From d9131088a2142d65a892a9a244ab821fc72ca48f Mon Sep 17 00:00:00 2001 From: leon Date: Thu, 30 Nov 2023 13:52:14 +0100 Subject: [PATCH] Changed LR and makefile --- Makefile | 16 ++- bin/LifNet | Bin 162096 -> 194864 bytes conf/config_EI.yml | 1 - conf/config_LR.yml | 13 +- conf/config_LR.yml.bak | 17 ++- obj/globals.o | Bin 199096 -> 207552 bytes obj/lif_network.o | Bin 82656 -> 106744 bytes obj/main.o | Bin 7480 -> 7464 bytes obj/sparse_mat.o | Bin 61720 -> 67832 bytes org/LR_multi.org | 298 ++++++++--------------------------------- org/single.org | 29 ++-- src/globals.cpp | 9 +- src/globals.hpp | 1 - src/lif_network.cpp | 136 +++++++++---------- src/sparse_mat.cpp | 107 +++++++-------- src/sparse_mat.hpp | 4 +- src/utils.hpp | 2 +- 17 files changed, 225 insertions(+), 408 deletions(-) diff --git a/Makefile b/Makefile index 9961987..f90769b 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,9 @@ CC = g++ # Compiler flags -CFLAGS = -Wall -std=c++17 -pthread -Ofast -s +CFLAGS = -Wall -std=c++17 -Ofast -s -march=native -funroll-loops -ftree-vectorize -ffast-math -fomit-frame-pointer -fexpensive-optimizations +CFLAGS_DEBUG = -Wall -Wextra -std=c++17 -O0 -g + # INCLUDES = -I ~/mambaforge/include LIBS = -lyaml-cpp # LIBS = -L ~/mambaforge/lib -Wl,--disable-new-dtags,-rpath,~/mambaforge/lib -llapack -lblas -larmadillo -lyaml-cpp @@ -24,14 +26,24 @@ OBJS := $(SRCS:$(SRCDIR)/%.cpp=$(BUILDDIR)/%.o) # Build both Release and Debug by default all: $(TARGET) +debug: $(TARGET) + $(TARGET): $(OBJS) $(CC) -o $(TARGETDIR)/$@ $^ $(CFLAGS) $(INCLUDES) $(LIBS) +# Create a special rule for the debug build +$(TARGET)_debug: $(OBJS) + $(CC) -o $(TARGETDIR)/$@ $^ $(CFLAGS_DEBUG) $(INCLUDES) $(LIBS) + $(BUILDDIR)/%.o: $(SRCDIR)/%.cpp +ifndef DEBUG $(CC) $(CFLAGS) $(INCLUDES) $(LIBS) -c $< -o $@ +else + $(CC) $(CFLAGS_DEBUG) $(INCLUDES) $(LIBS) -c $< -o $@ +endif clean: rm -rf $(BUILDDIR)/*.o $(TARGETDIR)/$(TARGET) # Recompile if any headers change -.PHONY: clean +.PHONY: clean debug diff 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@@ -45,7 +45,7 @@ A_STIM: [0.1, 0.0] # std of the stimulus STD_STIM: [0.0, 0.0] # Phase of the 1st stimulus -PHI_STIM: [180.0, 0.0] +PHI_STIM: [0.0, 0.0] # Tuning of the stimulus KAPPA_STIM: [0.0, 0.0] @@ -120,15 +120,14 @@ KAPPA: [3.5, 0.0, 0.0, 0.0] # Low rank LR_SEED: 3 -LR_LOAD: 1 +LR_LOAD: 0 LR_RANK: 3 LR_MEAN: [0.0, 0.0, 0.0] LR_STD: [1.0, 1.0, 1.0] # xy, xz, yz -LR_RHO: [0.5, 0.5, 0.5] - -COV_SD: 0.5 +# LR_RHO: [0.05, 0.8, 0.2] +LR_RHO: [0.0, 1.0, 0.0] ############## # STP diff --git a/conf/config_LR.yml.bak b/conf/config_LR.yml.bak index 91344f5..63fe8a6 100644 --- a/conf/config_LR.yml.bak +++ b/conf/config_LR.yml.bak @@ -16,7 +16,7 @@ verbose: 1 # Time step in ms DT: 0.05 # total simulation time in ms -DURATION: 15000.0 +DURATION: 8000.0 # time to start showing simulation result ms T_STEADY: 0.0 # Saving to files every T_WINDOW in ms @@ -30,7 +30,7 @@ N: 40000 # Number of populations N_POP: 2 # Average number of presynaptic inputs -K: 2000.0 +K: 4000.0 # Fraction of neurons in each population FRAC: [0.8, 0.2] @@ -120,15 +120,14 @@ KAPPA: [3.5, 0.0, 0.0, 0.0] # Low rank LR_SEED: 3 -LR_LOAD: 1 +LR_LOAD: 0 -LR_RANK: 2 -LR_MEAN: [0.0, 0.0] -LR_STD: [1.0, 1.0] +LR_RANK: 3 +LR_MEAN: [0.0, 0.0, 0.0] +LR_STD: [1.0, 1.0, 1.0] # xy, xz, yz -LR_RHO: [0.0, 0.0] - -COV_SD: 0.0 +# LR_RHO: [0.05, 0.8, 0.2] +LR_RHO: [0.0, 1.0, 0.0] ############## # STP diff --git a/obj/globals.o b/obj/globals.o index bc6cf88e4b695e980345584b02fc7578938403c7..bdf1d60b9bba1d49fde8d1f543825decc27f9849 100644 GIT binary patch literal 207552 zcmeFa4SZC^)jxhW*-I7(+(l7S#j@a9gQ6yiEdjJyN#tI=i}95pFQU8%N&#Ubh(Lnb zE!_1QMwbQI|c4d(tk7DTcm$F-0w;M47f9;e-_+Z@yy0EM}|e< z*5SDg&s-Tc5AN-F=Ht0ThAn`*5YHk!^)jph?qWQ5;%US~Wsc&B;rTwEC3q;z!t(>^ zx8dF;{c*T=OaBkyE|dOy;4a7WQ#?PDVfVrPx%B@6?)