 7392 9153 6 34 1294 4469 5378 6718 7220 +11 1 7203 1 3461 3 139 4423 9448 3 1709 3726 7653 2 229 4181 3 2451 3665 5321 3 2424 5309 8825 1 3355 1 790 1 7827 1 6241 +13 5 619 3592 4202 6782 7986 7 430 1146 1798 6111 6674 7183 9517 5 304 1880 4252 8279 8732 4 2370 6042 9170 9431 5 6376 6771 8274 8289 9043 5 3338 3835 6437 6795 7396 3 1609 4669 6775 7 44 1714 2410 6629 7560 8351 8440 7 1018 1092 1731 1991 4664 5171 6474 8 188 1504 1806 1954 7192 8902 9852 9916 10 183 210 548 738 1234 3551 4149 5924 8393 9954 5 2073 2658 5226 5724 7443 3 2285 5249 6953 +7 4 606 2766 5441 7145 7 1638 3188 3606 4024 4626 6531 9668 5 3311 4296 5373 7170 7823 5 1274 7098 7683 8071 9836 5 950 7146 7420 7721 9613 8 1918 1930 3680 3889 4751 5928 6899 7002 5 3334 4876 5366 5479 7379 diff --git a/Datasets/sixty.pat b/Datasets/sixty.pat new file mode 100644 index 0000000..84bd12c --- /dev/null +++ b/Datasets/sixty.pat @@ -0,0 +1,19 @@ +Number of customers in database = 60 +Average sequence length = 10 +Average transaction length = 2.5 +Number of items = 10000 +Repetition-level = 0 +Variation in repetition-level = 0.1 +Large Itemsets: + Number of patterns = 25000 + Average length of pattern = 1.25 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 +Large Sequences: + Number of patterns = 5000 + Average length of pattern = 4 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 + diff --git a/Datasets/ten.data b/Datasets/ten.data new file mode 100644 index 0000000..e0c435a --- /dev/null +++ b/Datasets/ten.data @@ -0,0 +1,10 @@ +9 1 2479 2 2154 5477 4 440 2276 6036 9838 4 6639 7926 9054 9748 1 1247 1 1992 1 2535 3 5095 7744 9337 6 2343 4737 7188 8518 9241 9486 +9 3 765 1542 7203 3 5854 7309 8827 1 5993 4 991 4956 6376 7993 3 606 1798 8964 3 606 1091 2787 2 4023 5242 3 2451 6392 8259 5 703 3355 5892 7169 9239 +6 1 9033 3 1845 7713 8778 3 1285 1705 7890 3 4049 6908 7443 2 765 1739 2 3627 9693 +1 2 269 4701 +2 2 6273 7655 1 2654 +6 1 149 2 3533 9471 1 5758 1 1491 1 5024 1 3812 +8 3 3404 4764 6487 3 5210 7637 7771 3 1405 5501 9708 3 3572 3875 7705 3 2709 4167 5713 7 886 2870 6918 7963 8571 9125 9795 2 723 6970 2 625 5975 +4 1 7851 1 2053 2 5826 8595 1 1734 +6 2 7213 7771 1 5510 2 4652 9373 1 9928 2 6771 7774 2 3113 8769 +5 2 765 6075 1 5735 1 3541 1 2715 1 2104 diff --git a/Datasets/ten.pat b/Datasets/ten.pat new file mode 100644 index 0000000..4a2be20 --- /dev/null +++ b/Datasets/ten.pat @@ -0,0 +1,19 @@ +Number of customers in database = 10 +Average sequence length = 10 +Average transaction length = 2.5 +Number of items = 10000 +Repetition-level = 0 +Variation in repetition-level = 0.1 +Large Itemsets: + Number of patterns = 25000 + Average length of pattern = 1.25 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 +Large Sequences: + Number of patterns = 5000 + Average length of pattern = 4 + Correlation between consecutive patterns = 0.25 + Average confidence in a rule = 0.75 + Variation in the confidence = 0.1 + From d81a424edbabb5cfadc11487e9a6e9432357bba4 Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 10:52:21 +0530 Subject: [PATCH 07/13] no problem with graph 1 --- DWDM .ipynb | 76 +++++++++++++++++++++++------------------------------ 1 file changed, 33 insertions(+), 43 deletions(-) diff --git a/DWDM .ipynb b/DWDM .ipynb index bb8b125..d8c6974 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -21,13 +21,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[8.26190007992944, 231.43377796807852, 288.57207376228035, 393.20026101373907, 555.762816492401, 849.1041639045409]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[98.54525150040439, 213.4393359715151, 336.7893581280956, 497.34713226725825, 901.5305270407393, 914.5416050807784]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" } ], "source": [ @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "[['2479', 15.492770154312607], ['21545477', 23.391842015568653], ['0440227660369838', 79.14141934541364], ['6639792690549748', 168.69894222291003], ['1247', 359.0942846017272], ['1992', 423.60767452783097], ['2535', 729.7298702352183], ['509577449337', 773.8626211377941], ['234347377188851892419486', 777.847023671456]]\n" + "text": "{'0': [['2479', 40.00737599075366], ['21545477', 207.93653671490975], ['0440227660369838', 210.59121194306485], ['6639792690549748', 390.52918604998723], ['1247', 499.6514793463094], ['1992', 675.9269612509048], ['2535', 846.3626284334488], ['509577449337', 880.1776746649838], ['234347377188851892419486', 905.1158710256487]], '1': [['076515427203', 198.17246031770043], ['585473098827', 209.37086169919573], ['5993', 224.43726769302586], ['0991495663767993', 296.59760194727767], ['060617988964', 381.3216245605809], ['060610912787', 428.2578933478413], ['40235242', 516.7967922961599], ['245163928259', 635.5326972991845], ['07033355589271699239', 890.9975971954083]], '2': [['9033', 98.54525150040439], ['184577138778', 213.4393359715151], ['128517057890', 336.7893581280956], ['404969087443', 497.34713226725825], ['07651739', 901.5305270407393], ['36279693', 914.5416050807784]], '4': [['62737655', 74.78656445870836], ['2654', 670.4666496743686]], '5': [['0149', 217.072114031018], ['35339471', 388.7343158529507], ['5758', 644.6361582227731], ['1491', 672.6688406109625], ['5024', 878.1316190289217], ['3812', 962.316082781499]], '6': [['340447646487', 122.48512842842652], ['521076377771', 374.0582855374077], ['140555019708', 551.6479565188292], ['357238757705', 670.1674420126049], ['270941675713', 680.5630918693074], ['0886287069187963857191259795', 804.2488843160671], ['07236970', 864.7328790313439], ['06255975', 999.9196067474759]], '7': [['7851', 224.19765736798604], ['2053', 616.770183912852], ['58268595', 681.0509296430255], ['1734', 930.6983371093326]], '8': [['72137771', 33.45785376305355], ['5510', 257.7641036658326], ['46529373', 291.98023790741854], ['9928', 568.4965480433992], ['67717774', 659.4182474130035], ['31138769', 859.6781144900726]], '9': [['07656075', 59.47056507535697], ['5735', 401.69975628861897], ['3541', 504.9773205369124], ['2715', 680.6806209664097], ['2104', 726.1765334081601]]}\n" } ], "source": [ @@ -159,12 +159,12 @@ "# for j in database[i]:\n", "# print(j[1],end=' ')\n", "# print(database['0'])\n", - "print(database['0'])" + "print(database)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -402,13 +402,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "1\n0.0576375955011454\n" + "text": "0.01078052840079274\n" } ], "source": [ @@ -423,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -453,18 +453,13 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 29, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n[0.6295623401489693, 0.06400334431495451, 1.1382365893827026e-05, 2.4106937423125545e-26, 0.06980274352729837, 0.16814142609183225, 9.173387044906773e-14, 6.018367177955828e-08, 0.06847870336728823]\n" - }, { "data": { - "image/png": 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" }, "metadata": { @@ -476,38 +471,33 @@ "source": [ "# For graph 1\n", "gs = []\n", - "dels = [0.9, 0.7, 0.5]\n", - "pers = [[0.3, 0.5, 0.7] for _ in range(len(dels))]\n", - "counts = [[0, 0, 0] for _ in range(len(dels))]\n", + "dels = [0.5, 0.7, 0.9]\n", + "pers = [[0.2, 0.3, 0.5, 0.7, 1] for _ in range(len(dels))]\n", + "counts = [[0, 0, 0, 0, 0] for _ in range(len(dels))]\n", "for ds in dels:\n", " gs.append(get_supports(ds, 0.5))\n", "\n", - "print(get_supports(0.9, 0.5))\n", + "# print(get_supports(0.9, 0.5))\n", "\n", "for i in range(3):\n", - " for j in range(3):\n", - " percent = pers[i][j]\n", + " for j in range(5):\n", + " percent = pers[i][j]/100\n", " for k in gs[i]:\n", " if k > percent:\n", " counts[i][j] += 1\n", "\n", - "plt_graphs(pers, counts, labels=[\"del 0.3\", \"del 0.5\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + "plt_graphs(pers, counts, labels=[\"del 0.5\", \"del 0.7\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": "1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n1\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n2\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n3\n[[0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08], [0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08], [0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08], [0.9888019243212202, 0.007297417598115534, 1.3655187408799638e-32, 6.776385467884207e-174, 1.0219530866808834e-05, 0.003890412241745201, 8.290163529238275e-86, 1.503306565165979e-45, 2.630805250174863e-08], [0.29787831622928973, 0.10998404415793075, 0.07288173998819697, 0.04312801605926226, 0.11815976256806271, 0.12675007603117472, 0.03722235238630999, 0.048272653466542866, 0.14572303911322995], [0.29787831622928973, 0.10998404415793075, 0.07288173998819697, 0.04312801605926226, 0.11815976256806271, 0.12675007603117472, 0.03722235238630999, 0.048272653466542866, 0.14572303911322995], [0.29787831622928973, 0.10998404415793075, 0.07288173998819697, 0.04312801605926226, 0.11815976256806271, 0.12675007603117472, 0.03722235238630999, 0.048272653466542866, 0.14572303911322995], [0.29787831622928973, 0.10998404415793075, 0.07288173998819697, 0.04312801605926226, 0.11815976256806271, 0.12675007603117472, 0.03722235238630999, 0.048272653466542866, 0.14572303911322995], [0.992862087960294, 