def main(ntrain=800, ntest=200, nsplits=1, seed=1234567):
# Set the configuration parameters for the SP
ninputs = 784
kargs = {
'ninputs': ninputs,
'ncolumns': ninputs,
'nactive': 10,
'global_inhibition': True,
'trim': False,
'seed': seed,
'disable_boost': True,
'nsynapses': 392,
'seg_th': 10,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.002,
'pwindow': 0.01,
'random_permanence': True,
'nepochs': 10,
'clf': LinearSVC(random_state=seed),
'log_dir': os.path.join('simple_mnist', '1-1')
}
# Seed numpy
np.random.seed(seed)
# Get the data
(tr_x, tr_y), (te_x, te_y) = load_mnist()
x, y = np.vstack((tr_x, te_x)), np.hstack((tr_y, te_y))
# Split the data for CV
cv = MNISTCV(tr_y, te_y, ntrain, ntest, nsplits, seed)
# Execute the SP on each fold. Additionally, get results for each fitting
# method.
for i, (tr, te) in enumerate(cv):
# Create the region
sp = SPRegion(**kargs)
# Train the region
sp.fit(x[tr], y[tr])
# Test the base classifier
clf = LinearSVC(random_state=seed)
clf.fit(x[tr], y[tr])
# Get a random set of unique inputs from the training set
inputs = np.zeros((10, ninputs))
for i in xrange(10):
ix = np.random.permutation(np.where(y[tr] == i)[0])[0]
inputs[i] = x[tr][ix]
# Get the SP's predictions for the inputs
sp_pred = sp.predict(inputs)
# Get the reconstruction in the context of the SP
sp_inputs = sp.reconstruct_input(sp_pred)
# Make a plot comparing the images
shape = (28, 28)
path = os.path.join(sp.log_dir, 'input_reconstruction.png')
plot_compare_images((inputs, sp_pred, sp_inputs), shape, out_path=path)
def main(ntrain=800, ntest=200, nsplits=1, seed=1234567):
# Set the configuration parameters for the SP
ninputs = 784
kargs = {
'ninputs': ninputs,
'ncolumns': ninputs,
'nactive': 10,
'global_inhibition': True,
'trim': False,
'seed': seed,
'disable_boost': True,
'nsynapses': 392,
'seg_th': 10,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.002,
'pwindow': 0.01,
'random_permanence': True,
'nepochs': 10,
'clf': LinearSVC(random_state=seed),
'log_dir': os.path.join('simple_mnist', '1-1')
}
# Seed numpy
np.random.seed(seed)
# Get the data
(tr_x, tr_y), (te_x, te_y) = load_mnist()
x, y = np.vstack((tr_x, te_x)), np.hstack((tr_y, te_y))
# Split the data for CV
cv = MNISTCV(tr_y, te_y, ntrain, ntest, nsplits, seed)
# Execute the SP on each fold. Additionally, get results for each fitting
# method.
for i, (tr, te) in enumerate(cv):
# Create the region
sp = SPRegion(**kargs)
# Train the region
sp.fit(x[tr], y[tr])
# Test the base classifier
clf = LinearSVC(random_state=seed)
clf.fit(x[tr], y[tr])
# Get a random set of unique inputs from the training set
inputs = np.zeros((10, ninputs))
for i in xrange(10):
ix = np.random.permutation(np.where(y[tr] == i)[0])[0]
inputs[i] = x[tr][ix]
# Get the SP's predictions for the inputs
sp_pred = sp.predict(inputs)
# Get the reconstruction in the context of the SP
sp_inputs = sp.reconstruct_input(sp_pred)
# Make a plot comparing the images
shape = (28, 28)
path = os.path.join(sp.log_dir, 'input_reconstruction.png')
plot_compare_images((inputs, sp_pred, sp_inputs), shape, out_path=path)
def main(ntrain=800, ntest=200, nsplits=1, seed=123456789):
"""Run a simple MNIST classification task."""
# Set the configuration parameters for the SP
ninputs = 784
kargs = {
'ninputs': ninputs,
'ncolumns': ninputs,
'nactive': 30,
'global_inhibition': True,
'trim': False,
'seed': seed,
'disable_boost': True,
'nsynapses': 392,
'seg_th': 10,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.002,
'pwindow': 0.01,
'random_permanence': True,
'nepochs': 10,
'clf': LinearSVC(random_state=seed),
'log_dir': os.path.join('simple_mnist', '1-1')
}
# Seed numpy
np.random.seed(seed)
# Get the data
(tr_x, tr_y), (te_x, te_y) = load_mnist()
x, y = np.vstack((tr_x, te_x)), np.hstack((tr_y, te_y))
# Split the data for CV
cv = MNISTCV(tr_y, te_y, ntrain, ntest, nsplits, seed)