}pLOSmhf|3SFFlKx-AeMtI$ z1NXPm{|MaQN&lm8S4#h5a37ccC*b~3`u_y?N$GzI?keg3Gu*#O|I=`vk^a?ipOyYK zaMw!zb8y#5e-qqh>3<&Xdg)KVO-g?Y+*Uk0@cc`LZGig%9zrS+4E~kB;&~CzzwvyA 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The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (100, 32000) + inhomogeneous part. +#+end_example +:END: #+begin_src ipython print(end_point.head()) @@ -297,11 +237,11 @@ Low rank #+RESULTS: : trial phi m0 m1 phase -: 2320 0 0 3.802875 3.069512 0.051333 -: 2321 0 1 3.876875 2.539821 3.154328 -: 2322 1 0 3.998500 2.082789 1.355927 -: 2323 1 1 3.936000 3.221273 3.179001 -: 2324 2 0 3.895000 3.018065 1.699571 +: 3000 0 0 3.550875 3.105146 1.779070 +: 3001 0 1 3.479250 3.330270 2.245522 +: 3002 1 0 3.473750 3.219861 0.194016 +: 3003 1 1 3.637000 3.703045 2.282712 +: 3004 2 0 3.458125 2.993749 0.749735 *** Phases #+begin_src ipython @@ -333,165 +273,45 @@ Low rank #+end_src #+RESULTS: -[[file:./.ob-jupyter/cf56ea9a62dd4a73187c162bc9c78055d5573d5a.png]] - -#+begin_src ipython +[[file:./.ob-jupyter/f8ebcaa796d218aee7db8b4eb81e67536166f07b.png]] -#+end_src - -#+RESULTS: #+begin_src ipython from matplotlib.patches import Circle fig, ax = plt.subplots() - phi = end_point['phase'] - m1 = end_point['m1'] + phi = end_point[end_point.phi==0].phase + m1 = end_point[end_point.phi==0].m1 + theta = np.array([m1 * np.cos(phi), m1 * np.sin(phi)]) + + ax.plot(theta[0], theta[1], 'or', label='A') + phi = end_point[end_point.phi==1].phase + m1 = end_point[end_point.phi==1].m1 theta = np.array([m1 * np.cos(phi), m1 * np.sin(phi)]) - print(theta.shape) - ax.plot(theta[0], theta[1], 'ob') + ax.plot(theta[0], theta[1], 'ob', label='B') + # ax.plot(theta[0][0], theta[1][0], '*', ms=10, color='b') # ax.plot(theta[0][-1], theta[1][-1], 'o', ms=10, color='r') ax.axis('off') # ax.set_title('Dual Go') # Create a circle patch circle = Circle((0, 0), radius=2.5, color='blue', fill=False, lw=20, alpha=0.1) - # Add the circle to the current Axes ax.add_patch(circle) - + plt.axvline(x=0, ls='--', color='k', alpha=0.1) + plt.axhline(y=0, ls='--', color='k', alpha=0.1) + plt.legend(fontsize=14, frameon=False) # Set equal scaling by changing the axis limits or the plot aspect ratio ax.axis('equal') - plt.savefig('trajectory_A.svg', dpi=300) + plt.savefig('errors_' + name + '.svg', dpi=300) plt.show() #+end_src #+RESULTS: -:RESULTS: -: (2, 40) -[[file:./.ob-jupyter/db9e66e8005b86a59fc22230e9900fad6b15a267.png]] -:END: - -#+begin_example - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. - with pd.option_context('mode.use_inf_as_na', True): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. - with pd.option_context('mode.use_inf_as_na', True): -#+end_example -# [goto error] -#+begin_example - --------------------------------------------------------------------------- - ValueError Traceback (most recent call last) - Cell In[170], line 9 -  6 ax[0].set_ylabel('m1 (Hz)') -  7 # ax[0].set_yticks([0, 90, 180, 270, 360]) - ----> 9 sns.lineplot(data=df_code, x='time', y=df_code['phase']*180/np.pi, legend=False, lw=2, ax=ax[1], hue=['phi'], alpha=0.25) -  11 ax[1].set_xlabel('Time (s)') -  12 ax[1].set_ylabel('$\phi$ (°)') - - File ~/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/relational.py:618, in lineplot(data, x, y, hue, size, style, units, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, estimator, errorbar, n_boot, seed, orient, sort, err_style, err_kws, legend, ci, ax, **kwargs) -  615 errorbar = _deprecate_ci(errorbar, ci) -  617 variables = _LinePlotter.get_semantics(locals()) - --> 618 p = _LinePlotter( -  619  data=data, variables=variables, -  620  estimator=estimator, n_boot=n_boot, seed=seed, errorbar=errorbar, -  621  sort=sort, orient=orient, err_style=err_style, err_kws=err_kws, -  622  legend=legend, -  623 ) -  625 p.map_hue(palette=palette, order=hue_order, norm=hue_norm) -  626 p.map_size(sizes=sizes, order=size_order, norm=size_norm) - - File ~/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/relational.py:365, in _LinePlotter.