0.004796233097276124, 8.486408409788647e-33, 6.186050379250821e-174, 9.338368675738258e-06, 0.002332314518038043, 4.375419157622053e-86, 8.366768006269914e-46, 2.6055716171144692e-08], [0.992862087960294, 0.004796233097276124, 8.486408409788647e-33, 6.186050379250821e-174, 9.338368675738258e-06, 0.002332314518038043, 4.375419157622053e-86, 8.366768006269914e-46, 2.6055716171144692e-08], [0.992862087960294, 0.004796233097276124, 8.486408409788647e-33, 6.186050379250821e-174, 9.338368675738258e-06, 0.002332314518038043, 4.375419157622053e-86, 8.366768006269914e-46, 2.6055716171144692e-08], [0.992862087960294, 0.004796233097276124, 8.486408409788647e-33, 6.186050379250821e-174, 9.338368675738258e-06, 0.002332314518038043, 4.375419157622053e-86, 8.366768006269914e-46, 2.6055716171144692e-08]]\n" - }, { "data": { - "image/png": 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6C/bay4rSZZBVm1alRZG5aJUmAlUtw+YuNsYfeXkwdizMnu3mLzBpb0eRuS4ZlAg8b4rIGZLqxTKMSUdnn+3uL/jTn2DFiqCjMTUUCodom9eWg/Y+KOhQqiWe4aPXA3sA20XkF0AAVdVGvkZWDaWlpRQXF7Mly27dr1evHvn5+dSubcVg01akKF23bnD99fDPfwYdkdlNm0s389Y3b3HJwZekfJG5aFUmAlVtmIxAaqK4uJiGDRtSUFCQdh/A7lJV1q5dS3FxMe3btw86HFMTHTu6K4I77oD//V93hWDSzlvfvOWKzKVZsxDEd2exiMgFIjLCe91GRA6PZ+ciMlBEwiKySERurmCbs0XkKxGZJyL/qF74zpYtW2jWrFnWJAEAEaFZs2ZZdxWUsW66ySWEK6+0onRpqjBcSF7dPPq1S/0ic9Hi6SN4DOgDnOe93giMq+pNIpLjbTcIV7b6XBHpGrVNR1xBu76qegBwbfyh73K83X1r2srGc85Ydeu6onSLFsGf/xx0NKaayheZq52Tfk218SSCXqo6DNgCoKo/AfGU0zscWOQVqSsBXgSir5kuA8Z5+0RVV8UduTGZ5thj4bzz4O67XZVSkzZmFM9IqyJz0eJJBKXer3sFEJEWQFkc72sNfF/udbG3rLxOQCcR+UhEZojIwFg7EpGhIjJLRGatXr06jkMn13XXXcfYsWN3vD7hhBO49NJLd7y+4YYbeOCBB6hfvz49evTY8SgpKYm5vwULFtCnTx/q1q3LfffZ6N2scv/9UL++ayKyonRpIxQOUbtWbQbuF/MrLOXFkwgeBl4F9hKRMcA04O443her3SL6X3Yu0BHoD5wLPCUijXd5k+p4Ve2pqj1btGgRx6GrVlTkfngVFdV8X0cccQTTp08HoKysjDVr1jBv3rwd66dPn07fvn3Zd999+fzzz3c86tSJfWHVtGlTHn74YW688caaB2fSS8uWrkz122/Diy8GHY2JU7oVmYsWz6ihCSIyGxiA+3L/rarOj2PfxUCbcq/zgeUxtpmhqqXAtyISxiWGmfEEH8u118Lnn1e+zbp1MGcOlJVBrVrQvbu7t6ciPXq4+34q0rdvX6677joA5s2bx4EHHsiKFSv46aefaNCgAfPnz6dJkyZxn8Nee+3FXnvtxeuvv171xibz/P738NxzbjjpoEHQeJffRiaFLFizgIVrF3L14ek7TUs8o4ZeUNUFqjpOVR9V1fki8kIc+54JdBSR9iJSBzgHKIza5jXgaO84zXFNRYurdwrVt26dSwLg/qxpAchWrVqRm5vL0qVLmT59On369KFXr14UFRUxa9YsunfvTp06dfjmm292NAsNGzas5idiMlNOjrvTeNUquO22oKMxVSgMu6+1dCoyFy2eG8oOKP/C6y84tKo3qeo2EbkKmALkAM94U12OAmapaqG37ngR+Qo318EfVHVtdU+ivMp+uUcUFcGAAVBSAnXqwIQJ0KdPTY7qrgqmT5/O9OnTuf7661m2bBnTp08nLy+PI444AmBH05AxVTrkELjqKnjkERgyBA47LOiITAVC4RCH7HMIbfLaVL1xiqrwikBEbhGRDUB3EVnvPTYAq9j1l31MqjpZVTup6r6qOsZbdruXBFDnelXtqqrdVDUpjaJ9+sDUqTB6tPuzpkkAfu0nmDt3LgceeCC9e/emqKhoR/+AMdU2erTrM7CidCnrh40/UPR9UdqOFoqoMBGo6t3eXcX3qmoj79FQVZupasybw9JJnz5wyy2JSQLgrggmTZpE06ZNycnJoWnTpvz8888UFRXRJ1EHMdmlUSN3ifvpp+4eA5NyIkXm0rlZCOIbNbTLXcQiMtWHWNJat27dWLNmDb17995pWV5eHs2bN6/WvlauXEl+fj4PPPAAd911F/n5+axfvz7RIZt0cNZZcMIJcOutsDx6rIUJWigcol1eu7QrMhetwj4CEamHKzbXXESa8Otw0EZAqyTEllZycnJ2+bJ+7rnndjwvKCjgyy+/jGtfLVu2pLi4OJHhmXQVKUp3wAFuFJENKU0Zm0o28dbit7jskMvS/i7/yq4Ifo+biawLv85MNhsIEUeJCWNMguy7r7si+Ne/YMqUoKMxnrcXv82WbVvSvn8AKu8jeEhV2wM3qmqHcrOTHaSqjyYxxoz17LPP7nSnsQ0rNRX64x+hUycYNgx++SXoaAyuWahxvcYc1e6ooEOpsXhuKHtERA7EFY6rV2753/wMLBtcfPHFXHzxxUGHYdJB3brw+ONu3POf/wx33hl0RFlte9l2Ji2clLZF5qLFc0PZHcAj3uNo4B4gvbvIjUlHxxwD55/vEkE4HHQ0Wa2ouIjVm1czuFNmfBXGM2roTFx5iZWqejFwEFDX16iMMbFZUbqUEFrgiswN6jgo6FASIp5E8Is3if02EWmEu6Gsg79hGWNi2ntvd0Xwzjs2rWVAVJVQOMTR7Y+mUd2UmbG3RuJJBLO8iqBP4kYNfQp84mtUxpiKDR0Khx8O110HP/0UdDRZJ7w2zNc/fp0Ro4UiqkwEqnqlqv6sqk8AxwFDvCaitDVh7gQKxhZQ685aFIwtYMLcCTXaX6LnI5gwYQLdu3ene/fuHHHEEXzxxRc1is9kmFq1XFG6NWvcsFKTVKEFISC9i8xFi+eKABE5XUQeAIYD+/obkr8mzJ3A0IlDWbJuCYqyZN0Shk4cWqNkkOj5CNq3b8/777/PnDlzGDFiBEOHDt3t2EyGOvhguPpqlxA+sQv0ZAqFQxy6z6HkN8oPOpSEqXL4qIg8BuwHRBokfy8ix3rTV6aca9+4ls9XVlzhc0bxDLZu37rTss2lm7kkdAlPzn4y5nt6tOzB2IEVlzVN9HwEkWqlAL1797a7jE1so0bBSy+5onSffAK58RQTNjXxw8YfmFE8g5H9RwYdSkLF8y+nH3CgqkamqnwemOtrVD6KTgJVLY9HrPkIli1bRlFREXl5ebvMRwAueYwbV/UN2k8//TSDBmXGyASTYA0bwkMPuXpE48bBNdcEHVHGm7hwIopmVP8AxJcIwkBbYIn3ug0wx7eIaqiyX+4ABWMLWLJuyS7L2+W1472L3tvt4/oxH8G7777L008/zbRp03Y7LpPhzjjDzWI2YgSceSa0jp4W3CRSpMhc9727Bx1KQsXTR9AMmC8i74nIe8BXQAsRKRSRuOYlSCVjBoyhQe0GOy1rULsBYwaMqdF+Ez0fwZw5c7j00ksJhUI0a9asRrGZDCYCjz4KpaVuFJHxzaaSTby9+G1O7Xxq2heZixbPFcHtvkeRROd3Ox+AW6feytJ1S2mb15YxA8bsWL67+vbty/3330+HDh12mo9g3rx5PPnkk2zcuDHufS1dupTTTz+dF154gU6dOtUoLpMFOnRwU1redhu88QYMHBh0RBnprcVvuSJzXTKrWQjiqzX0fjICSabzu51f4y/+aJH5CM4777ydlm3cuJHmzZtXKxGMGjWKtWvXcuWVVwKQm5vLrFmzEhqvyTA33gh//7srSvfll+7uY5NQkSJzv2n7m6BDSTjRNLtNvWfPnhr9pTh//nz233//gCIKVjafu4ny7ruuHtFtt7lpLk3CbC/bTsv7W3L8vscz4fSa3XcUFBGZrao9Y62L6z4CY0waOPpouPBC+MtfYMGCoKPJKNO/n86azWsybrRQRGWT10/1/vxL8sLJLjYfgUm4++6DPfawonQJFgq7InMD98vM/pfK+gj2EZF+wGAReZFfp6oEQFU/9TWyLGDzEZiE22svV5Tu8sthwgS44IKgI0p7kSJzx7Q/JmOKzEWrLBHcDtwM5AMPRK1T4Bi/gjLG1MBll8Fzz7k5jk86CapxV7vZ1YI1C1j04yKu73190KH4prKpKl9W1UHAPap6dNTDkoAxqSpSlO7HH+FPfwo6mrQXCrsic6d0PiXgSPwTT/XR0SIyWETu8x4nJyMwY0wNHHSQK0r317/CjBlBR5PWMrHIXLR4pqq8G7gGd0fxV8A13jJjTCq7805o1cr1F2zbFnQ0aWnlxpV8XPxxxo4Wiohn+OhJwHGq+oyqPgMM9JaltxUroF8/WLmyxrtK9HwEoVCI7t2706NHD3r27Gm1hszuadgQHn4YvvjClaEw1TYx7BWZy8C7icuL9z6CxuWe5/kRSNKNHg3TpiXkxptEz0cwYMAAvvjiCz7//HOeeeaZnZKKMdVy2mlw4omuKJ2VM6+2woWFFDQuoNte3YIOxVfx1Bq6G/hMRN7FDSE9CrjF16hq4tproaoKn1u3uvrtZWWuU+2zz6CCL2UAevSAscmbj2DPPffc8XzTpk0ZV+DKJFGkKF3Xru7/xssvBx1R2ogUmfv9ob/P+P+D8XQW/xPoDfzHe/RR1Rf9DsxXS5b8erONqntdA7HmI+jVqxdFRUXMmjVrl/kI4rlx7NVXX6VLly6cdNJJPPPMMzWKz2S59u3h9tvhlVdg8uSgo0kbb37zpisyl+H9AxDfFQGqugJIj5LTlfxyB1zfQIcOOyeCn36CF1+Eli13+7CJno/gtNNO47TTTuODDz5gxIgRvP3227sdmzHccAO88AJcdZUrStegQdXvyXKRInNHtj0y6FB852utIREZKCJhEVkkIjdXst2ZIqIiErMgUkKNHu2ahMrbvr3GfQWJno8g4qijjuKbb75hzZo1NYrPZLk6deDxx+Hbb2FMzebeyAbbyrYxaeEkTup4ErVzagcdju98SwQikgOMAwYBXYFzRaRrjO0aAlcDH/sVy06KiiB6tE5JCXidvburb9++TJo0iaZNm+40H0FRURF9+vSp1r4WLVpEpCrsp59+SklJiU1OY2quXz8YMgTuvRfmzw86mpQ2/fvprP1lbVY0C0EViUBEaonIl7u578OBRaq6WFVLgBeBWH+ro4F7gC27eZzq+ewz1xwU/fjssxrtNjIfQe/evXdalpeXR/Pmzau1r1deeYUDDzxwR1/Cv/71r4zvrDJJcu+9sOeecMUVVpSuEoXhQurk1MnYInPRKu0jUNUyEflCRNqq6tJq7rs18H2518VAr/IbiMjBQBtVnSQiN1a0IxEZCgwFaNu2bTXDSI6cnBzWr1+/07Lnnntux/OCggK+/DK+nHrTTTdx0003JTI8Y5wWLVyZ6qFDXZ/B734XdEQpp3yRuYZ1GwYdTlLE0zS0DzBPRKZG5imOc67iWD9hd/wEEZFawIPADVXtSFXHq2pPVe3ZokWLOA5tjKnQJZdAnz5uVrMffww6mpQzf818Fv24KGuahSC+UUN37ua+i4E25V7nA8vLvW4IHAi85zV7tAQKRWSwqmbFvIzPPvssDz300E7L+vbty7hx4wKKyGSFSFG6Qw6BW25x9YjMDqEFXpG5TplbZC5aXHMWi0g7oKOqvi0iDYCcOPY9E+goIu2BZcA5wI4JfVV1HbCj8VxE3gNu3N0koKpp145e0/kI0m2aUZNCund3N5jdfz9cdJG7QjCAGzbas1VPWjdqHXQoSRNP0bnLgJeByM+G1sBrVb1PVbcBVwFTgPnAS6o6T0RGicjg3Q95V/Xq1WPt2rVZ9cWoqqxdu5Z69eoFHYpJVyNHQn6+FaUrZ8WGFXy8LPOLzEWLp2loGG4E0McAqvq1iOwVz85VdTIwOWrZ7RVs2z+efcaSn59PcXExq1ev3t1dpKV69eqRn5+5pXGNz/bc0xWlO/109+f1mTvxSrwmLZwEYIkghq2qWhJpdhGRXMp1+qaC2rVr0759+6DDMCb9/Pa3cPLJrgTFWWdBmzZVvyeDhcIh2jduz4F7HRh0KEkVz6ih90XkT0B9ETkO+Dcw0d+wjDFJIQKPPOLutr/22qCjCdTGko28vfhtTu18atr1N9ZUPIngZmA1MBf4Pa6p5zY/gzLGJFFBgbsi+M9/YNKkoKMJzJvfvMnW7VsZ3DmhXZhpIZ5RQ2Ui8jyuj0CBsGZTr6wx2eD6690NZsOHwzHHZGVRulA4RJN6TfhNu98EHUrSxTNq6CTgG+Bh4FFgkYgM8jswY0wSRYrSffcd3HVX0NEk3Y4ic51OIrdWXEWZM0o8TUP3A0eran9V7Qccjbsj2BiTSY46yt1TcO+9UG6GvWww/fvp/PjLj1k3WiginkSwSlUXlXd05H0AABU4SURBVHu9GFjlUzzGmCDdcw80agRXXplVRelCC0LUyanDCfueEHQogagwEYjI6SJyOq7O0GQRuUhEhuBGDM1MWoTGmORp0cIlgw8+gL/9LehokiJSZG5A+wFZU2QuWmVXBKd4j3rAD0A/oD9uBFH8E/AaY9LLxRfDEUe4onRr1wYdje++Wv0V3/z0TVaOFoqosFdEVXe/CI4xJn1FitIdfDDcfDM8+WTQEfkqFHZF5rI5EcQzaqi9iDwgIv+pZhlqY0y66tbNDSl96qkaz96X6grDhRzW6jBaNWwVdCiBiaez+DXgO+AR3AiiyMMYk8luv92VnLj8cigtDToaX2Rrkblo8SSCLar6sKq+q6rvRx6+R2aMCdaee7ryE3PnQtS8GZli4kJXLefULpYIqvKQiNwhIn1E5JDIw/fIjDHBO/VUGDzYlaxeWt3ZalNfKByiQ5MOHNDigKBDCVQ8t9B1Ay4EjgHKvGXqvTbGZLqHH4auXeGaa+DVV4OOJmE2lmxk6uKpXNHziqwrMhctnkRwGtBBVUv8DsYYk4LatYM77oCbboKJE+GUzJjCccqiKWzdvjXrm4UgvqahL4DGfgdijElh110HBxzgitJt2hR0NAlRuLCQpvWbcmTbI4MOJXDxJIK9gQUiMsWGjxqTpWrXdvcWLFkCo0cHHU2N7Sgy1zE7i8xFi+dv4A7fozDGpL4jj4T//V834f0FF8CB6TuL10dLP8rqInPR4pmPwIaKGmOcv/wFQiG44gp4/313F3IaCoVD1M2pywn7ZWeRuWjx3Fm8QUTWe48tIrJdRNYnIzhjTIpp3twVpZs2DZ5/PuhodsuOInMdBrBnnT2DDiclVJkIVLWhqjbyHvWAM3AT1BhjstFFF7lmoj/8IS2L0s1bPY/FPy1mcKfsrS0UrdrXdar6GnYPgTHZq1YtN5vZunVuSGmaKQy7sS6ndM6MYbCJUGUfgTcnQUQtoCfuhjJjTLY68EBXlO6ee369QkgToXCIw1sfntVF5qLFc0VwSrnHCcAGwLrajcl2t98Obdu6juM0KUq3fMNyPln2iY0WihLPqCGbl8AYs6s99oBHH3W1iMaOdX0GKW5i2CsyZ4lgJxUmAhG5vZL3qaqm/10lxpiaOeUUV5hu5Eg4+2xXjiKFhcIh9m2yL11bdA06lJRSWdPQphgPgEuA9OshMsb44+GH3Z9XXx1sHFXYsHUDU7+dyuDOg7O+yFy0ChOBqt4feQDjgfrAxcCLQIckxWeMSXVt28Kdd0JhobvZLEW9+c2blGwvsWahGCrtLBaRpiJyFzAH14x0iKrepKqrkhKdMSY9XHONG0k0fDhs3Bh0NDGFwiGa1m9K37Z9gw4l5VSYCETkXmAmbpRQN1Udqao/VWfnIjJQRMIiskhEbo6x/noR+UpE5ojIVBFJ7QZGY0xskaJ0338Po0YFHc0utpVt4/WvX+fkTidbkbkYKrsiuAFoBdwGLC9XZmJDPCUmRCQHGAcMAroC54pIdA/NZ0BPVe0OvAzcszsnYYxJAX37wqWXwgMPuOktU8i0pdOsyFwlKusjqKWq9aNKTDSKvI5j34cDi1R1sTepzYtE3X/gzYO82Xs5A8jf3RMxxqSAP/8ZmjRx9xaUlVW9fZKEFrgic8fve3zQoaQkP0sHtga+L/e62FtWkUuA/8ZaISJDRWSWiMxavXp1AkM0xiRUs2Zw773w0Ufw7LNBRwNYkbl4+JkIYo3PilmaQkQuwJWuuDfWelUdr6o9VbVnixYtEhiiMSbhhgyBo46CP/4R1qwJOhrmrZ7Htz9/a81ClfAzERQDbcq9zgeWR28kIscCtwKDVXWrj/EYY5JBBB57DNavd8kgYKEFbkjrKZ2syFxF/EwEM4GOItJeROoA5wA7TXEpIgcDf8UlARuSakymOOAAuPFG1zz0wQeBhhIKh+jVuhf7NNwn0DhSmW+JQFW3AVcBU4D5wEuqOk9ERolIpBD4vcCewL9F5HObC9mYDDJiBBQUuI7jkpJAQli+YTkzl8+0ZqEq+DqgVlUnA5Ojlt1e7vmxfh7fGBOgBg3gkUdcPaIHHwxk7oLI3AOndrFEUJn0nHDUGJMeTj4ZTjvNlaD47rukHz5SZG7/5vsn/djpxBKBMcZfDz3kZjUbPhw0eXNabdi6gXe+fYdTO59qReaqYInAGOOvNm3