# Execute the SP on each fold. Additionally, get results for each fitting
# method.
for i, (tr, te) in enumerate(cv):
# Create the region
sp = SPRegion(**kargs)
# Train the region
sp.fit(x[tr], y[tr])
# Test the base classifier
clf = LinearSVC(random_state=seed)
clf.fit(x[tr], y[tr])
score = clf.score(x[te], y[te])
print('SVM Only Accuracy: {0:.2f}%'.format(score * 100))
# Test the region for the column method
score = sp.score(x[te], y[te])
print('Column Accuracy: {0:.2f}%'.format(score * 100))
# Test the region for the probabilistic method
score = sp.score(x[te], y[te], tr_x=x[tr], score_method='prob')
print('Probabilistic Accuracy: {0:.2f}%'.format(score * 100))
# Test the region for the dimensionality reduction method
score = sp.score(x[te], y[te], tr_x=x[tr], score_method='reduction')
ndims = len(sp.reduce_dimensions(x[0]))
print('Input Reduced from {0} to {1}: {2:.1f}X reduction'.format(
ninputs, ndims, ninputs / float(ndims)))
print('Reduction Accuracy: {0:.2f}%'.format(score * 100))
# Get a random set of unique inputs from the training set
inputs = np.zeros((10, ninputs))
for i in range(10):
ix = np.random.permutation(np.where(y[tr] == i)[0])[0]
inputs[i] = x[tr][ix]
# Get the SP's predictions for the inputs
sp_pred = sp.predict(inputs)
# Get the reconstruction in the context of the SP
sp_inputs = sp.reconstruct_input(sp_pred)
# Make a plot comparing the images
title = 'Input Reconstruction: Original (top), SP SDRs (middle), ' \
'SP Reconstruction (bottom)'
shape = (28, 28)
path = os.path.join(sp.log_dir, 'input_reconstruction.png')
plot_compare_images((inputs, sp_pred, sp_inputs),
shape,
title,
out_path=path)
def main(ntrain=800, ntest=200, nsplits=1, seed=123456789):
# Set the configuration parameters for the SP
ninputs = 784
kargs = {
'ninputs': ninputs,
'ncolumns': ninputs,
'nactive': 20,
'global_inhibition': True,
'trim': False,
'seed': seed,
'max_boost': 3,
'duty_cycle': 8,
'nsynapses': 392,
'seg_th': 2,
'syn_th': 0.5,
'pinc': 0.01,
'pdec': 0.02,
'pwindow': 0.5,
'random_permanence': True,
'nepochs': 1,
'clf': LinearSVC(random_state=seed),
'log_dir': os.path.join('simple_mnist', '1-1')
}
# Get the data
(tr_x, tr_y), (te_x, te_y) = load_mnist()
x, y = np.vstack((tr_x, te_x)), np.hstack((tr_y, te_y))
# Split the data for CV
cv = MNISTCV(tr_y, te_y, ntrain, ntest, nsplits, seed)
# Execute the SP on each fold. Additionally, get results for each fitting
# method.
for i, (tr, te) in enumerate(cv):
# Create the region
sp = SPRegion(**kargs)
# Train the region
sp.fit(x[tr], y[tr])
# Test the base classifier
clf = LinearSVC(random_state=seed)
clf.fit(x[tr], y[tr])
score = clf.score(x[te], y[te])
print 'SVM Only Accuracy: {0:.2f}%'.format(score * 100)
# Test the region for the column method
score = sp.score(x[te], y[te])
print 'Column Accuracy: {0:.2f}%'.format(score * 100)
# Test the region for the probabilistic method
score = sp.score(x[te], y[te], tr_x=x[tr], score_method='prob')
print 'Probabilistic Accuracy: {0:.2f}%'.format(score * 100)
# Test the region for the dimensionality reduction method
score = sp.score(x[te], y[te], tr_x=x[tr], score_method='reduction')
ndims = len(sp.reduce_dimensions(x[0]))
print 'Input Reduced from {0} to {1}: {2:.1f}X reduction'.format(
ninputs, ndims, ninputs / float(ndims))
print 'Reduction Accuracy: {0:.2f}%'.format(score * 100)
# Get a random set of unique inputs from the training set
inputs = np.zeros((10, ninputs))
for i in xrange(10):
ix = np.random.permutation(np.where(y[tr] == i)[0])[0]
inputs[i] = x[tr][ix]
# Get the SP's predictions for the inputs
sp_pred = sp.predict(inputs)
# Get the reconstruction in the context of the SP
sp_inputs = sp.reconstruct_input(sp_pred)
# Make a plot comparing the two
x1_labels = [str(i) for i in xrange(10)]
x2_labels = [str(i) for i in xrange(10)]
title = 'Input Reconstruction: Original (top), SP (bottom)'
shape = (28, 28)
path = os.path.join(sp.log_dir, 'input_reconstruction.png')
plot_compare_images((inputs, sp_inputs), shape, title, (x1_labels,
x2_labels,), path)