__init__(self, data, variables, estimator, n_boot, seed, errorbar, sort, orient, err_style, err_kws, legend) -  351 def __init__( -  352 self, *, -  353 data=None, variables={}, -  (...) -  359 # the kind of plot to draw, but for the time being we need to set -  360 # this information so the SizeMapping can use it -  361 self._default_size_range = ( -  362 np.r_[.5, 2] * mpl.rcParams["lines.linewidth"] -  363 ) - --> 365 super().__init__(data=data, variables=variables) -  367 self.estimator = estimator -  368 self.errorbar = errorbar - - File ~/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:640, in VectorPlotter.__init__(self, data, variables) -  635 # var_ordered is relevant only for categorical axis variables, and may -  636 # be better handled by an internal axis information object that tracks -  637 # such information and is set up by the scale_* methods. The analogous -  638 # information for numeric axes would be information about log scales. -  639 self._var_ordered = {"x": False, "y": False} # alt., used DefaultDict - --> 640 self.assign_variables(data, variables) -  642 for var, cls in self._semantic_mappings.items(): -  643 -  644 # Create the mapping function -  645 map_func = partial(cls.map, plotter=self) - - File ~/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:701, in VectorPlotter.assign_variables(self, data, variables) -  699 else: -  700 self.input_format = "long" - --> 701 plot_data, variables = self._assign_variables_longform( -  702  data, **variables, -  703  ) -  705 self.plot_data = plot_data -  706 self.variables = variables - - File ~/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:953, in VectorPlotter._assign_variables_longform(self, data, **kwargs) -  947 val_cls = val.__class__.__name__ -  948 err = ( -  949 f"Length of {val_cls} vectors must match length of `data`" -  950 f" when both are used, but `data` has length {len(data)}" -  951 f" and the vector passed to `{key}` has length {len(val)}." -  952 ) - --> 953 raise ValueError(err) -  955 plot_data[key] = val -  957 # Try to infer the name of the variable - - ValueError: Length of list vectors must match length of `data` when both are used, but `data` has length 2360 and the vector passed to `hue` has length 1. -#+end_example -[[file:./.ob-jupyter/52641d066940b583869bad758efa66c2a4767b20.png]] -:END: -#+RESULTS: -:RESULTS: -: (2, 40) -[[file:./.ob-jupyter/0b5c08f40c7fb7b8aca0655bfb61767001ef2cb6.png]] -:END: - -#+begin_example - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead - if pd.api.types.is_categorical_dtype(vector): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. - with pd.option_context('mode.use_inf_as_na', True): - /home/leon/mambaforge/envs/dual_data/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead. - with pd.option_context('mode.use_inf_as_na', True): -#+end_example -:END: -#+RESULTS: -:RESULTS: -: (2, 40) -[[file:./.ob-jupyter/17acc2438d47e171d393196e216559b4ca776b6f.png]] -:END: +[[file:./.ob-jupyter/00fbd97ce74733dfd9a4b14338917ef31ae36615.png]] #+begin_src ipython sample_A = end_point[end_point.phi==0].phase * 180 / np.pi @@ -500,11 +320,11 @@ Low rank plt.hist(sample_A) errors_A = (sample_A > 90) & (sample_A<270) errors_B = (sample_B < 90) | (sample_B>270) - print((np.sum(errors_A) + np.sum(errors_B)) / 40) + print(1-(np.sum(errors_A) + np.sum(errors_B)) / 100) #+end_src #+RESULTS: :RESULTS: -: 0.3 -[[file:./.ob-jupyter/1c577b47741ca171e873f2ced7b94035b884c894.png]] +: 0.6799999999999999 +[[file:./.ob-jupyter/8791935a4e14273b4ab929f854d2c86cce76a29b.png]] :END: diff --git a/org/single.org b/org/single.org index a7180d7..6a9b295 100644 --- a/org/single.org +++ b/org/single.org @@ -109,12 +109,12 @@ #+end_src #+RESULTS: -| rm | -rf | ./obj/*.o | ./bin/LifNet | | | | | | | | | | -| g++ | -Wall | -std=c++17 | -pthread | -Ofast | -s | -lyaml-cpp | -c | src/globals.cpp | -o | obj/globals.o | | | -| g++ | -Wall | -std=c++17 | -pthread | -Ofast | -s | -lyaml-cpp | -c | src/lif_network.cpp | -o | obj/lif_network.o | | | -| g++ | -Wall | -std=c++17 | -pthread | -Ofast | -s | -lyaml-cpp | -c | src/main.cpp | -o | obj/main.o | | | -| g++ | -Wall | -std=c++17 | -pthread | -Ofast | -s | -lyaml-cpp | -c | src/sparse_mat.cpp | -o | obj/sparse_mat.o | | | -| g++ | -o | ./bin/LifNet | obj/globals.o | obj/lif_network.o | obj/main.o | obj/sparse_mat.o | -Wall | -std=c++17 | -pthread | -Ofast | -s | -lyaml-cpp | +| rm | -rf | ./obj/*.o | ./bin/LifNet | | | | | | | | | | | | | | | +| g++ | -Wall | -std=c++17 | -Ofast | -s | -march=native | -funroll-loops | -ftree-vectorize | -ffast-math | -fomit-frame-pointer | -fexpensive-optimizations | -lyaml-cpp | -c | src/globals.cpp | -o | obj/globals.o | | | +| g++ | -Wall | -std=c++17 | -Ofast | -s | -march=native | -funroll-loops | -ftree-vectorize | -ffast-math | -fomit-frame-pointer | -fexpensive-optimizations | -lyaml-cpp | -c | src/lif_network.cpp | -o | obj/lif_network.o | | | +| g++ | -Wall | -std=c++17 | -Ofast | -s | -march=native | -funroll-loops | -ftree-vectorize | -ffast-math | -fomit-frame-pointer | -fexpensive-optimizations | -lyaml-cpp | -c | src/main.cpp | -o | obj/main.o | | | +| g++ | -Wall | -std=c++17 | -Ofast | -s | -march=native | -funroll-loops | -ftree-vectorize | -ffast-math | -fomit-frame-pointer | -fexpensive-optimizations | -lyaml-cpp | -c | src/sparse_mat.cpp | -o | obj/sparse_mat.o | | | +| g++ | -o | ./bin/LifNet | obj/globals.o | obj/lif_network.o | obj/main.o | obj/sparse_mat.o | -Wall | -std=c++17 | -Ofast | -s | -march=native | -funroll-loops | -ftree-vectorize | -ffast-math | -fomit-frame-pointer | -fexpensive-optimizations | -lyaml-cpp | * Single trial ** Simulation @@ -132,7 +132,7 @@ #+begin_src ipython session = '/' - # session = '/LR_Go_ortho_phi_0_ini_3' + session = '/LR_Go_early_phi_0_ini_0' data = get_data(session) print('data', data.shape) rates = data[:32000] @@ -140,8 +140,8 @@ #+end_src #+RESULTS: -: data (40000, 36) -: rates 2.58225 9.250059 +: data (40000, 17) +: rates 1.4341692 5.6550074 #+begin_src ipython index_order = get_index() @@ -180,7 +180,7 @@ #+RESULTS: :RESULTS: : (28,) -[[file:./.ob-jupyter/7c2483f87fbe25f3457611ea54ac8bedfe55f46c.png]] +[[file:./.ob-jupyter/ae684e2e9c23ba7afe3cc1406197d361785a5c1a.png]] :END: **** Fourier vs Time @@ -217,9 +217,9 @@ #+RESULTS: :RESULTS: -: /tmp/ipykernel_3988353/3551809589.py:16: RuntimeWarning: invalid value encountered in divide +: /tmp/ipykernel_962795/3551809589.py:16: RuntimeWarning: invalid value encountered in divide : ax[1].plot(times, m1 / m0) -[[file:./.ob-jupyter/3fcc917f72f668facd29bb42953c276a2497bd07.png]] +[[file:./.ob-jupyter/fd338dc9e8fcc2d2831b43f41e9df081ee48965d.png]] :END: #+begin_src ipython @@ -246,11 +246,10 @@ #+end_src #+RESULTS: -[[file:./.ob-jupyter/8f069a5398b31057b9ccb6306cf2741a5ce63ec1.png]] +[[file:./.ob-jupyter/460520178b0dd946d25406256bc43e005add4b40.png]] **** Spatial profile - #+begin_src ipython smooth_E = circcvl(rates_E, windowSize=3000, axis=0) theta = np.linspace(0, 360, rates_E.shape[0]) @@ -264,7 +263,7 @@ #+end_src #+RESULTS: -[[file:./.ob-jupyter/3071aa03e77a11a12bba00b1333ac85ccd295079.png]] +[[file:./.ob-jupyter/513294c611b7436f612df12a8621a8bd50955160.png]] #+BEGIN_SRC ipython diff --git a/src/globals.cpp b/src/globals.cpp index a6303bc..4c3a6a8 100644 --- a/src/globals.cpp +++ b/src/globals.cpp @@ -68,7 +68,6 @@ int LR_LOAD; std::vector LR_MEAN; std::vector LR_STD; std::vector LR_RHO; -std::vector LR_FF_RHO; std::vector ksi_0(N); std::vector ksi_1(N); @@ -226,7 +225,6 @@ void loadConfig(std::string configname){ IF_FF_CORR = config["IF_FF_CORR"].as(); CORR_FF = config["CORR_FF"].as>(); A_CORR = config["A_CORR"].as>(); - LR_RANK = config["LR_RANK"].as(); LR_SEED = config["LR_SEED"].as(); @@ -235,10 +233,9 @@ void