cFcGkSUktSjflmymuyJw1C1XJEoExxn9XXw3duye1KF0oHKJZ/WYc0eaIpBwvnVkiMMb4L1KUrrjYTWLjs9Ltpby+0IrMxcsSgTEmOfr0gcsuc9Nazpnj66GmLZ3GT1t+smGjcbJEYIxJnkhRussv97UoXSjsiswdt+9xvh0jk1giMMYkT9OmcP/9UFQETz/tyyFUlcJwIcd2ONaKzMXJEoExJrkuvBD69XM3mPlQTfjLVV9akblqskRgjEkuEXj8cTd66A9/SPjuQ+EQgnBKZysyFy9LBMaY5Nt/f5cEnn8e3n8/obsOhUP0yu9Fyz1bJnS/mcwSgTEmGLfemvCidMvWL2PW8lnWLFRNlgiMMcFo0ADGjYP5810HcgJEiswN7jy4ii1NeZYIjDHBOfFEOOMMGDUKvv22xrsrXFjIfk33syJz1WSJwBgTrLFjITcXrrqqRkXprMjc7rNEYIwJVn6+uyKYPBlefXW3d/PGojdckTnrH6g2SwTGmOANHw4HHeSK023YsFu7CIVDNG/Q3IrM7QZLBMaY4OXmuqJ0y5fvVlG60u2lvP61KzKXUysn8fFlOEsExpjU0Ls3DB3qJrL5/PNqvfXDpR/y85afGdzJRgvtDksExpjUcffdrh7RFVdUqyhdYbiQern1OH7f430MLnNZIjDGpI4mTdw9BTNmwFNPxfUWVSUUDnFsh2PZo84ePgeYmSwRGGNSywUXQP/+rijdqlVVbj531Vy++/k7Gy1UA5YIjDGpJVKUbtMmuPHGKjcPLfCKzHWyInO7yxKBMSb1dOkCf/wjvPACvPtupZuGwiF65/dm7z33TlJwmccSgTEmNd16K3To4DqOt26NuUnx+mJmr5httYVqyBKBMSY11a/vitKFw3DffTE3mRieCGD9AzVkicAYk7oGDoQzz4S77oLFi3dZHQqH6Ni0I12adwkguMxhicAYk9oiRemGDdupKN36reutyFyCWCIwxqS21q3dFcEbb8Arr+xY/MaiNygtK+XULtYsVFOWCIwxqW/YMOjRA665ZkdRukiRuT75fQIOLv1ZIjDGpL5IUboVK+D22yndXsrkrydbkbkE8TURiMhAEQmLyCIRuTnG+roi8i9v/cciUuBHHBPmTqBgbAG17qxFwdgCJsyd4MdhTMDsc85wvXrB5ZdT9vBDnHdFC157/Gdmzp6YPZ/zihXQrx+sXJnwXfuWCEQkBxgHDAK6AueKSNeozS4BflLV/YAHgb8kOo4JcycwdOJQlqxbgqIsWbeEoROHZs8/nixhn3N2eOn8Hqyprzz6j3UcuRSu+O/a7PmcR4+GadPcnwkmWoOp4SrdsUgfYKSqnuC9vgVAVe8ut80Ub5siEckFVgIttJKgevbsqbNmzYo7joKxBSxZt2SX5bm1cunUrFPc+zGpbeHahWwr27bLcvucM8vCtQsZWrSNcf91r8uAr5uB5NamU7OOgcbmq9JSWLTIjZqqX98NpW3Zslq7EJHZqtoz1rrchAQZW2vg+3Kvi4FeFW2jqttEZB3QDFhTfiMRGQoMBWjbtm21gli6bmnM5dvKttG1RfQFiklXX63+KuZy+5wzy1erv+KA1bBNIFdBgZwy+KxZKZ26ZvDn/OmnrgaTKmzf7q4Kxo1L2O79TASxBvZG/9KPZxtUdTwwHtwVQXWCaJvXNuYVQbu8dvz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" }, "metadata": { @@ -526,7 +516,7 @@ " for dl in dels[0]:\n", " gs.append(get_supports(ds, 0.5, wf))\n", "\n", - "print(gs)\n", + "# print(gs)\n", "\n", "for i in range(3):\n", " for j in range(4):\n", From 8f54c9593227a71197474b763bbfdf3a296bfe7b Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 11:09:02 +0530 Subject: [PATCH 08/13] done --- DWDM .ipynb | 64 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 54 insertions(+), 10 deletions(-) diff --git a/DWDM .ipynb b/DWDM .ipynb index d8c6974..9634c94 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -264,6 +264,7 @@ " weighted_strength=0\n", " for i in itemset_pairs_time_strength:\n", " w_TI=weighting_function(i[2], delta, u)\n", + " print(weighting_function_1(i[2], delta, u) < weighting_function_2(i[2], delta, u))\n", " weighted_strength+=w_TI*i[3]\n", " total_strength+=i[3]\n", " TiW=weighted_strength/total_strength\n", @@ -491,13 +492,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" }, "metadata": { @@ -509,17 +510,60 @@ "source": [ "# For graph 2\n", "gs = []\n", + "us = [1, 0.7, 0.5, 0.1]\n", + "pers = [[0.2, 0.3, 0.5, 0.7, 1] for _ in range(len(us))]\n", + "counts = [[0, 0, 0, 0, 0] for _ in range(len(us))]\n", + "\n", + "for u in us:\n", + " gs.append(get_supports(0.7, u))\n", + "\n", + "# print(get_supports(0.9, 0.5))\n", + "\n", + "for i in range(4):\n", + " for j in range(5):\n", + " percent = pers[i][j]/100\n", + " for k in gs[i]:\n", + " if k > percent:\n", + " counts[i][j] += 1\n", + "\n", + "plt_graphs(pers, counts, labels=[\"u 1\", \"u 0.7\", \"u 0.5\", \"u 0.1\"], colors=[\"b\", \"g\", \"y\", \"r\"], markers=['.', 'o', '^', \">\"], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "0.9\n0.7\n0.5\n0.9\n0.7\n0.5\n0.9\n0.7\n0.5\n[0.6776481876825674, 0.09976685658547987, 0.10385041172718347, 9.874043278937897e-54, 0.0043823750212221395, 0.11302312330213833, 9.381732235816013e-06, 0.0011617343934576237, 0.00015792955571542863]\n[0.11482980028095009, 0.11618436336293894, 0.11174135661916718, 0.09889031197743084, 0.11135109076020455, 0.11471652497373365, 0.113272597829712, 0.10893366993067578, 0.11008028426518704]\n" + }, + { + "data": { + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# For graph 3\n", + "gs = []\n", "WF = [1, 2, 3]\n", - "dels = [[1, 0.9, 0.7, 0.5] for _ in range(3)]\n", - "counts = [[0, 0, 0, 0] for _ in range(3)]\n", + "dels = [[0.9, 0.7, 0.5] for _ in range(3)]\n", + "counts = [[0, 0, 0] for _ in range(3)]\n", "for wf in WF:\n", " for dl in dels[0]:\n", - " gs.append(get_supports(ds, 0.5, wf))\n", - "\n", - "# print(gs)\n", + " print(dl)\n", + " gs.append(get_supports(dl, 0.5, wf))\n", "\n", "for i in range(3):\n", - " for j in range(4):\n", + " for j in range(3):\n", " for k in gs[3*i + j]:\n", " if k > 0.3:\n", " counts[i][j] += 1\n", From 4551445a0696f89a023cb0df87b6ea201b01c31f Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 12:01:01 +0530 Subject: [PATCH 09/13] changes --- DWDM .ipynb | 82 +++++++++++++++++++++++++++-------------------------- 1 file changed, 42 insertions(+), 40 deletions(-) diff --git a/DWDM .ipynb b/DWDM .ipynb index 9634c94..8c28952 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -21,13 +21,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[98.54525150040439, 213.4393359715151, 336.7893581280956, 497.34713226725825, 901.5305270407393, 914.5416050807784]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9'])\n" + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[146.64251826887897, 231.09545708001343, 480.2510304420213, 662.565987796003, 815.0425461366199, 987.0379848850146]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'])\n" } ], "source": [ @@ -71,7 +71,7 @@ "\tres.sort()\n", "\treturn res\n", "\n", - "text_file= open(\"sample.data\",\"r\")\n", + "text_file= open(\"Datasets/hundred.data\",\"r\")\n", "lines= text_file.readlines()\n", "# line1= lines[0]\n", "undelted_lines= lines\n", @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "{'0': [['2479', 40.00737599075366], ['21545477', 207.93653671490975], ['0440227660369838', 210.59121194306485], ['6639792690549748', 390.52918604998723], ['1247', 499.6514793463094], ['1992', 675.9269612509048], ['2535', 846.3626284334488], ['509577449337', 880.1776746649838], ['234347377188851892419486', 905.1158710256487]], '1': [['076515427203', 198.17246031770043], ['585473098827', 209.37086169919573], ['5993', 224.43726769302586], ['0991495663767993', 296.59760194727767], ['060617988964', 381.3216245605809], ['060610912787', 428.2578933478413], ['40235242', 516.7967922961599], ['245163928259', 635.5326972991845], ['07033355589271699239', 890.9975971954083]], '2': [['9033', 98.54525150040439], ['184577138778', 213.4393359715151], ['128517057890', 336.7893581280956], ['404969087443', 497.34713226725825], ['07651739', 901.5305270407393], ['36279693', 914.5416050807784]], '4': [['62737655', 74.78656445870836], ['2654', 670.4666496743686]], '5': [['0149', 217.072114031018], ['35339471', 388.7343158529507], ['5758', 644.6361582227731], ['1491', 672.6688406109625], ['5024', 878.1316190289217], ['3812', 962.316082781499]], '6': [['340447646487', 122.48512842842652], ['521076377771', 374.0582855374077], ['140555019708', 551.6479565188292], ['357238757705', 670.1674420126049], ['270941675713', 680.5630918693074], ['0886287069187963857191259795', 804.2488843160671], ['07236970', 864.7328790313439], ['06255975', 999.9196067474759]], '7': [['7851', 224.19765736798604], ['2053', 616.770183912852], ['58268595', 681.0509296430255], ['1734', 930.6983371093326]], '8': [['72137771', 33.45785376305355], ['5510', 257.7641036658326], ['46529373', 291.98023790741854], ['9928', 568.4965480433992], ['67717774', 659.4182474130035], ['31138769', 859.6781144900726]], '9': [['07656075', 59.47056507535697], ['5735', 401.69975628861897], ['3541', 504.9773205369124], ['2715', 680.6806209664097], ['2104', 726.1765334081601]]}\n" + "text": "3', 248.1459879345197], ['3467', 294.6307729943467], ['6711', 329.3981986304095], ['15376643', 347.0216366417831], 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653.0302524467071]]}\n" } ], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +264,7 @@ " weighted_strength=0\n", " for i in itemset_pairs_time_strength:\n", " w_TI=weighting_function(i[2], delta, u)\n", - " print(weighting_function_1(i[2], delta, u) < weighting_function_2(i[2], delta, u))\n", + " # print(weighting_function_1(i[2], delta, u) < weighting_function_2(i[2], delta, u))\n", " weighted_strength+=w_TI*i[3]\n", " total_strength+=i[3]\n", " TiW=weighted_strength/total_strength\n", @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -357,13 +357,13 @@ "\n", " for x in db.keys():\n", " TiW_supports[x] /= TiW_normalise\n", - " return TiW_supports \n", + " return TiW_supports, TiW_normalise \n", " " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -403,13 +403,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "0.01078052840079274\n" + "text": "0.07789652902935301\n" } ], "source": [ @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -454,13 +454,18 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 76, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "[[18, 17, 12, 8, 8], [24, 23, 21, 19, 16], [56, 52, 43, 38, 31]]\n" + }, { "data": { - "image/png": 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B2LYNLrgATjsN/vEP2xXMmBioN/Gr6ofAT4G3gbuACaq6NcpxmZYuIwN+9CM35/+JJ9xYwCWXuNk/L7zgqigaY6Kirlk9r4nIqyLyKvALIAsoB570jhkTufR0twnMJ5/AX/8KlZVuk5jBg93jqqqE3TPVmOaqrlk9o+t6o6q+G5WIgrBZPUnk6FF4+WVXEXTNGjjhBOjRw5WJvvFGePTReEdoTLPR4AVcIiJaz7LecF7TFCzxJ6HqalcK+te/dg0AuC0jH3gArrgCunePb3zGNAONKcs8X0RuEpGex31QuoiME5GZwPVNHagxgNsW8uKL4cwz3Sbx4K4GbrkF8vNdsbhbb4U33oCDB+MbqzHNTF1n/BnAD4BrgN7AXiADSMUN9D6qquFvMBkBO+NPUr49UwPrsrRu7fYCWLIEFi50z7Vq5RqIc891ewYMG+auDoxJcqHO+ENutq6qR4A/AH8QkVZAJ+Cwqtr+eyY2gu2Zqgqlpa4UxOHDbp/gt9+GOXNcTaApU6BDBxg3zt8QFBbGJXxjElXIxB9IVSuBkijHYkxN9e2ZmpkJ55zjbgC7dsHcuf6G4KWX3PG+ff2NwNix0K5d7H4HYxKQlWU2LZOqmyLqawQWLHBjAampbjtJX0MwYoTrKjKmBWpUWeZEYYnfRKyiwo0L+BqC5ctdN1J2tusWmjDB3fr1A5F4R2tMk2h04heRNri+/WoROREYALzpdf/EhCV+0+S+/toVifM1BJs2ueM9e/qvBsaPh44d4xunMRGIJPGvwG3EkgssAZYDh1T1mmgEGowlfhN1n3/ubwTmzYN9+9yZ/7Bh/obgzDPdrCJjmolIEv9HqjpMRG4CMlX1PhFZqapDoxXs8Szxm5iqqnJdQb6GYMkSdywrC0aN8jcEgwZZt5BJaI1ZwBXwXjkDN5/ftzFLWLOBvDenishKEXnde9xbRJaKyEYReV5E0sP9LGNiIi0NRo50q4YXLnTTR195xW0kv2kT/OxncPLJbiHZf/wH/O1vVkfINCvhJP6bcUXaZqvqxyLSB5jfgO+4GVgf8Phe4Heq2g/4GpjUgM8yJvZyctzWkb//vZsptGWLqyg6apRbOXzdddCtG5xyCtx2G/zrX26/YWMSVFRn9YhIATATmAb8DLgI2A10VdUq70riLlU9r67Psa4ek7Cqq2HlStclNGcOvP++m0GUnu62l/R1CxUVuTIUxsRQJH38JwK3AYUEdPGo6rgwvvQl4B4g2/uM7wFLVLWv93wP3AyhwUHeOxmYDNCzZ89Tt2zZUt/XGRN/hw65SqK+hmDtWne8Uyc3S8jXEPToEd84TVJocMmGAC8CfwKeAMLeHUNEvgXsUtUVIjLGdzjIS4O2PKo6A5gB7ow/3O81Jq6ysuD8890NXL2hd97xNwTPP++O9+/vbwTGjHHrCYyJkbCmc6rqqQ3+YJF7gOuAKlxxtxxgNnAe1tVjkpEqrFvnbwTefdfVG/INJvsaguHD/RVJjYlAJF09dwG7cEm73HdcVb9qwJePAW5T1W+JyIvA31X1ORH5E7BGVf9Q1/st8ZsW6cgRV3fI1xB89JFrHNq1q1lk7oQT4h2paaYiSfybghxWVe3TgC8fgz/x9wGeAzoAK4FrVbW8rvdb4jdJYc8eV2Ruzhy3hmCrt7V1797+RmDcOMjNjW+cptloVOIXkRTgDFVdFM3g6mOJ3yQdVfj0U//VwLx5cOCAmxk0fLi/IRg50s0gClRSAldd5cYTunaNT/wmIURyxr9YVc+IWmRhsMRvkl5lJSxd6m8Ili51U0nbtHGlpn1F5gYMgJ/8BB57zPYoNhEl/t8Aa4CXY7G/bjCW+I05zt69MH++v1vo88/d8W7d3L4ER49CRoZbaWxn/UkrksRfBrTBTeU8jJuSqaqaE41Ag7HEb0w9Nm1yjcC998IXX/iPt2sHV1/tpoyOGQNdusQrQhMHVo/fmJYu2B7FKSlupzLfhvQDB7oGYOxYGD0a8vLiEqqJjUYXaRPnWhH5lfe4h4iMiEaQxpgIBNujOC3N1RJasgSmT3crhmfOhCuugM6dXbG5n/4UZs92xehMUginq+ePQDUwTlVPEpFc4G1VPS0WAYKd8RsTlqFDYdWq2seLilw9IZ/KSld2ev58d1u0yC0kE3GF5saOdVcFo0bZ1NFmrinq8R+rwS8iq1V1SJRircUSvzFRVFEBy5a5RmDBAreo7MgR1xAMHervGjr7bNuovpmJJPEvBc4EPvQagDzcGb9txGJMS3TkiJsuumCBawwWL3aNQ0qK25Fs7Fh3O+ssqzGU4CJJ/NcAVwLDcCWWLwd+paovRCPQYCzxGxNHhw+7MQLfFcGSJa67KDXVLSbzdQ2ddZZbV2ASRkSzekRkADAeN5Vzrqqur+ctTcoSvzEJ5NAh1x3kuyJYtsxtTZmWBiNG+LuGzjzTVSs1cRPJGf9fVfW6+o5FkyV+YxLYgQOuIfANFi9f7haQtWoFp5/u7xoaOdJNLTUxE/HgbsDjVGCtqg5s+jCDs8RvTDNSVuZ2IvN1Da1Y4aaZtm7tkr+va2jkSHfMRE2DE7+I/AK4E8gEfBuIClABPK6qd0Qp1los8RvTjO3b5zat93UNrVzpitBlZLjuIF/X0IgRtQvOmYhEcsZ/j6r+ImqRhcESvzEtyNdfu4bA1zW0erU7npkJ3/iGv2to+HDXXWQaLZKtF2ut0hWRuao6vkkiM8Ykl9xcmDjR3cCtGH7vPf8VwZQp7nibNm6mkK9r6NRTbWeyJhLyrygiGbjibJ281bq+/XJzgO4xiM0Ykww6doRLLnE3gN27XUPguyK4w+tVzs52i8h8XUNDh7oppabB6mo+bwBuwSX5FfgT/37AinwbY6IjLw8uu8zdAHbudPsT+waL33jDHc/JcWUlfF1Dp5xiDUGYwunjv0lVfx+jeIKyPn5jzDElJa4B8HUNbdzojufm+huCMWNcAbqUeutQtmiRLuAaDAwEMnzHVPXpJo2wDpb4jTEhFRf7rwjmz/fvR9Cxoys97esaGjTI1R9KIpHM6vkfYAwu8b8BfBN4X1Uvj0KcQVniN8aE7csv/VcDCxbA5s3ueF6eawh8XUMDBrT4hiCSxL8WGAKsVNUhItIFeEJVL4pOqLVZ4jfGNNrmzf5GYP582LrVHe/SxX81MGYMnHhii2sIIpnOeVhVq0WkSkRygF1AnyaP0BhjoqGwEL7/fXdTdV1BvkZg/nx4/nn3um7d/I3A2LFwwgktriHwCSfxLxeR9sDjuNk9B4BlUY3KGGOiQcQl9BNOgEmTXEPw2Wf+RmDePHjmGffaggJ/IzB2rGtAWkhD0KA9d0WkEMhR1TXRCigY6+oxxsSEKmzY4O8aWrAAdu1yz/Xs6W8ExoyBXr3iGGh4Ip3VcylwFqC4gd3ZTR9iaJb4jTFxoQrr1/uvCBYs8O9N3Lt3za6hgoJ4RhpUJIO7fwD6As96h64EPlfVnzR5lCFY4jfGJITqavj445pXBF9/7Z7r27fmYHH3+Bc4iCTxfwwMVu+FIpKCK8s8KCqRBmGJ3xiTkKqrYc0a/2Dxu++6aqTgZgn5uoZGj4auXWMeXiSzejYAPYEt3uMeQEz7+I0xJiGlpEBRkbvdcovbgGb1an/X0DPPwGOPudeedJL/amDMGLeuIE7COeN/FzgN/0ye04DFeDX6VXViNAMEO+M3xjRTVVVu/wFf19DChW7HMoDBg/1dQ6NHu5XGgUpK4Kqr3HTTRl4tRNLVM7qu51X13UZF1ACW+I0xLUJlpduRzNc19P77bg9jcEXmfF1Do0a58tSPPQY33giPNq4uZkSzeuLNEr8xpkWqqHB7FPu6hhYtgiNH3HMi/p3KNm1q1Fl/qMSf3KXrjDEmntLT3faTU6bAO+/A3r1uL4LhAbm6uhqmTm3Sr41a4heRDBFZJiKrReRjEfmNd7y3iCwVkY0i8ryI2CabxhgDbvP5vn1h3Tp3tg/uquCpp2DHjib7mpCJX0Tmej/vbeRnlwPjVHUIUAScLyIjgXuB36lqP+BrYFIjP98YY1qeqVPdWX6go0eb9Ky/rjP+bt7A7kQRGSoiwwJv9X2wOt7wNa28mwLjgJe84zOBb0cQvzHGtCyLF7uz/EAVFfDBB032FXXN4/81cAdQADx43HO+BF4nEUnFFXbri9uu8XNgr6pWeS8pBvJDvHcyMBmgZ8+e9X2VMca0DCtXRv0rQiZ+VX0JeElEfqWqjbrGUNWjQJFX3XM2cFKwl4V47wxgBrhZPY35fmOMMbXVu3JXVaeKyERglHdogaq+3pAvUdW9IrIAGAm0F5E076y/ANjewJiNMcZEoN5ZPSJyD3Az8G/vdrN3rL735Xln+ohIJnAOsB6YD/i2bbweeKVxoRtjjGmMcGr1XAgUqWo1gIjMBFYCv6jnfd2AmV4/fwrwgqq+LiL/Bp4Tkbu9z3my0dEbY4xpsHASP0B74Cvvfrtw3uBt1jI0yPEvgBFhfq8xxpgmFk7ivwdYKSLzAcH19dd3tm+MMSZBhTO4+6w3MHsaLvHfrqpNt4TMGGNMTIXV1aOqJcCrUY7FGGNMDFiRNmOMSTKW+I0xJsnUmfhFJEVE1sUqGGOMMdFXZ+L35u6vFhErlmOMMS1EOIO73YCPRWQZcNB3MBZ77RpjjGl64ST+30Q9CmOMMTETzjz+d0WkF9BPVd8RkSwgNfqhGWOMiYZwirT9ELdxymPeoXzgH9EMyhhjTPSEM53zJ8A3gP0AqroR6BzNoIwxxkRPOIm/XFWP7QMmImmE2DzFGGNM4gsn8b8rIncCmSIyAXgReC26YRljjImWcBL/HcBuYC1wA/AG8MtoBmWMMSZ6wpnVU+1tvrIU18WzQVWtq8cYY5qpcGb1XAh8DjwC/B/wmYh8M9qBRWrW2lkUPlRIym9SKHyokFlrZ8U7JGOMSQjhLOB6ABirqp8BiMgJwD+BN6MZWCRmrZ3F5Ncmc6jyEABb9m1h8muTAbjm5GviGZoxxsRdOIl/ly/pe74AdkUpniYxZe6UY0nf51DlIW5+82bys/MpyCkgPzufzFaZcYrQGGPiJ2TiF5FLvbsfi8gbwAu4Pv4rgA9jEFujfbnvy6DHSw+XMnbm2GOPO2R2oCCnwN2yC8jPyT/22NdA5LTOQURiFboxxkRdXWf8FwXc3wmM9u7vBnKjFlET6NmuJ1v2bal1vHvb7jx9ydMU7y+meH8x28q2Hbu/fPtydh2sfSHTNr1tjYbg+PsFOQV0yuoUduMwa+0spsydwpf7vqRnu55MGz/Nup+MMTEVMvGr6vdjGUhTmjZ+Wo0+foCsVlncd+59jO8zPuT7yqvK2V62vUaDENhAzN00l5KyEo7q0RrvS09N93ch5eRTkO1vFHxXEV3bduX5j5+3sQdjTNxJfTMzRaQ3cBNQSEBDEcuyzMOHD9fly5c36D3ROrM+Wn2UnQd3+huF/V4jUVbzcfnR8hrvS5EUBKnVaAB0adOFRT9YRH5OPhlpGRHHaIwxACKyQlWH1zoeRuJfDTyJW8BV7Tuuqu82dZChNCbxx5OqUnq41N8oeFcNU9+bWu97O2V1qtWVVKObKSefnNY5MfgtjDHNXajEH86sniOq+kgUYmqxRIROWZ3olNWJIV2HHDv+9Oqng449dG7TmXvPubdW19LSbUvZc2hPrddnp2fX7ErKrtmtVJBTQMfMjjYobYwJKpzE/7CI/A/wNnCs/0JVP4paVC1UqLGHB897MGQ31JGqI2wv216zW8nrWtq2fxv//vzflBwooVqra7yvdWrroLOUAu93bduV1BTbWsGYZBNO4j8ZuA4Yh7+rR73HpgF8yb0hYw8ZaRn0ye1Dn9w+IV9TVV3FzgM7gw5IF+8vZknxEor3F1NxtKLG+1Illa5tu9bZrZSfnU/rtNZN8wcwxiSEcPr4PwFOCSzNHGvNrY8/Eakqew7tCTljyXc7UHGg1nvzsvLq7FbKz84nu3V2HH4rY0xdIunjXw20J8FX65q6iQh5bfLIa5NHUdeikK/bX76/dreS10Bs3beVxVsXU3q4tNb7clrn1NmtVJBTQIfMDg0ed7B1D8Y0vXASfxfgExH5kJp9/DGbzmliJ6d1DgPzBjIwb2DI1xZGsQYAABZwSURBVByuPHxs3OH4q4ZtZdtYt2sdJWUl6HH79WSkZYRsFHxXEF3adDk27mA1l4yJjnC6ekYHO27TOU1dqqqr2HFgR82rhv3baq13qKyurPG+VEmle3Z38nPyWbNjDYeqDtX67J45Pdlya+3ZUcaYmhrd1RPLBG9ajrSUtGNn86FUazV7Du2p1bXku4IIlvQBvtz/JV1+26XObqX8nHzapreN1q9nTLNWb+IXkTL8e+ymA62Ag6pa5yoiEekBPA10xc0GmqGqD4tIB+B53ErgzcB3VPXrxv4CpvlKkRQ6t+lM5zadGdZtWK3nCx8qDLruoV3rdlzc/2K2lW1jy74tLNq6iK8OfxX0daHqK/m6lnIzcm29g0k64Zzx15iuISLfBkaE8dlVwM9V9SMRyQZWiMgc4HvAXFWdLiJ34LZ2vL3BkZsWL9S6h0cvfLRWH/+hykM1xx2OK6WxZucadhzYUWvcITMts8YMpeOrtBbkFNC5TWdSJJxdSo1pHsIZ3K1BVf/hJez6XlcClHj3y0RkPZAPXAyM8V42E1iAJX4TREPWPWS1yqJvh7707dA35OdVHq2k5EBJ0BlLxfuLWbhlIdvLttcad0hLSaN7dvfgVw7e427Z3UhPTW/aP4AxURLO4O6lAQ9TgOHAaFU9I+wvESkE3gMGA1+qavuA575W1VplnkVkMjAZoGfPnqdu2WKDeSb6qrWa3Qd3h5yx5Lt//EY/gtC5Tec6F8MV5BSQ1SorTr+ZSUaRFGl7KuBhFa5f/nFVDWtev4i0Bd4FpqnqyyKyN5zEH8hm9ZhEoqrsPbK3ZqMQpErr10dqD13lZuTWuRiuIKeAdq3b2biDaRKRzOppdF1+EWkF/B2Ypaove4d3ikg3VS0RkW7YwjDTzIgIuZm55GbmMrjz4JCvO1hxkG1l20J2La0sWcnOgztrvS+rVVboKwfvfl6bvKDjDrbgzYSjrq0Xf13H+1RV66wxLO6U5Ulgvao+GPDUq8D1wHTv5yvhh2tM89EmvQ0ndjyREzueGPI1FUcrKCkrCVlCY8HmBWwv205VdVWN97VKaXVs3MF321G2gxfXv3isJpMteDOhhOzqEZGfBzncBpgEdFTVOidJi8hZwEJq1vG/E1iK27+3J/AlcIWq1p6LF8C6ekwyO1p9lF0Hd9VbZ+lI1ZGg709PTeeCfheErLOU2Sozxr+RiZVG9/F7b84GbsYl/ReAB8Lt428KlviNqZuqkvq/qbWmq/oMyhtE8f5i9pXvq/Vch8wOodc7eI9zWufYuEMz1Kg+fm+x1c+Aa3BTL4fZYitjEo+I0LNdz6AL3nq168W6H68D4EDFgaCrpH33l29fzq6Dtc/p2qa3DblK2ncV0Smrk613aCbq6uO/H7gUmAGcrKq16/UmuPnzYfFiGDsWzgh78qkxzVOoBW/Txk879rhtelv6d+pP/079Q35OeVU528u215615M1YmrtpLiVlJbX2j05PTSc/O9/flRSka6lr266kpTR4+ZBpYnX18VfjqnFWQY3rR8EN7sZs49fGdPUsXgxnnQXV1SACp5wCAwdCfj4UFLib737XrpBm/xZNCxCrWT1Hq4+y8+DOkDvD+R6XHy2v8b4USTm2+U+obqX8nHwy0jKaPOZkFFEff7w1JvHfcw/88pf+xN+rF6SmQnExlNf8t0hKCnTrFrxR8N26d4cM+7doTNhUla8OfxVyMNr3eH/5/lrv7ZjZsd7FcDmtY3bu2WxFshFLszRmDLRuDRUVkJ4OzzzjuntUobTUNQDbtrmfvtu2bbB+PcyZA2VltT+zU6fgjULg42zbiMoYwI07dMzqSMesjgzpOiTk68rKy+pcDLds2zJ2H9pd633Z6dl11lnKz3bjDjYoXVuLPeMH192zYIFrBBrax79/f82GIVgjsWdP7ffl5IS+cvDd//RTePfdxsVlTDI6UnXEjTuEWAxXvL+YkgMlVGt1jfe1Tm19rDEINTjdtW3XY5v/tDRJ19UTC4cPw/btwRsF3/0dO1x3UzBpaXD//XDDDZBpU6mNiUhVdRU7D+yst86Sb4GbT6qkHht3CNWtlJ+dT+u01nH6zRrPEn+cVFW55O9rCP7yF3jjDdfl5NO6tRuInjABzj0Xhgxx4w7GmKalqpQeLq1zZ7it+7dyoKL2JMa8rLwaM5aOr7GUn51PduvE6uu1xJ8gFi+G8eP9Yw933+0ahDlzYJ2bak1ennvNuee6xqAg9CZWxpgo2F++P+TOcL5b6eHSWu/LaZ1TZ7dSQU4BHTI71Dvu0FSzsyzxJ5BQYw/bt8M777hGYM4c2OnV7xowwN8IjB5tA8jGJILDlYePbf4Tqltpx4EdtcYdMtIyakxd9a138D1etm0Zt79ze631GDMumtHg5G+Jv5lRhbVr/Y3Ae++5MYW0NNdY+BqC4cPdNFVjTOKpqq5ix4EddZbv3la2rda4QzC92vVi8y2bG/T9lvibuSNHYNEif0Pw0UfuePv2rltowgR369MnvnEaYxqmWqvZc2jPsUZh4nMTg75OEKr/J8RMkRAs8bcwu3fD3Ln+hmDrVne8Tx//1cC4ca5hMMY0H4UPFYasudRUZ/w2d6SZysuDq66CJ5+ELVvgk0/gkUdg0CD429/gssugY0fXLfTrX8PChVBZWf/nGmPia9r4abW26Dy+5lKk7Iy/BaqshKVL4e233dXAsmVuLUHbtq5gna9bqH9/V87CGJNYbFYPlvgjtXcvzJvn7xb6/HN3vEcPfyMwfry7ijDGtByW+M0xX3zhbwTmznUNA8CwYf6G4BvfsKJ0xjR3lvhNUEePwvLl/obggw/cauPMTBg1yt8QnHyydQsZ09xY4jdhKStzBeR8DcH69e54ly7+RmDCBFfG2hiT2Czxm0YpLnarid9+2/3c7VXHHTzY3wiMGgVt2sQ3TmNMbZb4TcSqq2HNGv9soYUL3aY26eluTMDXEAwbZkXmjEkElvhNkzt8GN5/398QrF7tjnfsWHM1ca9e8Y3TmGSVdDtwmejLzPQnd3BF5QKLzL3wgjt+4on+140d6zarMcbEj53xm6hQdQPDvquBBQvg0CFXUG7kSH9DMGKEbXRvTLRYV4+Jq4oKV47a1xAsX+4ah5wcV1PI1xD07WvTRo1pKpb4TUIpLa25mnjzZne8sLDmauIOHSLbO9mYZGaJ3yQsVVdGYs4cd0Uwb57b7F7EbUKzcaObUdS6tVtpbMnfmPBY4jfNRlUVfPihawSeespVH/Xp0gUuughOP93dBg60jWiMCcUSv2mWfHsUl5e7tQHDh8Onn8JXX7nn27aF005zA8a+xqBr1/jGbEyisOmcplk64wzXvRPYx68Kn33mSk8vWeJ+3n+/u1IAt27g9NP9jcGwYVZwzphAdsZvWoTDh2HlSn9DsHSpv4uoVSsYMqTmVYHNHjLJwLp6TNLZsaPmVcGyZXDwoHuuY0e3hsDXGIwYAbm58Y3XmKZmXT0m6XTtChdf7G7gSlD/+981G4O33nJdR+B2JAvsIjr5ZHe1YExLE7UzfhH5M/AtYJeqDvaOdQCeBwqBzcB3VPXr+j7LzvhNtOzf7xaT+RqDJUtg1y73XGYmnHpqzcagoMC6iEzzEfOuHhEZBRwAng5I/PcBX6nqdBG5A8hV1dvr+yxL/CZWVN3YQOBVwUcfuVlFAN2712wIhg+3ktQmccWlj19ECoHXAxL/BmCMqpaISDdggar2r+9zLPGbeKqocJVHAxuDzz5zz6WkuC6hwMZgwAArS20SQ6Ik/r2q2j7g+a9VNeiQmohMBiYD9OzZ89Qtgat4jImzPXvcYLGvMVi2zL93cU6OGywObAxsI3sTD80u8QeyM36T6Kqr3cIy31TSJUvcpjV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" }, "metadata": { @@ -492,13 +497,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" }, "metadata": { @@ -517,8 +522,6 @@ "for u in us:\n", " gs.append(get_supports(0.7, u))\n", "\n", - "# print(get_supports(0.9, 0.5))\n", - "\n", "for i in range(4):\n", " for j in range(5):\n", " percent = pers[i][j]/100\n", @@ -531,18 +534,18 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "0.9\n0.7\n0.5\n0.9\n0.7\n0.5\n0.9\n0.7\n0.5\n[0.6776481876825674, 0.09976685658547987, 0.10385041172718347, 9.874043278937897e-54, 0.0043823750212221395, 0.11302312330213833, 9.381732235816013e-06, 0.0011617343934576237, 0.00015792955571542863]\n[0.11482980028095009, 0.11618436336293894, 0.11174135661916718, 0.09889031197743084, 0.11135109076020455, 0.11471652497373365, 0.113272597829712, 0.10893366993067578, 0.11008028426518704]\n" + "text": "0.3\n0.5\n0.7\n0.9\n0.3\n0.5\n0.7\n0.9\n0.3\n0.5\n0.7\n0.9\n" }, { "data": { - "image/png": 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" }, "metadata": { @@ -555,20 +558,19 @@ "# For graph 3\n", "gs = []\n", "WF = [1, 2, 3]\n", - "dels = [[0.9, 0.7, 0.5] for _ in range(3)]\n", - "counts = [[0, 0, 0] for _ in range(3)]\n", + "dels = [[0.3, 0.5, 0.7, 0.9] for _ in range(3)]\n", + "counts = [[0, 0, 0, 0] for _ in range(3)]\n", "for wf in WF:\n", " for dl in dels[0]:\n", - " print(dl)\n", " gs.append(get_supports(dl, 0.5, wf))\n", "\n", "for i in range(3):\n", - " for j in range(3):\n", - " for k in gs[3*i + j]:\n", - " if k > 0.3:\n", + " for j in range(4):\n", + " for k in gs[4*i + j]:\n", + " if k > 0.003:\n", " counts[i][j] += 1\n", "\n", - "plt_graphs(dels, counts, labels=[\"WF_1\", \"WF_2\", \"WF_3\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + "plt_graphs(dels, counts, labels=[\"WF_1\", \"WF_2\", \"WF_3\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"delta\", y_label= \"Number of patterns (k)\")" ] } ], From b18efe427ca8db9ecdee25e0d8ec38dbe8e9dc57 Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 12:14:56 +0530 Subject: [PATCH 10/13] xyzabc --- DWDM .ipynb | 62 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/DWDM .ipynb b/DWDM .ipynb index 8c28952..0c05a01 100644 --- a/DWDM .ipynb +++ b/DWDM .ipynb @@ -401,6 +401,26 @@ " return [sups[k] for k in sups.keys()]" ] }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "def get_support_modified(delta, u, wf = 1, x1 = 1, x2 = 0):\n", + " TiW_db = dict()\n", + " for i in database.keys():\n", + " itemset_pairs=itemset_pair_generator(database[i])\n", + " itemset_pairs_time=time_interval_sequence(itemset_pairs)\n", + " itemset_pairs_time_strength=strength_pair(itemset_pairs_time)\n", + " TiW_db[i]=TiW_sequence(itemset_pairs_time_strength, delta, u, wf)\n", + " sups_TiW = TiW_support(TiW_db,database)\n", + " sups_nominal = nominal(TiW_db,database)\n", + " print(\"Hi\")\n", + " assert len(sups_TiW.keys()), len(sups_nominal.keys())\n", + " return [x1*sups_TiW[i] + x2*sups_nominal[i] for i in sups_TiW.keys()]" + ] + }, { "cell_type": "code", "execution_count": 74, @@ -495,6 +515,48 @@ "plt_graphs(pers, counts, labels=[\"del 0.5\", \"del 0.7\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" ] }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "Hi\nHi\nHi\n" + }, + { + "data": { + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gs = []\n", + "dels = [0.5, 0.7, 0.9]\n", + "pers = [[0.2, 0.3, 0.5, 0.7, 1] for _ in range(len(dels))]\n", + "counts = [[0, 0, 0, 0, 0] for _ in range(len(dels))]\n", + "for ds in dels:\n", + " gs.append(get_support_modified(ds, 0.5, 1, 0.7, 0.3))\n", + "\n", + "# print(get_supports(0.9, 0.5))\n", + "\n", + "for i in range(3):\n", + " for j in range(5):\n", + " percent = pers[i][j]/100\n", + " for k in gs[i]:\n", + " if k > percent:\n", + " counts[i][j] += 1\n", + "\n", + "plt_graphs(pers, counts, labels=[\"del 0.5\", \"del 0.7\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + ] + }, { "cell_type": "code", "execution_count": 77, From 1169124766948e2effae38099f6a0b44687fc57e Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 12:17:27 +0530 Subject: [PATCH 11/13] renamed --- DWDM .ipynb => DWDM.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename DWDM .ipynb => DWDM.ipynb (100%) diff --git a/DWDM .ipynb b/DWDM.ipynb similarity index 100% rename from DWDM .ipynb rename to DWDM.ipynb From cfd2d7c07b138fbf347648c228fd5937d039bc25 Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Wed, 27 Nov 2019 12:31:07 +0530 Subject: [PATCH 12/13] all done --- DWDM.ipynb | 102 ++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 70 insertions(+), 32 deletions(-) diff --git a/DWDM.ipynb b/DWDM.ipynb index 0c05a01..ecee956 100644 --- a/DWDM.ipynb +++ b/DWDM.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -21,13 +21,13 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[146.64251826887897, 231.09545708001343, 480.2510304420213, 662.565987796003, 815.0425461366199, 987.0379848850146]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'])\n" + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[111.36040229579802, 457.187622985742, 688.0489924627027, 899.6499803987468, 925.7600090876037, 992.1739498484766]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'])\n" } ], "source": [ @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "3', 248.1459879345197], ['3467', 294.6307729943467], ['6711', 329.3981986304095], ['15376643', 347.0216366417831], ['36299542', 579.0347820145503], ['6734', 594.5468795071309], ['21327396', 856.4733771758637], ['450855739488', 904.0987083972038], ['32467317', 924.0092831528765], ['37117635', 962.4997517228871], ['4876', 965.8276515729156], ['38129296', 994.9182385358965]], '39': [['58356804', 22.994390508154773], ['56508723', 161.50014164157923], ['7080', 192.49020809788973], ['42468760', 327.37249448647157], ['03411805', 391.19359172467574], ['0827330049767473', 469.16974944712763], ['37714432', 527.8058219833164], ['046724856353', 580.1115812933191], ['10674047', 679.3144756528613], ['40565461', 983.1781111402298]], '40': [['31349119', 11.904334360250402], ['453352615619', 222.76965690626383], ['019829886879', 267.2167933776021], ['555963567534', 270.2834023451408], ['357780329614', 547.3891015554343], ['16697090', 561.0151008016717], ['4001', 590.2040956075688], ['60847220', 645.550488563548], ['76989662', 784.0895704372713], ['0229', 866.764387653589]], '41': [['2963', 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535.0646006302546], ['02992804', 730.2562719150445], ['7992873998869915', 805.1390873683526], ['68618544', 849.3060994674059], ['665788299030', 883.492161493273], ['320482699384', 904.1738583102893]]}\n" } ], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -357,13 +357,13 @@ "\n", " for x in db.keys():\n", " TiW_supports[x] /= TiW_normalise\n", - " return TiW_supports, TiW_normalise \n", + " return TiW_supports \n", " " ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -423,13 +423,13 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "0.07789652902935301\n" + "text": "0.028330524812387403\n" } ], "source": [ @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -474,18 +474,13 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": "[[18, 17, 12, 8, 8], [24, 23, 21, 19, 16], [56, 52, 43, 38, 31]]\n" - }, { "data": { - "image/png": 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" }, "metadata": { @@ -517,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -527,8 +522,8 @@ }, { "data": { - "image/png": 