loadConfig(std::string configname){ LR_MEAN = config["LR_MEAN"].as>(); LR_STD = config["LR_STD"].as>(); LR_RHO = config["LR_RHO"].as>(); - LR_FF_RHO = config["LR_FF_RHO"].as>(); - - if(PHI_STIM[0] == 180.0) - LR_FF_RHO[0] *= -1; + + if(PHI_STIM[0] == 0.0) + LR_RHO[1] *= -1; } diff --git a/src/globals.hpp b/src/globals.hpp index 0ca6ba7..ac6736b 100644 --- a/src/globals.hpp +++ b/src/globals.hpp @@ -67,7 +67,6 @@ extern int LR_LOAD; extern std::vector LR_MEAN; extern std::vector LR_STD; extern std::vector LR_RHO; -extern std::vector LR_FF_RHO; extern std::vector ksi_0; extern std::vector ksi_1; diff --git a/src/lif_network.cpp b/src/lif_network.cpp index 95d3b44..7794c13 100644 --- a/src/lif_network.cpp +++ b/src/lif_network.cpp @@ -42,40 +42,32 @@ std::uniform_real_distribution unif_theta(0.0, twoPi); std::vector> spike_pair; - -void odr_stimuli(float* &ff_inputs, int FLAG) { - for (int i = 0; i < Na[0]; ++i) { +void odr_stimuli(float* &ff_inputs) { + for (int i = 0; i < Na[0]; i++) { ff_inputs[i] = Iext_scaled[which_pop[i]] + (A_STIM[which_pop[i]] + STD_STIM[which_pop[i]] * white(gen)) * sqrt(Ka[0]) * (1.0 + KAPPA_STIM[which_pop[i]] - * cos(theta[i] - PHI_STIM[which_pop[i]] * M_PI / 180.0)); + * cos(theta[i] - PHI_STIM[which_pop[i]] * M_PI / 180.0)); } } void dual_task_stimuli(float* &ff_inputs, int FLAG) { - if(FLAG ==0) - for (int i = 0; i < Na[0]; ++i) { - if(PHI_STIM[0]==0) - ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 - A_STIM[which_pop[i]] * ksi_0[i]); - if(PHI_STIM[0]==180) - ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 + A_STIM[which_pop[i]] * ksi_0[i]); - - if(PHI_STIM[0]==270) - ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 - A_STIM[which_pop[i]] * ksi_2[i]); - if(PHI_STIM[0]==90) - ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 + A_STIM[which_pop[i]] * ksi_2[i]); + if(FLAG ==0) { + for (int i = 0; i < Na[0]; i++) + ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 + A_STIM[0] * ksi_0[i]); } - else - for (int i = 0; i < Na[0]; ++i) { + else { + for (int i = 0; i < Na[0]; i++) { if(PHI_DIST[0]==270) ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 - A_DIST[which_pop[i]] * ksi_2[i]); if(PHI_DIST[0]==90) - ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 + A_DIST[which_pop[i]] * ksi_2[i]); + ff_inputs[i] = Iext_scaled[which_pop[i]] * (1.0 + A_DIST[which_pop[i]] * ksi_2[i]); } + } } void init_lif() { @@ -90,9 +82,9 @@ void init_lif() { Jab_scaled = new float[N_POP * N_POP](); Iext_scaled = new float[N_POP](); - + colptr = new size_t[N+1](); - indices = new int[(size_t) (N * 5.0 * K)](); + indices = new int[(size_t) (N * N)](); for (int i = 0; i < N_POP; i++) inputs[i] = new float[N](); @@ -109,7 +101,7 @@ void init_lif() { } theta = new float[N](); - for (int i = 0; i < N; ++i) + for (int i = 0; i < N; i++) theta[i] = (twoPi * (i - cNa[which_pop[i]])) / (float) Na[which_pop[i]]; ensureDirExists(DATA_PATH); @@ -129,7 +121,7 @@ void free_lif() { delete[] ff_inputs; - for (int i = 0; i < N_POP; ++i) { + for (int i = 0; i < N_POP; i++) { delete[] inputs[i]; } delete[] inputs; @@ -160,15 +152,15 @@ void initNetwork() { std::cout << "Initializing Network, output dir:" << DATA_PATH; // volts = generateGaussianVector(N, 2.0, 0.5); - for(int i=0; i < N_POP; ++i) - for(int j=0; j < N_POP; ++j) + for(int i=0; i < N_POP; i++) + for(int j=0; j < N_POP; j++) // Jab_scaled[j + i * N_POP] = GAIN * Jab[j + i * N_POP]; // Jab_scaled[j + i * N_POP] = GAIN * Jab[j + i * N_POP] / TAU_SYN[j] / Ka[j] * sqrt(Ka[0]); Jab_scaled[j + i * N_POP] = GAIN * Jab[j + i * N_POP] * (V_THRESH - V_REST) / TAU_SYN[j] / sqrt(Ka[j]); // Jab_scaled[j + i * N_POP] = GAIN * Jab[j + i * N_POP] * (V_THRESH - V_REST) / TAU_SYN[j] / sqrt(K); if(IF_NMDA) { - for(int i=0; i < N_POP; ++i) + for(int i=0; i < N_POP; i++) // Jab_NMDA[i] = GAIN * Jab[i * N_POP] ; // Jab_NMDA[i] = GAIN * Jab[i * N_POP] / TAU_NMDA[i] / Ka[0] * sqrt(Ka[0]); Jab_NMDA[i] = GAIN * Jab[i * N_POP] * (V_THRESH - V_REST) / TAU_NMDA[i] / sqrt(Ka[0]); @@ -177,28 +169,28 @@ void initNetwork() { Jab_NMDA[1] *= (1.0 - R_NMDA[1]) / R_NMDA[1]; } - for(int i=0; i < N_POP; ++i) + for(int i=0; i < N_POP; i++) // Iext_scaled[i] = GAIN * Iext[i] ; // // Iext_scaled[i] = GAIN * Iext[i] * sqrt(Ka[0]) ; Iext_scaled[i] = GAIN * Iext[i] * sqrt(Ka[0]) * (V_THRESH - V_REST); - for(int i=0; i= V_THRESH) { volts[i] = V_REST; spikes[i] = 1.0; @@ -346,7 +337,7 @@ void updateSpikes(int step){ spikes[i] = 0.0; } - for (int i = 0; i < N; ++i) + for (int i = 0; i < N; i++) rates[i] += spikes[i]; } @@ -381,39 +372,39 @@ void printParam(){ std::cout << "N_POP " << N_POP; std::cout << " N " << N << " Na "; - for(int i=0; i < N_POP; ++i) + for(int i=0; i < N_POP; i++) std::cout << Na[i] << " "; std::cout << " cNa "; - for(int i=0; i < N_POP+1; ++i) + for(int i=0; i < N_POP+1; i++) std::cout << cNa[i] << " "; std::cout << std::endl; std::cout << "K " << K << " Ka "; - for(int i=0; i < N_POP; ++i) + for(int i=0; i < N_POP; i++) std::cout << Ka[i] << " "; std::cout << std::endl; std::cout << "Jab "; - for(int i=0; i < N_POP; ++i) - for(int j=0; j < N_POP; ++j) + for(int i=0; i < N_POP; i++) + for(int j=0; j < N_POP; j++) std::cout << Jab[j + i * N_POP] << " "; // std::cout << std::endl; std::cout << "Iext "; - for(int i=0; i < N_POP; ++i) + for(int i=0; i < N_POP; i++) std::cout << Iext[i] << " "; std::cout << std::endl; std::cout << "Iext_scaled "; - for (int i = 0; i < N_POP; ++i) { + for (int i = 0; i < N_POP; i++) { std::cout << Iext_scaled[i] << " "; } // std::cout << std::endl; std::cout << "Jab_scaled "; - for(int i=0; i < N_POP; ++i) - for(int j=0; j < N_POP; ++j) + for(int i=0; i < N_POP; i++) + for(int j=0; j < N_POP; j++) std::cout << Jab_scaled[j + i * N_POP] << " "; std::cout << std::endl; } @@ -426,13 +417,18 @@ void runSimul(){ if (PROBA[0] == "lr") { if (LR_LOAD) - load_ksi(ksi_0, ksi_1, ksi_2, Na[0]); + load_ksi(ksi_0, ksi_1, ksi_2); else generate_ksi(ksi_0, ksi_1, ksi_2, Na[0]); float product = std::inner_product(ksi_1.begin(), ksi_1.end(), ksi_2.begin(), 0); - std::cout << std::endl; - std::cout << "inner " << product << std::endl; + std::cout << "ksi_1 . ksi_2 " << product << std::endl; + + product = std::inner_product(ksi_1.begin(), ksi_1.end(), ksi_0.begin(), 0); + std::cout << "ksi_0 . ksi_1 " << product << std::endl; + + product = std::inner_product(ksi_2.begin(), ksi_2.end(), ksi_0.begin(), 0); + std::cout << "ksi_0 . ksi_2 " << product << std::endl; } if(IF_LOAD_MAT) @@ -448,7 +444,7 @@ void runSimul(){ std::ofstream (DATA_PATH + "/inputsE.txt", std::ios::trunc).close(); std::ofstream (DATA_PATH + "/inputsI.txt", std::ios::trunc).close(); std::ofstream (DATA_PATH + "/volts.txt", std::ios::trunc).close(); - + std::ofstream (DATA_PATH + "/x_stp.txt", std::ios::trunc).close(); std::ofstream (DATA_PATH + "/u_stp.txt", std::ios::trunc).close(); std::ofstream (DATA_PATH + "/A_stp.txt", std::ios::trunc).close(); @@ -470,7 +466,7 @@ void runSimul(){ // int N_SAVE = (int) (DURATION - T_SAVE) / DT; std::cout << "Running Simulation" << std::endl; - for(int step = 0; step < N_STEPS; ++step) { + for(int step = 0; step < N_STEPS; step++) { updateVolts(); updateSpikes(step); // must come before updateRecInputs in this implementation @@ -479,11 +475,11 @@ void runSimul(){ updateNetInputs(); if(step==N_STEADY) - for(int i=0; i N_STEADY) { - for(int i=0; i generateTrivariateGaussian(std::vectormeans, std::vector stds, std::vector rhos) { - + // Generate three independent standard normal variables float Z1 = white(rng_lr); float Z2 = white(rng_lr); float Z3 = white(rng_lr); - + // Extract correlation coefficients for readability - float rho_xy = rhos[0]; - float rho_xz = rhos[1]; - float rho_yz = rhos[2]; - + float rho_21 = rhos[0]; + float rho_31 = rhos[1]; + float rho_32 = rhos[2]; + // float deter = (1.0 - rho_yz*rho_yz) - rho_xy * (rho_xy - rho_xz*rho_yz) + rho_xz * (rho_xy*rho_yz - rho_xz); // std::cout << deter << std::endl; // Cholesky decomposition of the covariance matrix - float L11 = stds[0]; - float L21 = rho_xy * stds[1]; - float L22 = stds[1] * std::sqrt(1.0f - rho_xy * rho_xy); - float L31 = rho_xz * stds[2]; - float L32 = (rho_yz - rho_xy * rho_xz) / std::sqrt(1.0f - rho_xy * rho_xy) * stds[2]; - float L33 = std::sqrt(1.0f - rho_xz * rho_xz - (L32 / stds[2]) * (L32 / stds[2])) * stds[2]; - + float L11 = 1.0; + float L21 = rho_21; + float L22 = sqrt(1.0 - L21 * L21); + // float L31 = rho_xz / L11; + float L31 = rho_31; + // float L32 = (rho_32 - L31 * L21) / L22 ; + float L32 = rho_32; + float L33 = sqrt(1.0 - L31 * L31 - L32 * L32); + // Generating correlated variables float X1 = L11 * Z1 + means[0]; float X2 = L21 * Z1 + L22 * Z2 + means[1]; float X3 = L31 * Z1 + L32 * Z2 + L33 * Z3 + means[2]; - - return {X1, X2, X3}; + + return {X3, X1, X2}; } std::array generateBivariateGaussian(std::vector means, std::vector stds, std::vector correlation) { @@ -64,7 +66,7 @@ std::array generateBivariateGaussian(std::vector means, std::ve return {X1, X2, X2}; } -std::array generateGaussian(std::vector means, std::vector stds, std::vector correlation) { +std::array generateGaussian(std::vector means, std::vector stds) { float Z1 = white(rng_lr); float X1 = stds[0] * Z1 + means[0]; @@ -76,8 +78,8 @@ std::vector> outerProduct(std::vector& vec) { size_t length = vec.size(); std::vector> result(length, std::vector(length)); - for (size_t i = 0; i < length; ++i) { - for (size_t j = 0; j < length; ++j) { + for (size_t i = 0; i < length; i++) { + for (size_t j = 0; j < length; j++) { result[i][j] = vec[i] * vec[j]; } } @@ -86,46 +88,44 @@ std::vector> outerProduct(std::vector& vec) { } void generate_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2, int Nb) { + + std::cout << "Generating LR Vec "; + + if(LR_SEED!=0){ + std::cout << " LR_SEED " << LR_SEED ; + rng_lr.seed(LR_SEED); + } std::array samples; if(LR_RANK == 1) - for (int i = 0; i < Nb; ++i) { - samples = generateGaussian(LR_MEAN, LR_STD, LR_RHO); + for (int i = 0; i < Nb; i++) { + samples = generateGaussian(LR_MEAN, LR_STD); sample_0.push_back(samples[0]); sample_1.push_back(samples[0]); } if(LR_RANK == 2) - for (int i = 0; i < Nb; ++i) { + for (int i = 0; i < Nb; i++) { samples = generateBivariateGaussian(LR_MEAN, LR_STD, LR_RHO); sample_1.push_back(samples[0]); // ksi_1 sample_2.push_back(samples[1]); // ksi_2 - - // // Sample Stimuli - sample_0.push_back(LR_FF_RHO[0] * samples[0] - + LR_FF_RHO[1] * samples[1] - + sqrt(1.0 - - LR_FF_RHO[0] * LR_FF_RHO[0] - - LR_FF_RHO[1] * LR_FF_RHO[1]) - * white(rng_lr)); } - if(LR_RANK==3) - for (int i = 0; i < Nb; ++i) { + if(LR_RANK==3) { + for (int i = 0; i < Nb; i++) { samples = generateTrivariateGaussian(LR_MEAN, LR_STD, LR_RHO); - sample_0.push_back(samples[0]); - sample_1.push_back(samples[1]); - sample_2.push_back(samples[2]); + sample_0.push_back(samples[0]); // Input + sample_1.push_back(samples[1]); // ksi_1 + sample_2.push_back(samples[2]); // ksi_2 } - - std::cout << std::endl; + } std::cout <<" ksi "; - for (int i=0; i<5; ++i) + for (int i=0; i<5; i++) std::cout << sample_1[i] << " "; std::cout << std::endl; - std::cout << "Saving LR vec to:" << MAT_PATH; + std::cout << "Saving LR Vec to:" << MAT_PATH; std::ofstream ksi0File(MAT_PATH + "/ksi_0.txt"); saveVectorToFile(ksi0File, sample_0); @@ -138,10 +138,12 @@ void generate_ksi(std::vector &sample_0, std::vector &sample_1, st std::ofstream ksi2File(MAT_PATH + "/ksi_2.txt"); saveVectorToFile(ksi2File, sample_2); ksi2File.close(); + + std::cout << " Done" << std::endl; } -std::vector> gen_mat_LR(std::vector sample_1, std::vector sample_2) { +std::vector> gen_mat_LR(std::vector sample_1, std::vector sample_2) { std::vector> outer = outerProduct(sample_1); return outer; } @@ -150,7 +152,7 @@ float genConProb(int i, int j) { int pres_pop = which_pop[j]; int post_pop = which_pop[i]; - + float theta_i = (2.0 * M_PI * (i - cNa[post_pop])) / (float) Na[post_pop]; float theta_j = (2.0 * M_PI * (j - cNa[pres_pop])) / (float) Na[pres_pop]; @@ -164,7 +166,7 @@ float genConProb(int i, int j) { else if (PROBA[pres_pop + N_POP * post_pop] == "lr") { proba *= (1.0 + KAPPA[pres_pop + N_POP * post_pop] - * (ksi_1[i - cNa[post_pop]] * ksi_1[j - cNa[pres_pop]] + * (ksi_1[i - cNa[post_pop]] * ksi_1[j - cNa[pres_pop]] + ksi_2[i - cNa[post_pop]] * ksi_2[j - cNa[pres_pop]]) / sqrt(Ka[pres_pop])); } @@ -180,11 +182,6 @@ float genConProb(int i, int j) { void genSparseMatCSC(size_t*& colptr, int*& indices) { std::cout << "Generating Sparse Matrix" ; - - if(LR_SEED!=0){ - std::cout << " LR_SEED " << LR_SEED << std::endl; - rng_lr.seed(LR_SEED); - } // arma::mat ksi_mat; // std::vector ksi_1; @@ -207,7 +204,7 @@ void genSparseMatCSC(size_t*& colptr, int*& indices) { colptr[0] = 0; for (int j = 0; j < N; j++) { // presynaptic for (int i = 0; i < N; i++) { // postsynaptic - + // std::cout << i << " " << j << std::endl; if (unif(rng) < genConProb(i, j)) { indices[nnz] = i; nnz++; @@ -222,26 +219,26 @@ void genSparseMatCSC(size_t*& colptr, int*& indices) { std::cout << " Done" << std::endl; } -void load_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2, int Nb) { +void load_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2) { std::cout << "Loading LR vec from:" << MAT_PATH; - + std::ifstream ksi0File(MAT_PATH + "/ksi_0.txt"); - loadVectorFromFile(ksi0File, sample_0, Nb); + loadVectorFromFile(ksi0File, sample_0); ksi0File.close(); std::ifstream ksi1File(MAT_PATH + "/ksi_1.txt"); - loadVectorFromFile(ksi1File, sample_1, Nb); + loadVectorFromFile(ksi1File, sample_1); ksi1File.close(); std::ifstream ksi2File(MAT_PATH + "/ksi_2.txt"); - loadVectorFromFile(ksi2File, sample_2, Nb); + loadVectorFromFile(ksi2File, sample_2); ksi2File.close(); std::cout << std::endl; std::cout <<" ksi "; - for(int i=0; i<5; ++i) + for(int i=0; i<5; i++) std::cout << sample_1[i] << " "; - std::cout << std::endl; + std::cout << " Done" << std::endl; } void getSparseMatCSC(size_t *&colptr, int *&indices) { @@ -263,7 +260,7 @@ void saveSparseMatCSC(size_t* colptr, int* indices){ std::cout << "Saving Sparse Matrix to: " << MAT_PATH ; ensureDirExists(MAT_PATH); - + std::ofstream colptrFile(MAT_PATH + "/colptr.txt"); saveArrayToFile(colptrFile, colptr, (size_t) N+1); colptrFile.close(); diff --git a/src/sparse_mat.hpp b/src/sparse_mat.hpp index ab0efc0..e97f9f9 100644 --- a/src/sparse_mat.hpp +++ b/src/sparse_mat.hpp @@ -12,12 +12,12 @@ void saveSparseMatCSC(size_t* colptr, int* indices); void cscToDense(size_t* colptr, int* indices, int** dense); void generate_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2, int Nb); -void load_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2, int Nb); +void load_ksi(std::vector &sample_0, std::vector &sample_1, std::vector &sample_2); std::vector> gen_mat_LR(std::vector sample_1, std::vector sample_2); std::vector> outerProduct(std::vector& vec); -std::array generateGaussian(std::vector means, std::vector stds, std::vector correlation); +std::array generateGaussian(std::vector means, std::vector stds); std::array generateBivariateGaussian(std::vector means, std::vector stds, std::vector correlation); std::array generate_trivariate_gaussian(std::vector means, std::vector stds, std::vector rhos); diff --git a/src/utils.hpp b/src/utils.hpp index 6a72cea..ccacb92 100644 --- a/src/utils.hpp +++ b/src/utils.hpp @@ -85,7 +85,7 @@ void saveVectorToFile(std::ofstream& outFile, std::vector& vec) { } template -void loadVectorFromFile(std::ifstream& inFile, std::vector& vec, size_t len) { +void loadVectorFromFile(std::ifstream& inFile, std::vector& vec) { inFile.seekg(0, std::ios::end); size_t fileSize = inFile.tellg();