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07Xvx/yhK5UdLlkDiBUOWiYmweHGO3s4fpbN79OhBw4YN6dKlC2+99RalS5fOUewXMcb49AbcDkxO9/gebDfRESDMOdYG+D6r92revLlR1jvvGAPGLFuWbFq/19rUHF/TnEs65/kbvP66fYOnn/ZekEp50R9//OHvEAKSq88FWGky+bvqjympsUBrESkqtk10LfAHsADo6ZzTD3CfTlUGvXvbwqjvvScMbzucHf/s4Is/vvD8DR5+2Fba+9//bMtBKVUg+WNMYRnwOXba6QYnhonACOBxEdkORAGTfR1bMCtZEu64A2bMgGurdKVuVF3GLB6TeaG8C4nAW2/Zgef+/bOezqeUypf8snjNGPOsMaa+MaaxMaavMSbBGLPDGNPSGFPbGHO7MUaX22bTwIFw8iR8+kkIw9oOY/X+1czfOd/zNyhcGGbNgjJlbCkMHchXQcbjL0EFRE4+D13RnI+0aWNXOb/3HvS9rC8Vi1dkzKIsCuVdqEIFuznPwYPQs+fFg3FKBaiIiAji4uI0MTiMMcTFxREREZH1yen4a52C8gIRu8J5yBDYurkwg1sN5sn5T7J6/2qaVWrm+Rs1b2638+zd2652fkeXi6jAV6VKFfbs2YNOVU8TERGR7fUL4i6rikgEcAvQDjt99AywEfjaGLMp56HmjRYtWpiVWgY6g7g4qFwZ7r8fRo2Jp+q4qnSu25kZPWZk/82efBJeeskmhQceyPtglVJ+ISKrjDEu62Jk2n0kIiOBRdjpocuAd4FPgfPAiyLyo4hclvfhqtyIirKljT78EMJNJA+0eIBPN33Kjn92ZP/NRo+Gzp3hkUfgVzeb+Cil8g13YworjDHNjTFDjTEfG2N+MsbMM8aMNcZ0AfoAbqpHKX+JiYFjx+z2zINbDyZUQhm7ZGz23yg01O7UVquWzTS5qA2jlAoOmSYFY8zXACJS/cLnROQKY8whY4z23QSg9u3t3/FJk6Byicr0vawvU9ZM4fCpHPS1RkbCnDl244auXeHUqbwPWCkVMDyZfTTLqWwKgIi0B6Z4LySVWyEhdnrqr7/C1q0w7MphnDl/hjeXv5mzN6xb1xYK27DB1unW2R1K5VueJIX7ga9EpKKI3AyMB27O4jXKz/r3h7AwOz21ftn6dK3XlTdXvMmpxBx+0+/UyQ46f/aZHWtQSuVLWSYFY8wK7P4HP2DLWV9vjPnby3GpXKpYEbp0galT7VKDEVeO4OiZo0xek4uF4kOHwt13wzPP2OqqSql8x93so7kiMkdE5gD/xu57kABMdo6pABcTYxclz5kDbaq24aroq3h1yaucSzqXszcUgYkT4YorbHLY5PdZyUqpPJbpOgVn7CBTxphfvBJRNug6BfeSkqBGDWjQAL7/HuZtnUeXGV34qPtH9LmsT87feO9eaNHC7vCzfLkti6GUCho5WqcA/GqM+SWzm/PGF+/8oAJGaCjcdx/8+CPs2gU317mZhuUaZq9QniuXXAJffgl//22r8J0/n/VrlFJBwV1SWCAij4hIdPqDIhIuIh1FZBq2xLUKYPfdZ39OmQIhEsLwtsNZf3A93//1fe7euHVrePddu7/zE0/kPlClVEBwlxQ6AUnADBHZJyJ/iMgOYBtwFzDOGDPVBzGqXIiOthOHpkyxX+jvuvQuqpSswkuLXsr9m/fvD4MHw/jxdpNopVTQc7d47awx5m1jzJVANexmOM2MMdWMMTHGGC24HyRiYuwwwHffQXhoOENaD2HhroUs37s892/+8stw/fW2NtKSJbl/P6WUX3lUOtsYc84Ys98Yc8zbAam8d8sttiL2pEn2cUyzGEpFlMp+WW1XwsLswraqVeG222z2UUoFLd1PoQAoVMj29Hz9NezbByUKl+ChFg8xa/MstsZtzf0FypSx6xZOnrSb85w5k/v3VEr5hSaFAmLgQDtFdepU+/jRVo8SHhrOq4tfzZsLNGpki+etWmX7q7QUhlJBKcukICLFRCTEuV9XRG4VkULeD03lpdq1oUMHW/YiORkqFK9A/6b9mbZuGgdOHsibi9x6K4waZZPDK6/kzXsqpXzKk5bCr0CEUxRvPnAvMNWbQSnviImBnTvh55/t4yfaPkFiUiKvL3s97y7y1FPQqxeMGAHffpt376uU8glPkoIYY04DtwFvGGO6Aw29G5byhu7dbfd/yoBz7TK16dGwB2+veJvjCcfz5iIidv5rkyZw112wZUvevK9Syic8Sgoi0ga7qc7XzjHd2zkIRURA3752MfKRI/bY8LbDiU+IZ9KqSXl3oWLF4KuvIDzc7sEQH593762U8ipPksJj2IJ4XxpjNolITWCBd8NS3hITY/fL+eAD+/iKS66gQ/UOjFs6jsSkxLy7ULVqduu3v/6yLYakpLx7b6WU13hSOvtXY8ytxpiXnMc7jDGPej805Q2NGkGbNrYLKWWC0IgrR7D3xF4+3vBx3l6sXTt48007tvDUU3n73kopr/Bk9lFdEZkoIj+IyM8pN18Ep7wjJgb+/BMWLbKPb6h1A00qNGHMojEkm+S8vdj998ODD8KYMXZWklIqoHnSffQZsAZ4GhiW7qaCVK9eUKJE2oCziDD8yuFsPrKZr7d+7f7FOTF+PFx9tV0soaXOlQponiSF88aYd4wxy40xq1JuXo9MeU2xYtC7t91Z85hTuKRXo15Ui6yWN4XyLlSoEHz+ua210a0bHMijdRFKqTznSVKYKyIPiUglESmTcvN6ZMqrYmJsNYqPnWGEsJAwhrYZyqK/F7EodlHeX7BcOVsK459/bI2khIS8v4ZSKtc8SQr9sN1Fi4FVzk37AIJc8+Zw+eUZB5zvu/w+oopEMWZxHhTKc6VJE5g2zVZTfeghLYWhVABymxSc8hZ3G2NqXHCr6aP4lBfFxMDatbZcEUCx8GI83PJh5myZw+bDm71z0Z494Zln7AK3N97wzjWUUjnmNikYY5IBLWKTT/XuDUWKpA04Azzc8mGKhBXh5cUve+/CI0faRW2PPw7z53vvOkqpbPOk++gHEemh+zHnP5GRdibSxx/bqtcAZYuWZcDlA/ho/UfsPe6lvRFCQuDDD6F+fbj9drvATSkVEDxJCo9jp6UmishxETkhInlUKEf5W0yMTQiffpp27PE2j5Nsknlt6Wveu3CJEnbgWcS2Gk6c8N61lFIe82RFcwljTIgxppAxpqTzuKQvglPe17YtNGiQsQupRuka9GrUi3dXvcuxs17cbK9WLZuN/vzTFmVKzuOFc0qpbPNkRbOIyN0i8ozzuKqItPR+aMoXRGxrYelS2Lgx7fiwtsM4kXiCCSsneDeAa6+FsWNtq2HkSO9eSymVJU+6j94G2gC9nccngbdyc1ERKSUin4vInyKyWUTaOOsffhSRbc7P0rm5hvJc3762oGn61sLllS7nhlo3MH7ZeM6eP+vdAB55BO67z27Q89ln3r2WUsotT5JCK2PMv4CzAMaYf4DwXF53PPCdMaY+0ATYDDwJzDfG1MFu5vNkLq+hPFS2rN1r4cMP4Wy6v//D2w7nwMkDfLjuQ+8GIAJvv20r9fXvD+vWefd6SqlMeZIUzolIKGAARKQckOPOXxEpCVwNTAYwxiQaY44BXYFpzmnTgG45vYbKvpgYu9h41qy0Yx1rdKR5pea8suQVkpK9XPq6cGF78dKl7cDz4cPevZ5SyiVPksLrwJdAeREZDfwOvJCLa9YEDgPvi8gaEXlPRIoBFYwx+wGcn+VdvVhEBonIShFZeVj/cOSZDh2gZs2MXUgphfK2xm1l9pbZ3g+iYkW7Oc/Bg3aq6rlz3r+mUioDT2YfTQeGYxPBfqCbMeZT969yKwxoBrxjjLkcOEU2uoqMMRONMS2MMS3KlSuXizBUeiEhtojpwoWwbVva8R4NelCrdC1eWvQSxhdlKVq0gMmT4Zdf4LHHvH89pVQGnsw++tAY86cx5i1jzJvGmM0ikptO5j3AHmPMMufx59gkcVBEKjnXrAQcysU1VA707w+hofDee2nHQkNCeaLtEyzfu5xfd//qm0B694bhw+Gdd+Ddd31zTaUU4Fn3UaP0D5zxheY5vaAx5gDwt4jUcw5dC/wBzMEW38P56YP+CpVepUpwyy0wdSokptuZs1+TfpQvVt47ZbUz8/zzcNNN8PDD8KuPkpFSKvOkICL/FpETwGXOSubjzuND2D/gufEIMF1E1gNNgeeBF4HrRWQbcL3zWPlYTAwcOgTz5qUdK1KoCI+2fJRvt3/L+oPrfRNIaKitv1Grli2it3u3b66rVAEnWfUTi8gLxph/+yiebGnRooVZqTt55amkJKheHRo3tlsrpzh65ijR46Lp3qA7H3b38hTV9LZsgVatoEYN+P13u0OQUipXRGSVMaaFq+c86T66aPWyiGhpy3wqNNSuI/v++4xfzssUKcOg5oOYsWEGu4/58Ft7vXowY4Zdu3DvvboHg1Je5q77KEJEooCyIlI63a5r1YHKvgpQ+d5999mfU6ZkPD6k9RBEhHFLx/k2oJtugpdesqudn3/et9dWqoBx11K4H7vDWn3SdlxbhR0AzlWZCxXYqlWDG26wSSEp3Zq1qpFV6X1pbyatnkTc6TjfBvXEE9CnDzz9NMzJ7ZCWUiozmSYFY8x4Y0wN4AljTM10u641Mca86cMYlR/ExMCePfDddxmPD2s7jNPnTvP2ird9G5CIXVnXooVNDps2+fb6ShUQWQ40A4hIY6AhEJFyzBjzgRfj8ogONHtPYiJUrWpLa3/5Zcbnbvn4FpbtXUbs4FiKFCri28D27LGJoXhxWL4cypTx7fWVygdyNdAsIs8Cbzi3DsAY4NY8jVAFnPBwu5ht7lzYvz/jc8OvHM6R00d4f+37vg+sShWbpf7+G7p1g6uvhgMHfB+HUvmUJ7OPemIXmB0wxtyLrWpa2KtRqYAwcKAdU5g6NePxdtHtaF2lNa8ueZXzyed9H1ibNjBhAvz2m70995zvY1Aqn/IkKZwxxiQD550Kp4ewRe1UPlenDrRvb8tepN8UTUQY3nY4O/7ZwRd/fOGf4Dp1svNnwSaIvn3hhx8yLsVWSmWbJ0lhpYiUAiZhZx+tBpZ7NSoVMGJiYMcOWLAg4/Gu9btSN6ouYxaP8U2hvAuNGpWWFEJCYOZMuPFGqFAB7rnHVls9fdr3cSkV5DypkvqQMeaYMWYCtvxEP6cbSRUAPXrYLQ7SF8kDCJEQhrUdxur9q5m/08drGffvh/ffT2sVJCdDoUIwbZodZ5g3z+4aVK6cLcE9YwYcP+7bGJUKUp60FBCR20RkLLZmUS3vhqQCSUSE7ZmZNQuOHMn4XN/L+lKxeEXGLBrj26BGjcrYnwV28GPZMpssDh6EH3+Efv1saYzevW2C6NzZluXWfTiUypQns4/eBh4ANgAbgftFRBevFSADB9ov5R9eUPKocFhhBrcazI87fmT1/tW+C2jJkovHDhITYfFie79QIbjuOrvF5969sGiRrbb6xx/2l6lYETp2hDfftFNclVKpPCmItwlobJwTRSQE2GCMaeT2hT6g6xR8p3Vr2wOzaZNdR5Yi/mw8VcdVpXPdzszoMcN/AXrCGFi71jZ7Zs2ySQJswb3bbrO32rX9G6NSPpDbgnhbgOh0j6sCPqqfrAJFTAxs3pz2ZTxFZEQkD7R4gE83fcqOf3b4JzhPicDll9vup02b7C80ejScPw8jRtjpVk2a2CmuGzZo8T1VIHmSFKKAzSKyUEQWYjfEKScic0REi9AUEHfcYRcRXzjgDDC49WBCJZSxS8b6PrDcqF8fnnoKVq6EXbtg3DgoWdImhcsug7p14ckn7cppTRCqgPCk+6i9u+eNMb/kaUTZoN1HvnX//XZcYf9+iIzM+NyA2QOYsXEGuwfvplyxIN87+8ABmD3bdjH9/LNtSVSpYmc03XYbtGuXNh1WqSCUq+4jY8wv7m55H64KVDExcOaM3RDtQsOuHMaZ82d4c3k+qJVYsaLNgN9/b7eh++ADaN7cFuTr0MHuWxoTY3chSkjwd7RK5SmPCuIFKm0p+JYxtks+JARWu5hs1G1mN36L/Y3YwbEUC8+HO6SdPGnLxs6aZddCnDhhu5tuucW2IDp10p3hVFDI7UCzUoAdp42JgTVrYHXnxX4AACAASURBVNWqi58fceUIjp45ynurXQw85AfFi9v9oj/+OG0j6549bYuiZ0+7FuK22+Cjj+DYMX9Hq1SOuNt5bb7z8yXfhaMCXZ8+UKSI6wHnNlXbcFX0VYxdOpZzSed8H5wvRUSkLYY7cMCOPQwYYBfQ9e1rE0SnTrbL6dAhf0erlMfctRQqOYPMt4rI5SLSLP3NVwGqwFKqlK0cMX06nDp18fMjrhxBbHwsn2z6xPfB+UtYmB1reOMNW9J7yRIYMgS2bYNBg+wYRPv2MH48xMb6O1ql3Mp0TEFEegIDgKuw23KmZ4wxHb0cW5Z0TME/fv/dTsCZMgXuvaAKVrJJ5rJ3LiNEQlj3wDok/Uq3gsYYWL8+bbHcxo32+BVXpC2Wq1vXvzGqAsndmIInU1KfMcaM8kpkuaRJwT+MgYYNbaG8CxezAUxbO43+s/vzTe9vuKnOTb4PMFBt3Wo3CPriC1ixwh5r1CgtQTRpknG5uFJekquk4LzBrcDVzsOFxph5eRhfjmlS8J9XX4UnnrBffhtdUPAkMSmRWq/XonaZ2izot8D1GxR0sbG2vPesWXajoORkqFkzLUG0amWneSnlBbndjvMF4DHsSuY/gMecY6oAu+ceW3du0qSLnwsPDWdI6yEs3LWQ5Xt16w2XoqPh0Udh4UK7GnDSJNuVNH683Ri7ShX417/SFs8p5SOedB+tB5o6u68hIqHAGmPMZT6Izy1tKfjXHXfATz/ZQqQRERmfO5FwgujXorm2xrV83utz/wQYjI4dg6+/ti2Ib7+1qwXLlIGuXW0L4rrrLv6wlcqmvFinUCrd/chMz1IFSkwMHD1qu8kvVKJwCR5q8RCzNs9ia9xW3wcXrEqVsvN+v/jC7vvwxRdw0032Z5cuUL483HUXfPaZXUynVB7zJCm8AKwRkakiMg27Jefz3g1LBYOOHaFGDdddSACPtnqU8NBwXl38qm8Dyy+KFUtbDHf4sG05pDTPevWCsmVtC2LaNJudlcoDntQ+mgG0BmY5tzbGmJneDkwFvpAQu15rwQLYvv3i5ysUr0D/pv2Ztm4aB04e8H2A+Ul4eNpiuAMH7FjE/ffbeiP9+9u9qW+4ASZMsM8rlUMedR8ZY/YbY+YYY2YbY/RfnEp17722YKirFc4AT7R9gsSkRF5f9rpvA8vPQkMzLoZbtgyGDoWdO+HBB6FyZbjqKhg71pYET2//fvtaTRwqEzrnTeVK5cq22sPUqXDORWWL2mVq06NhD95e8TbHE477PL58TwRatoQXX7TrIDZsgJEjbbG+oUNt/17z5nYzoc2b7QZDv/9ufyrlgiYFlWsxMXDwoK0P58rwtsOJT4hn0qpMBh9U3hCBxo3h//4P1q2zZTbGjLFdT08/bVccTphg10RMngz79vk7YhWA3CYFEQkRkY2+CkYFp06d4JJLMh9wvuKSK+hQvQPjlo4jMSnRt8EVZLVrw7BhthbTnj22SylFQoLdfvTBB205cN0XQjncJgVnbcI6EYl2d54q2MLC7NjCd99lXu9txJUj2HtiLx9vcLFDj/K+kBC77Wj6dUkJCXbm0k032aqud94JM2dCfLz/4lR+50n3USVgk4jMT9mXWfdmVhcaMMD+nDLF9fM31LqBJhWaMGbRGJLtOkjlS6NG2W6j9EJDbZnvuXPtFNcFC+waiHLl4MYb4Z137MpEVaDkeI/m3G7F6ayMXgnsNcbcIiI1gJlAGWA10NcY47avQVc0B5Ybb7RjmTt3ut7C+OMNH9NnVh/m3DmHLvW6+D7Aguzyy2Ht2ouPN21qd00CSEqCpUttTaavvkqbZ3zFFdCtm701aKBF+/KBvCiIVw2oY4z5SUSKAqHGmBO5DOpxoAVQ0kkKnwKzjDEzRWQCsM4Y846799CkEFg+/9zutfDNN7ZH4kLnk89T+/XaVClZhd/v+933ASrPGWMz/FdfwezZsNypYVW7dlqCaN3adfZXAS+3BfFigM+Bd51DlwBf5TKgKkBn4D3nsQAdnesATAO65eYayvduvdX2PGQ24BwWEsbQNkNZ9PciFsUu8m1wKntE7Gylp56y6yD27IG334Zatez6iKuuspsHDRxop52dOePviFUe8WRM4V/AlcBxAGPMNqB8Lq/7GjAcSOnkjAKOGWNSykHuwSYfFUTCw6FfP9tFndnaqPsuv4+oIlGMWTzGt8Gp3LnkkrSZSocPw4wZts7Jp5/amkxly0KPHvDhh1pyI8h5khQS0vfti0gYkHWfUyZE5BbgkDEm/dbvrjopXV5DRAaJyEoRWXn48OGchqG8ZOBAW+l56lTXzxcLL8bDLR9mzpY5bD682aexqTwSGZk2U+nwYZso7rnHTn295x5btK9jR3j9ddi929/RqmzyZKB5DHAMuAd4BHgI+MMY858cXdDuxdAXOA9EACWBL4EbgYrGmPMi0gYYaYy50d176ZhCYGrf3k5a2bbN9ZjkkdNHiB4XzZ2N72RK10ymK6ngk5xsp72mDFRvdpL+5Zfbwn3dusFll+lAdQDIbensJ4HDwAbgfuAb4OmcBmOM+bcxpooxpjpwJ/CzMaYPsADo6ZzWD5id02so/4qJgb/+sjXbXClbtCwDLh/AR+s/Yu9xnfKYb4SE2JIbzz8Pf/wBW7bYFdVFisBzz9mZTjVrwuDB9h+Hbh4UkDypkpqMHfgdBTwHTDOeTFnKvhHA4yKyHTvGMNkL11A+0KOH3RYgswFngMfbPE6ySea1pa/5LjDlW3Xr2hXVixal7S7XqJEttdGhg63s2r+/3ZDj1Cl/R6scnnQfdQYmAH9h+/5rAPcbY771fnjuafdR4HrkEZg40ZbXiYpyfU7vL3ozb+s8YofEUiqilOuTVP5z8iR8/73tYpo3z+42FxFhS3937WoHrsuV83eU+Vpuu49eBToYY64xxrQHOgDj8jJAlf/ExEBiop2MkplhbYdxIvEEE1ZO8F1gyv+KF0+bqXToEMyfb//BrFljl8ZXrAhXXw2vvmr7IZVPedJS+NUYc3W6xwL8kv6Yv2hLIbC1amW/FG7cmPnY4o0f3ci6A+vYNXgXEWG693CBZoxddZ0yUL1+vT3euLEdpO7a1ZYB14HqXMtRS0FEbhOR27B1j74Rkf4i0g+YC6zwUqwqH4mJseONS5dmfs6IK0dw8NRBPlznpkmhCgYRO1Ppueds6e8dO2DcONv/+PzzttxGdDQ8/DD8+KNtiqo8l2lLQUTed/M6Y4y5zzsheU5bCoHt5Em76PX22zMvlGeM4YpJV3A84Tib/7WZ0BAtm6BcOHIEvv7atiC+/96uoI6MtDs8detm67eXKOHvKINGrmsfBSpNCoEvJgY+/thOPilZ0vU5n236jF6f9+KLXl9wW4PbfBugCj6nT8NPP9kEMXeuTRjh4XDttTZB3HqrHZdQmcpVUnCqlz4CVAfCUo4bY27NwxhzRJNC4Fu+3I4tvPMOPPCA63OSkpOo92Y9oopGsXTAUkT7jJWnkpJg8eK0cYgdO2w3VKtWaYX76tXzd5QBJ7dJYR12zcAG0moV5bp0dl7QpBD4jLFrlsLCYNWqzM+bsHICD379IAv7LaR9dZfV2pVyzxjYtCktQaT8g6tXLy1BtGxpF9kVcLlNCsuMMa28ElkuaVIIDm++adctrFoFzZq5PufMuTNUH1+d5pWa802fb3wboMqf/v4b5syxCSJlBXXFirZ7qVs3W5+pcGF/R+kXuV2nMF5EnhWRNiLSLOWWxzGqfKxPH7s26b33Mj+nSKEiPNryUb7d/i3rD673XXAq/6paFf71LztT6dAh+OgjaNfODnLdfLOt7Nqrl3187Ji/ow0YnrQUUgrY/UVa95ExxnT0cmxZ0pZC8Ojb135p27cPihVzfc7RM0eJHhdN9wbd+bC7TlFVXnL2rN16NGUDoYMHbf/mNdekrYeoUsXfUXpVblsK3YGaxpj2xpgOzs3vCUEFl5gYOH4cPvss83PKFCnDoOaDmLFhBruPacll5SUREXZrwHfftd9SFi+GoUMhNtaugaha1a6J+N//7MrLIJ6hmROeJIV1gBamUbnSrp0d73NXJA9gSOshiAjjlmolFeUDISHQpg28+KKt6rp5s70fFgbPPAOXXgp16sATT8Bvv9nZTvmcJ0mhAvCniHwvInNSbt4OTOUvInYDnsWL7SrnzFSNrErvS3szafUk4k7H+S5ApQDq14cRI+yGQfv22dZE3brwxhu2HlOlSrY+05w5+XYLUk/GFFzOD9QpqSq7Dh2yXbUPPwxjx2Z+3sZDG7n0nUv57zX/5Zn2z/guQKUyc/y43WFu9my7sjo+HooWhRtvtGMQt9ySeTngAKQrmlXAuP12O8a3d6/72YC3fHwLy/YuI3ZwLEUKFfFdgEplJTERfvklbaB6714IDbV9pF272luNGv6O0q1cDTSLyAkROe7czopIkogcz/swVUEQEwNxcXZfFXeGXzmcI6eP8P5adyW4lPKD8HC4/np46y27FmLFCnjySVtuY8gQu7tc06YwcqQtBx5kX7yz3VIQkW5AS2PMU94JyXPaUgg+yclQq5b9/2b+/MzPM8bQdkpbDp06xJaHtxAWEpb5yUoFiu3bbeth9mz4/XebEKKj01ZUt2tnB7H9LLdTUjMwxnwF6JRUlSMhIXac7uef3e+fIiIMbzucHf/s4Is/vvBdgErlRu3adnrrr7/CgQMwebJtNUycaFdQly8P99wDs2YF7Bakngw0py9bGQK0ANobY9p4MzBPaEshOO3da788jRhhy+RnJtkk0+CtBhQPL87KmJVaKE8Fr1On4Icf0rYgPXrUDqpdf71tQXTpYhOGj+S2pdAl3e1G4ATQNe/CUwXNJZfYKgPvvw/nzmV+XoiEMKztMFbvX838nW76mpQKdMWKQffuMG2aXUG9YIEtG7xxo52rXbEiXHUVvPIKbNvm11B19pHyizlz7CSNL7+0X5Qyk3A+gerjq3Np+Uv5oe8PvgtQKV8wxm47mjKTac0ae7xhw7RxiObNL67sun8/3HknfPJJjvaOyNGUVBH5P7e/ijGjsh1JHtOkELzOn4dq1Wx369dfuz/3pd9f4sn5T7Jq0CqaVdJajCof273bJoevvrLjEklJULmy/QbVrZutzxQeDg89ZBfWPfCAnQWVTTlNCkNdHC4GDACijDHFsx1JHtOkENyefhpeeAF27bLlZjITfzaequOq0rluZ2b0mOGz+JTyq6NH07Yg/e47u+NcyZLQoQN8843tey1SxG4slM3WQo7GFIwxr6bcgIlAEeBeYCZQM1sRKOXCgAF2iur7WSxFiIyI5IEWD/Dppk/Z8c8O3wSnlL+VKWPLC3/xhV0DMXeuXf35/fdpg3FJSTAqbztt3A40i0gZEfkfsB67FWczY8wIY8yhPI1CFUg1asB119lZe1nVGRvcejChEsqri1/1TXBKBZIiRWwpjQsTQGKi/VZ14ECeXSrTpCAiLwMrsLONLjXGjDTG/JNnV1YKu8I5Ntbug+JO5RKV6XtZX6asncLhU4d9E5xSgWbUKNu8Ti+PWwvuWgpDgcrA08C+dKUuTmiZC5VXuna1G2BlVVIbYNiVw0g4n8Aby9/wfmBKBaIlS2zrIL3ERFt+OI+4G1MIMcYUMcaUMMaUTHcrYYwpmWcRqAKtcGHo189OUT140P259cvWp2v9rry5/E1OJp70TYBKBZKUWkoX3lKmsuaBbJe5UCqvDRxop6hOm5b1ucPbDuefs/8wefVk7wemVAGkSUH5Xf36tk7Ye+9lXVCyTdU2tItux9ilYzmX5GY5tFIqRzQpqIAwcKBd3f+LB1s3Db9yOLHxsXyy6RPvB6ZUAaNJQQWEnj0hMtKzAeeb69xMo3KNGLNoDMFcpkWpQKRJQQWEokXh7rvtOp2jR92fGyIhXFn1SjYc2kDof0Op/lp1pm+Y7ptAlcrnNCmogBETAwkJ8NFH7s+bvmE6H22wJxkMu+N3M2juIE0MSuUBrZKqAkrLlnDmjC0cmdn2CdVfq87u+N0XHY8Ii2BQs0HUjapLnag61ClTh+jIaEJDQr0ctVLBxV3tI//vC6dUOgMHwv33w7Jl0Lq163Ni42NdHj97/iyT10zm1Lm0Ha3CQ8OpVboWdaLqULdMWrKoG1WXyiUq68Y9Sl3A50lBRKoCHwAVgWRgojFmvIiUAT4BqgO7gF5aVqPguesuePxxO+CcWVKIjox22VKoFlmNnY/t5MDJA2yN28q2o9vYFreNrUe3si1uG99v/56EpITU84sWKkrtMrVty8JJFHXK1KFOVB3KFS2nCUMVSD7vPhKRSkAlY8xqESkBrAK6Af2Bo8aYF0XkSaC0MWaEu/fS7qP8aeBAmDHD7iNS0sXa+ekbpjNo7iBOnzudeqxooaJM7DKRPpf2yfR9k00yf8f/zbaj22zSiNuWen/nsZ2cTz6fem5k4cgMrYqUZFE3qi6lIkrl6e+rlK/laD8FXxGR2cCbzu0aY8x+J3EsNMbUc/daTQr5U0rX0bvvwqBBrs+ZvmE6/5n/H2LjY4mOjGb0taPdJoSsnEs6x+743Rcli21Ht7H72G4Maf+flC1aNi1RpCSNqDrULlOb4uF+32ZEqSwFbFIQkerAr0BjINYYUyrdc/8YY0q7eM0gYBBAdHR08927L+5GUMHNGGjSxNZFWrHC39HYsYod/+ywXVEp3VJO0th3Yl+GcyuXqHxRsqgbVZeapWsSERbhp99AqYwCMimISHHgF2C0MWaWiBzzJCmkpy2F/Ov11+Gxx2ydr6ZN/R1N5k4lnmL70e0XJYttcds4fDqtxLcgREdGX9QVVadMHaqXqk6h0EJ+/C1UQRNwSUFECgHzgO+NMWOdY1vQ7iPlOHrUbk07YECOtqANCMfOHkvtiko/4L01bivxCfGp54WFhFGjVA2XM6SqRlYlRHQ5kcpbAZUUxE7pmIYdVB6c7vjLQFy6geYyxpjh7t5Lk0L+dvfdMG8e7NtnVzznF8YYjpw+4nLAe9vRbRkG0AuHFqZWmVouxzAqFa+kM6RUjgRaUrgK+A3YgJ2SCvAUsAz4FIgGYoHbjTFuCx5oUsjffvkFrrkGpk61ey4UBMYY9p/c7zJZbD+6ncSktA1WihUqlukMqagiURcljLwenFfBK6CSQl7SpJC/GQP16kGFCvDbb/6Oxv+SkpP4+/jfLge8d/6zkySTttF1qYhSGZLFoVOHmLJ2CmfPn009x5NpvCp/0qSggtaYMTBiBPzxBzRo4O9oAte5pHPsOrbL5aK92PjYDFNq0ysaVpSBzQZSqUQlKhWvlPqzYvGKRBWN0vGMfEqTggpaBw9ClSrw6KPw6qv+jiY4nT1/lqKji2aaGEoWLsnxhIu3XS8UUogKxStkSBYZ7pewyaNCsQo6eyrIaO0jFbQqVICuXe1Wnc8/b9cuqOyJCItwWxpk1+BdnEo8xYGTB9h/cj/7T+zP+PPkfnYd28WSv5dkmGabQhDKFi17UWvDVQIpWigfzRjIpzQpqIAXE2P3WfjqK7jjDn9HE5xGXzvaZWmQ0deOBqBYeDFqlalFrTK13L7PuaRzHDx18OLEkS6BbDq8iQMnD2QoG5KiZOGSbhNHys/IwpE6s8pPtPtIBbzkZKhZE+rUgR9/9Hc0wcuXs4+STTJxp+MyTRzpj505f+ai10eERVCxeMUsE0i5YuV03CMHdExBBb3//heefRb++ssmCJU/GGM4kXjCbcsj5f6xs8cuen2ohFK+WPksWx4Vi1ckPDTcD79hYNKkoILenj1QrRo8+SSMHu3vaJQ/nD1/1o57ZJFADp065HJQvUyRMh51XRWEooaaFFS+cMstsHo1xMZCmI6GqUycTz7P4VOHs2x5HDh5IMNiwBTFw4t71HVVpkiZoB330NlHKl+IiYFu3eDrr+2MJKVcCQsJs3+4S1SCSpmfZ4zh6Jmjbmddrdm/hm9OfsPJxJMXvT48NJyKxSumJZBMWh7li5UnLCR4/tRqS0EFjfPnIToamjeHuXP9HY0qSE4mnmT/if0XJ5ALkkncmbiLXitI6rhHVgnEk/LqeTFhQFsKKl8IC4P+/eGll+wYQ5Uq/o5IFRTFw4vbOlNRddyel5iUyIGTB1yPfTj31x9cz8GTBzOUJUlRKqJU6opyV11XK/ev5NkFz6bO2Nodv5tBc+1OVHk1k0xbCiqo/PUX1K5tZyM984y/o1EqZ5KSkzhy+kjq2Ia7sY/09aoyk7II0VM60Kzyleuug+3bYccOCNEp6iofM8YQnxCfmiCu/eBal+cJQvKzyS6fc3m+m6Sg/0upoBMTA7t360I2lf+JCKUiStGgXAM61uhItchqLs+LjozOs2tqUlBBp1s3iIqC997zdyRK+dboa0dfVD8qfbmSvKBJQQWdwoXhnntg9mw4dMjf0SjlO30u7cPELhOpFlkNQagWWS3P98TQMQUVlDZvhoYNoVMn+L//gzZt/B2RUsFDp6SqfOfYMTvI/N13sGABvPEGtGgBERG2JXHhT10BrZRn9H8VFZQWLky7n5AAgwa5Pz801HWySPnp7rns/nT3XKFCEKSVEVQBoUlBBaVrrrF/ZBMTbSvghRegRg2bIM6ezdnP+Hj7M7Nzki5ea5RtInmXYHKSrNavh8WLoWNHuOoqTVDqYpoUVFBq0wbmz7cthmuu8c2Ywvnz7pOGJz89OefUKTh6NPPnz53L/e8ycqT9GRZmWy+e3rJ7vi9vumYlb2hSUEGrTRvfDjCHhdlbsWK+u6Yryck5Szhz5tiaUcbYFkKHDtCqlU0y2bmdPOn6+Pnzro8ne76mKldCQoI7qaW/hYa6b8UtWeK9L0SaFJQKMiEhUKSIvWVHw4Z2wV9iIoSHw//+55ukmpyc/cTjq9vZs3DiROY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" }, "metadata": { @@ -557,6 +552,49 @@ "plt_graphs(pers, counts, labels=[\"del 0.5\", \"del 0.7\", \"del 0.9\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" ] }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": "Hi\nHi\nHi\n" + }, + { + "data": { + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Graph 5 modified vs\n", + "gs = []\n", + "x1s = [0, 0.7, 1]\n", + "pers = [[0.2, 0.3, 0.5, 0.7, 1] for _ in range(len(dels))]\n", + "counts = [[0, 0, 0, 0, 0] for _ in range(len(dels))]\n", + "for x1 in x1s:\n", + " gs.append(get_support_modified(0.7, 0.5, 1, x1, 1-x1))\n", + "\n", + "# print(get_supports(0.9, 0.5))\n", + "\n", + "for i in range(3):\n", + " for j in range(5):\n", + " percent = pers[i][j]/100\n", + " for k in gs[i]:\n", + " if k > percent:\n", + " counts[i][j] += 1\n", + "\n", + "plt_graphs(pers, counts, labels=[\"Nominal\", \"Modified\", \"TiW\"], colors=[\"b\", \"g\", \"r\"], markers=['.', 'o', '^'], x_label=\"Support threshold (%)\", y_label= \"Number of patterns (k)\")" + ] + }, { "cell_type": "code", "execution_count": 77, From c32d4bd8351451d67504dbfc81cbaff09eeb868e Mon Sep 17 00:00:00 2001 From: 26prajval98 <26prajval98@gmail.com> Date: Thu, 28 May 2020 18:45:35 +0530 Subject: [PATCH 13/13] many changes --- .vscode/settings.json | 3 ++ DWDM.ipynb | 75 ++++++++++++++++++++----------------------- 2 files changed, 38 insertions(+), 40 deletions(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7ad48a8 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.pythonPath": "C:\\Users\\appa_\\AppData\\Local\\Programs\\Python\\Python38-32\\python.exe" +} \ No newline at end of file diff --git a/DWDM.ipynb b/DWDM.ipynb index ecee956..4f2ce99 100644 --- a/DWDM.ipynb +++ b/DWDM.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -21,13 +21,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[111.36040229579802, 457.187622985742, 688.0489924627027, 899.6499803987468, 925.7600090876037, 992.1739498484766]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'])\n" + "text": "\nInput :\n\n1 2 269 4701 \n\n\n\nTime stamp\n\n[94.36192205809746, 117.6714270956375, 483.9212648643609, 533.4169600885405, 558.400191175879, 753.794192889342]\n\n\nSequence\n\n['9033', '184577138778', '128517057890', '404969087443', '07651739', '36279693']\ndict_keys(['0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96'])\n" } ], "source": [ @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "7121924259], ['3467', 445.1489955115996], ['6711', 469.7305770240133], ['15376643', 510.8143668765582], 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"execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -423,13 +423,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": "0.028330524812387403\n" + "text": "0.005656419848711377\n" } ], "source": [ @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -474,13 +474,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" }, "metadata": { @@ -512,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -522,8 +522,8 @@ }, { "data": { - "image/png": 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" }, "metadata": { @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -564,8 +564,8 @@ }, { "data": { - "image/png": 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" }, "metadata": { @@ -597,13 +597,13 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" }, "metadata": { @@ -634,18 +634,13 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": "0.3\n0.5\n0.7\n0.9\n0.3\n0.5\n0.7\n0.9\n0.3\n0.5\n0.7\n0.9\n" - }, { "data": { - "image/png": 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Bn4D3ga+wbg7/S0T+KyIdvRFkSYSGhnL06NESV5LlmTGGo0ePEhpqbxiKrYe3csMXN9CwRkMW3bGIaiHVin+TUqpCKOpKYY0x5rlCtr0qIhGAG7oUu1ejRo1ITk7m8OHDTofiVaGhoTRq1KjY/ZJTk+kzrQ+Vgirx7Yhviaga4YXolFLlRaFJwRizCEBEIo0xu/NuE5GrjDFrgEOeDa/kgoODiYqKcjoMn3T87HH6TutLyrkUVo5aSVQt/XdSSuVn587i166RTQEQkV7Ax54LSXnC2fSzDJo+iB3HdjBv2Dw61evkdEhKKR9kJyncB8wVkXoi0h94A+jv2bCUO2VkZTBs9jB+2PsD0wZP49qoa50OSSnlo4p9JNUYs0ZEHsQazfQccL0xpmI12Jdjxhj+vvDvzN8+n7f6vcWt7W51OiSllA8rNCmIyALyj1RaBUgBpogIxphBng5Old2zsc/y0YaPmHjNRO6/+n6nw1FK+biirhRe9loUyiPeW/Mek1ZO4p7L72HStWWfVU4p5f+KSgorTTEP+4uIFLePcsasrbMYt3gcA1sO5D8D/lPuOvMppZxR1I3m5SLygIjk64sgIiEicp2IfIY1xLXyMbG7Yxn+9XC6Nu7K9CHTCQrQ0UyUUvYUVVv0Be4GvhSR7FnSQoFArJvOrxlj7E0dprxm04FN3Dj9Ri6tfSkLbl9AleAqToeklCpHiuq8dg54F3hXRIKBusBZY8wJbwWnSmb3id30jelLjUo1+Gb4N9SuXNvpkJRS5YytdgXXtJn7PRyLKoPDpw/TZ1ofzmWcY/Wo1TQOa+x0SEqpckgbm/3AqbRT3PDFDexN2ct3d35Hu4h2ToeklCqnNCmUc+mZ6Qz5agjr9q9jzm1z6N6ku9MhKaXKsWKHuRCRqiLW7Csi0lJEBrnuMSiHZZks7pl/D0t3LeX9Ae8zqJX2J1RKlY2dsY9WAqGuQfGWAaOATz0ZlLLnie+eYGrCVCZdO4nRV4x2OhyllB+wkxTEGHMGuBl4yxgzGGjr2bBUcV6Nf5WX4l5i3FXjmHjNRKfDUUr5CVtJQUS6AsOBRa51ei/CQTEJMUz4dgJD2g7hjb5vaG9lpZTb2EkKDwFPAnOMMVtEpBmw3LNhqcJ8u+tbRs4bSXRkNFMHTyUwINDpkJRSfsTO0Nkrse4rZL/+FXjQk0Gpgq35fQ03z7iZduHtmHvbXEKD7M3JrJRSdhWbFESkJfAoEJl3f2PMdZ4LS11ox9Ed9P+iP+FVw1kyfAlhoWFOh6SU8kN27g3MBP4DfARkejYcVZADpw7QZ1ofAJaOWEr96vUdjkgp5a/sJIUMY8x7Ho9EFSj1fCr9Yvpx6PQhlt+1nJZ1WjodklLKj9m50bxARMaKSH0RqZ394/HIFOczznPT9JvYfGgzs4fO5qqGVzkdklLKz9m5UsieM+GxPOsM0Mz94ahsmVmZ3DnnTpbvXs7UwVPpc2kfp0NSSlUARSYF1/AWI4wxP3gpHgUYYxj/zXhmbp3Jy9e/zIiOI5wOSSlVQRTZfGSMyULnava6F1a/wNtr3mZC1wlM6DbB6XCUUhWInXsK34rILaLdZr1iyvopTPx+IsM7DOfF6190OhylVAVj557CI0BVIFNEzgICGGNMDY9GVgEt2L6AMQvH0Kd5Hz6+8WMCxE7OVkop97HTo7m6NwKp6OL2xTF01lCurH8ls4bOIiQwxOmQlFIVkJ35FERERojIM67XjUXkas+HVnFsPbyVAV8MoHGNxiy6YxHVQqo5HZJSqoKy0z7xLtAVuMP1+hTwjsciqmD2peyjz7Q+VAqqxNIRSwmvGu50SEqpCsxOUviTMWYccA7AGHMcKFPbhojUFJFZIpIkIttEpKurU9x/RWSH63etshyjPDh29hh9Y/qSej6VJcOXEFUryumQlFIVnJ2kkC4igVgd1hCRcCCrjMd9A/jGGNMauAzYBjwBLDPGtMCa4e2JMh7Dp51NP8ugLwex89hO5g2bR6d6nZwOSSmlbCWFN4E5QISITAZWAy+U9oAiUgPoCUwBMMakGWNOADcCn7l2+wy4qbTH8HUZWRkMmz2MuH1xxNwcQ3RktNMhKaUUYO/poxgRWQf0xnoc9SZjzLYyHLMZcBj4REQuA9ZhTeRziTFmv+uY+0UkoqA3i8gYYAxAkyZNyhCGM4wx/H3h35m/fT5v93ubIW2HOB2SUkrlsPP00VRjTJIx5h1jzNvGmG0iMrUMxwwCrgDeM8ZcDpymBE1FxpgPjDGdjTGdw8PL303ZZ2Of5aMNH/H0NU8z7upxToejlFL52Gk+apf3hev+wpVlOGYykGyM+cn1ehZWkjgoIvVdx6gPHCrDMXzSu2veZdLKSYy+fDTPX/u80+EopdRFCk0KIvKkiJwEOopIquvnJFZlPb+0BzTGHAD2iUgr16rewFbXZ2aPyHoXMK+0x/BFs7bO4v7F9zOo1SDeG/AeOmqIUsoXFXpPwRjzAvCCiLxgjHnSzcd9AIgRkRDgV2AUVoL6SkTuAfYCt7r5mI6J3R3L8K+H07VxV7685UuCAuyMLqKUUt5np3a6qPeyiCwzxvQu7UGNMRuBzgVsKvVn+qpNBzZx4/QbubT2pSy4fQFVgqs4HZJSShWq0KQgIqFYA+HVdXUky27vqAE08EJs5d5vx3+jb0xfalSqwTfDv6F2ZZ2wTinl24q6UrgPGI+VANaRmxRS0WEuinX49GH6TOvD+YzzLLt7GY3DGjsdklJKFauoewpvAG+IyAPGmLe8GFO5dyrtFDd8cQP7Uvex7K/LaBve1umQlFLKFjud194SkfZAWyA0z/rPPRlYeZWemc6Qr4awfv965tw2h26NuzkdklJK2VZsUhCRZ4ForKSwGOiHNdSFJoULZJks7p5/N0t3LeWjgR8xsNVAp0NSSqkSsdN5bQjWU0EHjDGjsAawq+TRqMqpx//7ONMSpvHPa//JPVfc43Q4SilVYnaSwlljTBaQ4RrM7hDW+EUqj1fjX+Xl+JcZd9U4nrrmKafDUUqpUrHTT2GtiNQEPsR6CukU8LNHoypnYhJimPDtBG5teytv9H1DeysrpcotOzeax7oW/yMi3wA1jDEJng2r/Fi6cykj543k2shrmTp4KoEBgU6HpJRSpWan+QgRuVlEXsUanqK5Z0MqP9b8voZbvrqFduHtmHPbHCoF6a0WpZQX7N8PvXrBgQNu/2g7Q2e/C/wNSAQ2A/eJSIXvvLbj6A76f9Gf8KrhLBm+hLDQMKdDUkpVEAfGTSJr1WoOjJvk9s+2c6XQC+hjjPnEGPMJ0B/rEdUKa//J/fSZ1geApSOWUr96fYcjUkpVFN//ew3hc94nwGRR4+tPWLvQvVcLdpLCdiDvFGeNgQp7TyHlXAr9Yvpx6PQhFt+xmJZ1WjodklKqAshMy2TFLW/S44luBJAFQACZyD/de7VgJynUAbaJSKyIxGLNfRAuIvNFpNTzKpRH5zPOc9OMm9hyeAtf3/Y1VzW8yumQlFIVwM55W9hWuzu9vn6IQLJyBqILJY1OGz9x670FO4+k/sNtRyvHMrMyuXPOncTujmXa4Gn8pflfnA5JKeXnzqeeJ37g/9Ft5QukShj72/Wm3o5VkJaWs0+gyYRJk+Ad99zqtfNI6gq3HKkcM8bw0DcPMXPrTF6+/mWGdxzudEhKKT+X+H4cVR4cTXTaNlZHjaDNkteoP+z6fAkBsF7HxbntuDoFmA3/t+r/eGfNOzza9VEmdJvgdDhKKT+WmpzKhv5PcU3iu/wR2Ji1k5bQ4+m+1sYNGzx+fFv9FCqyKeun8PTypxnRcQT/vv7fToejlPJja55bxKnIdlyT+C6rOj1IzeQtdM5OCF5SaFIQkWWu3xW2JlywfQFjFo6hT/M+fDzoYwJEc6hSyv0ObzlEXNPbuep/B3A6qCZbP4qn14bXqVavmtdjKar5qL6I9AIGich0cmdeA8AYs96jkTksbl8cQ2cN5cr6VzJr6CyCA4OdDkkp5WdMluGHv02l7UcPc6U5Rex1z9Nt3uOEVAtxLKaiksI/gCeARsCrF2wzwHWeCsppWw5tYcAXA2hcozGL7lhEtRDvZ2ullH/bt/I3Dt78N3oc/ZaE6t2p+sWHRA9o43RYRU7HOQuYJSLPGGPc35faR+1L2UffmL5UCqrE0hFLCa8a7nRISik/kpmWyaqhb3LVvKepSQArbnuHa6b9jYAg32ietvNI6iQRGQT0dK2KNcYs9GxYzjh29hh9Y/qSej6VlSNXElUryumQlFJ+5JdZCaSPHE306TX8HDGARvPfpdefGjsdVj52BsR7AXgIqyfzVuAh1zq/cib9DIO+HMTOYzuZN2wel9W7zOmQlFJ+4tyJc8T2eJqoW68k4sxu4h6czlX759PAxxIC2OuncAPQyTX7GiLyGbABeNKTgXlTRlYGw2YNI25fHF/d+hXRkdFOh6SU8hOb3l5F9Qn3Ep22ndXN76Ltklfo1qKO02EVym4jVs08y341RrQxhr8t/BsLflnA2/3fZkjbIU6HpJTyAyl7U1jZ7u9c9kBPgrPOs+6Fb+mx81Nq+3BCAHtXCi8AG0RkOdZjqT3xo6uEfyz/B1M2TOHpa55m7FVji3+DUkoV46eJ82nyr7F0z9pP7JWPcNXi52kcUdXpsGyxc6P5S9foqFdhJYXHjTHun+7HS2ISY5i4bCJ7U/ZSM7Qmx88dZ/Tlo3n+2uedDk0pVc4d3nyQHf0epFvyV/wS2oFj735N9KirnQ6rRGyNfWSM2Q+U+2GyYxJjGLNgDGfSzwBw/NxxAiWQnk17IiLFvFsppQpmsgyrR39K+08ncIU5Q+z1k+k+9zGCq5S/Tq++8WCsl0xcNjEnIWTLNJk8s/wZhyJSSpV3e77fxYa613PNJ3ezt3p7/li8iehvnyqXCQEqWFLYm7K3ROuVUqowGecyiB3wMuG9O3Dp8Z9Zeft7dDgaS7N+rZwOrUyKTAoiEiAim70VjKc1CWtSovVKKVWQ7TM28kudLkQveozEetdzes02en7hO72Sy6LIErj6JmwSEb+oNSf3nkyV4Cr51lUJrsLk3pMdikgpVZ6cPXaW2K5P0nxYZ+qeSyb+kZlc/ftc6ndu6HRobmPnRnN9YIuI/Ayczl5pjBnksag8ZHgHa8a07KePmoQ1YXLvyTnrlVKqMBvfWEHYY/cSnb6DVS3upv2Sl+javLbTYbmdGGOK3sEaPvsivjBNZ+fOnc3atWudDkMp5cdS9pxgU9/H6Zn0AXuCmnH0/z7gisd6Ox1WmYjIOmNM54K22ZqjWUSaAi2MMd+JSBUg0N1BKqWUr/nx8TlEvjyO7lkHib3qMa5e/BxN61Yp/o3lmJ0B8e4FZgHvu1Y1BOaW9cAiEigiG0Rkoet1lIj8JCI7RGSGiDg3y4RSqkI7uHE/Pza8hS4v3kxKSAS/TP2Z6J9fpIqfJwSw90jqOKA7kApgjNkBRLjh2A8B2/K8/jfwmjGmBXAcuMcNx1BKKdtMlmHlXVOodHlbOv2xiNg+L9Ds6BrajLjS6dC8xk5SOG+MSct+ISJBWDOvlZqINMIaffUj12vBmsltlmuXz4CbynIMpZQqiT3LdrKxTm96fj6a3WGXceDbRKK/eaLcdkIrLTtJYYWIPAVUFpHrgZnAgjIe93Xgf4As1+s6wAljTIbrdTJWM9VFRGSMiKwVkbWHDx8uYxhKqYou41wGsf3+TcSfO9DsxHpW3vkBHY98T+T1LZwOzRF2ksITwGEgEbgPWAw8XdoDisgA4JAxZl3e1QXsWuDViDHmA2NMZ2NM5/BwnSpTKVV622LWs6P21UR/8wSb6vfj7Lqt9Pz8Xr/ohFZadp4+ynJNrPMTVkW93RT3HGvRugODRKQ/EArUwLpyqCkiQa6rhUbAH2U4hlJKFerMkTP8fMP/0uPnVzgaEM6Pj82my4s3Ox2WT7Dz9NENwC7gTeBtYKeI9CvtAY0xTxpjGhljIoFhwPfGmOHAciB7hpu7gHmlPYZSShVmwyvfc7h+R6J/fpG4VqMI/XWbJoQ87FwjvQJca4yJNsb0Aq4FXvNALI8Dj4jITqx7DFM8cAylVAV14rfjrGw1mssf7Y0RYcMr39Mz6UPCmtYs/q89NL8AABWWSURBVM0ViJ1hLg4ZY3bmef0rcMgdBzfGxAKxruVfgfI1G4VSyueZLMOP//M1zV67n25Zh4n90+P8afGzRNau7HRoPqnQpCAi2ddTW0RkMfAV1j2FW4E1XohNKaXKZP/a39kz6H667p/LtspXcHzKYqJvv9zpsHxaUVcKA/MsHwSyx0A6DNTyWERKKVVGWRlZrB75EZfFPMZlpBHb/0V6zH6YoFBbk01WaIX+CxljRnkzEKWUcofflv7CidvG0DNlBetrXUedme8T3ftSp8MqN4pNmyISBTwARObdvzwOna2U8l/pZ9L5YfDLdPn2f6kllVk1cgo9poxCAnT+9ZKwcy01F+tJoAXk9kBWSimfsfXztQTcN5roc5uIbziE5ovf4pqO9ZwOq1yykxTOGWPe9HgkSilVQqcPnWZN/2e5Zt1rHAqox09PzKHrCzpsWlnYSQpviMizwLfA+eyVxpj1HotKKaWKsf7F76g7cQzRGb+xss19XPbNv/lTkzCnwyr37CSFDsCdWKOYZjcfGddrpZTyquO7jrG57wSu2fkpvwW3ZNObK+j5QE+nw/IbdpLCYKBZ3uGzlVLK20yWIf6RmbR48wG6mGPEdnuKLoueIapmqNOh+RU7SWETUBM39WJWSqmS2r8mmb0Dx9Lt4AK2VunMsU++JXroZU6H5ZfsJIVLgCQRWUP+ewr6SKpSyqOyMrJYfef7dJr+OB3IIHbQK/SY8aB2QvMgO/+yz3o8CqWUusCvi5M4efu99ExdzbrafyZ89vtERzdzOiy/Z2c+hRXeCEQppQDSTqURd9OLdF02iTNSldWjP6X7+3/VTmheYqdH80lyZ0ELAYKB08aYGp4MTClV8Wz55GeCx44m+lwicY1vo8XiN+jR/hKnw6pQ7FwpVM/7WkRuQoe4Vkq50amDp1nX72mu2fAGBwMa8PPT8+k2aWDxb1RuV+KJSI0xc9E+CkopN1k7eSnHG7an14bXWdX+71Tds5WrNSE4xk7zUd556gKAzuQ2JymlVKkc/eUo2/o9TI9fp7IrpDUJb66i19geTodV4dl5+ihvys4AdgM3eiQapZTfM1mG+Iem0+Kdh/iTOU7sNc/QZf5ThGonNJ9g556CzquglHKL3+P38vuNY+l2eBGbq17N8c+WEX1LB6fDUnkUNR3nP4p4nzHGTPJAPEopP5SVkcWq29/lillPUpMsVgx+nR7T7ycwJNDp0NQFirpSOF3AuqrAPUAdQJOCUqpYO+dv5ezw0fQ6Fc/aOn+h3tz36dUj0umwVCGKmo7zlexlEakOPASMAqYDrxT2PqWUAjh/Mo34QS/QLXYyp6Q6q+/7nO7vjtBOaD6uyHsKIlIbeAQYDnwGXGGMOe6NwJRS5VfiB/GEPngv0ee38EPTO2i56DV6tItwOixlQ6H9FETkJWANcBLoYIx5ThOCUqooJ/84yYrLHqTdfd2pkpHKmmcX0n13DOGaEMqNojqvTQAaAE8Df4hIquvnpIikeic8pVR5seb5JaQ0ac81CW+zqsM4auzdwlXP3eB0WKqEirqnUOLezkqpiufItsMk9X+YHrtj2BXShi3vrKbXfd2cDkuVklb8SqlSMVmGH/4+DWnXhqt3f0Vs9HM0OryBDpoQyjWdqUIpVWLJP+xh/01/o/uRb0is1oXK0z4i+sZ2Toel3ECvFJRStmWmZRJ785vU7NGONkdWseKWN2l7dDWXakLwG5oUlFKF278fevWCAwfYMWcz22p3J3rOQ2wL70lK3FZ6zXpAeyX7GW0+UkoV6sC4SUSsWs3u9gNpenQTqRLGD2Nj6PbW7doJzU9pUlBK5XPyJOzZdILt7y9n4JyPCCCLZkfXsiriFtqu/A/dW9V1OkTlQZoUlKpgMjPh99/h119h9y9pnFy7nYDNCVTfnUj9o4m0TkugPcm0z/OeNIKoEnUJdTQh+D1NCkr5odRU+O03q+L/9VfYtdOQujWZKjsTCD+QSNusRDqSQHeSCCYDgHQJ5kCtNqQ06UVqVCSt579EYGYaACFk0GnjJ3DgGahXz8GSKU/TpKBUOZSZCcnJuZV+3p9DO1NpcCyRDlg/V5PAaEkkzKTkvP9k7Saca9GBU1cMpHrXDgRd3oHgVq1oHBxMY4CxYyEQyMw9ZqDJhEmT4J13vF1c5UWaFJTyUSkpBVf6v/4Ku3cDGem05Bc6kMhlksi9lRNom5VIvXN7cj4js2oNTPsOBF1xB3ToYP20b0/1mjWpXtTB4+MhLS3/urQ0iIvzQEmVL/F6UhCRxsDnQD0gC/jAGPOGa0TWGUAk1pSfQ3UAPuXPMjJg377CK/5jx7L3NDTgD7pXT+SGsEQuD0ygee1ELjm2jcAMq+I2gUFIVCvo2A063GdV/h07Eti4MUgpnhLasMFt5VTlixNXChnABGPMetc8DetE5L/ASGCZMeZfIvIE8ATwuAPxKeU2x48XXunv2WM1A2ULDoa2TU4RXXcz4zsk0iYjkcYnEqiVnEhQyjFrvOKTQMOGcGVH6NAnp/KXVq2gUiWniqn8iNeTgjFmP7DftXxSRLYBDYEbgWjXbp8BsWhSUD4uPR327i284j9xIv/+4eHQrBl0vSqDB67fSUdJpNnpRCIOJlB5ZyKy61fY5dq5WjVo3x5uG5Lb9NOhA9Su7fVyqorD0XsKIhIJXA78BFziShgYY/aLSIEDsIvIGGAMQJMmTbwTqKqwjLGacQqr9Pfuhays3P1DQiAqylXxd4VmUYY2tQ/SKi2RBscSCd2eAImJMG8rnDtnvSkgAFq2hM6dYdSonLN/mja1tinlRWKMcebAItWAFcBkY8zXInLCGFMzz/bjxphaRX1G586dzdq1az0dqvJzaWlWU05hFX/qBbOHRERA8+ZWxZ/3p3n9M9Q/toWALYmQ4Kr8ExPh8OHcN9erZ1X42Wf9HTtCmzYQGurdQqsKTUTWGWM6F7TNkSsFEQkGZgMxxpivXasPikh911VCfeCQE7Ep/2MMHDlSeKWfnJz/bL9Spdyz/R498lf8UVFQrXKm9cZEV+W/wFX579xpHQygShWr6WfQoNzKv0MHqKudv5Rvc+LpIwGmANuMMa/m2TQfuAv4l+v3PG/Hpsqv8+ets/1duwqu+E+dyr9/vXpWJd+z58Vn/PXr52m1OXw4t/Kf76r8N2+Gs2et7SJw6aVWpT98eG7l36yZNv2ocsmJK4XuwJ1AoohsdK17CisZfCUi9wB7gVsdiE35KGOs+rmos/28LaGhobmVfHR0/ko/MhKqVr3gAOfOwdat8N88zT4JCXDwYO4+4eFWpX9f7iOftG1rXRUo5SecePpoNVDYg9O9vRmL8i2xsTB/vvXEZaVKF1f8p0/n379BA6uSv/bai8/269Ur5PH8rCyr51dinnb/hATYsSO3DSk0FNq1g3798jf9XHKJh/8FlHKe9mhWjjhzBjZtgnXrrJ+VK62KP68qVXIr+d69Lz7br1y5mIMcO5a/8s9u+snbltSsmVXpDx2amwAuvRQCdY4AVTFpUlAed+YMbNyYmwDWrYNt23I7boWHQ82a1pm9MVZT/BNPwD//abMz7vnzkJSUv/JPSIA//sjdp3Ztq8LP+8hnu3ZWXwClVA5NCsqtTp8uOAFkt8xERMCVV8JNN1m/r7wSGjWCH3+E4dft59Nzw7grZAYDBtS7OCEYY3UMuLDy3749N8OEhFjt/L1752/6qV+/dMM9KFXBaFJQpXbq1MUJICkpNwFccolV6d98c24CaNiw4Lq5a1eI6zeJiLmrie8/iXptJsOqxPyV/+bN+TsNREZaFf5NN+VW/i1aWONFKKVKxbHOa+6gnde858IEsHatlQCy/3zq1cut+LN/GjSwcXJ+/Lh1pv/jj/D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