def __init__(self,
input,
target,
ndelays=None,
bias=0.0,
group_index=None,
lags=None):
ConvolutionalInCrowdModel.__init__(self, input, target, ndelays, bias,
group_index, lags)
def test1():
#construct sample input matrix
num_channels = 60
num_timepoints = 5000
stim = np.random.randn(num_channels, num_timepoints)
#normalize stimulus so it's between -1 and 1
stim /= np.abs(stim).max()
#transpose matrix, incrowd expects matrix to be # of time points X # of channels
stim = stim.transpose()
#construct sample filter
time_lags = np.arange(0, 8, 1, dtype='int')
real_filter = np.random.randn(num_channels, len(time_lags))
#make filter sparse
real_filter[np.abs(real_filter) < 0.90] = 0.0
#normalize filter
real_filter /= np.abs(real_filter).max()
num_nonzero = (np.abs(real_filter) == 0.0).sum()
print '# of nonzero elements in filter: %d out of %d' % (
num_nonzero, len(time_lags) * num_channels)
#construct sample output using convolution
output = fast_conv(stim, real_filter, time_lags)
#add random noise to output
output += np.random.randn(len(output)) * 1e-6
#create a convolutional incrowd model
cic_model = ConvolutionalInCrowdModel(stim,
output,
lags=time_lags,
bias=0.0)
#create incrowd optimizer, using Lasso+Elastic net for the interior solver,
#lambda1 is the constant for the Lasso regularization
#lambda2 is the constant for the Elastic Net regularization
#threshold is the fraction of parameters that are introduced into the active set at each iteration
ico = InCrowd(cic_model,
solver_params={
'lambda1': 1.0,
'lambda2': 1.0
},
max_additions_fraction=0.25)
#run the optimization
num_iters = 15
for k in range(num_iters):
if ico.converged:
break
ico.iterate()
print 'Iteration %d, err=%0.9f' % (k + 1, (cic_model.residual(ico.x)**
2).sum())
#get the predicted filter, make cic_model reshape the parameters into what we would expect to see
predicted_filter = cic_model.get_filter(ico.x)
#predicted_output = fast_conv(stim, predicted_filter, time_lags)
predicted_output = cic_model.forward(ico.x)
filter_diff = real_filter - predicted_filter
plt.figure()
ax1 = plt.subplot2grid((2, 3), (0, 0))
plt.imshow(real_filter, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Actual Filter')
ax2 = plt.subplot2grid((2, 3), (0, 1))
plt.imshow(predicted_filter, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Predicted Filter')
ax3 = plt.subplot2grid((2, 3), (0, 2))
plt.imshow(filter_diff, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Differences')
ax4 = plt.subplot2grid((2, 3), (1, 0), colspan=3)
plt.plot(output, 'k-', linewidth=2.0)
plt.plot(predicted_output, 'r-', linewidth=2.0)
def test1():
#construct sample input matrix
num_channels = 60
num_timepoints = 5000
stim = np.random.randn(num_channels, num_timepoints)
#normalize stimulus so it's between -1 and 1
stim /= np.abs(stim).max()
#transpose matrix, incrowd expects matrix to be # of time points X # of channels
stim = stim.transpose()
#construct sample filter
time_lags = np.arange(0, 8, 1, dtype='int')
real_filter = np.random.randn(num_channels, len(time_lags))
#make filter sparse
real_filter[np.abs(real_filter) < 0.90] = 0.0
#normalize filter
real_filter /= np.abs(real_filter).max()
num_nonzero = (np.abs(real_filter) == 0.0).sum()
print '# of nonzero elements in filter: %d out of %d' % (num_nonzero, len(time_lags)*num_channels)
#construct sample output using convolution
output = fast_conv(stim, real_filter, time_lags)
#add random noise to output
output += np.random.randn(len(output))*1e-6
#create a convolutional incrowd model
cic_model = ConvolutionalInCrowdModel(stim, output, lags=time_lags, bias=0.0)
#create incrowd optimizer, using Lasso+Elastic net for the interior solver,
#lambda1 is the constant for the Lasso regularization
#lambda2 is the constant for the Elastic Net regularization
#threshold is the fraction of parameters that are introduced into the active set at each iteration
ico = InCrowd(cic_model, solver_params={'lambda1':1.0, 'lambda2':1.0}, max_additions_fraction=0.25)
#run the optimization
num_iters = 15
for k in range(num_iters):
if ico.converged:
break
ico.iterate()
print 'Iteration %d, err=%0.9f' % (k+1, (cic_model.residual(ico.x)**2).sum())
#get the predicted filter, make cic_model reshape the parameters into what we would expect to see
predicted_filter = cic_model.get_filter(ico.x)
#predicted_output = fast_conv(stim, predicted_filter, time_lags)
predicted_output = cic_model.forward(ico.x)
filter_diff = real_filter - predicted_filter
plt.figure()
ax1 = plt.subplot2grid((2, 3), (0, 0))
plt.imshow(real_filter, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Actual Filter')
ax2 = plt.subplot2grid((2, 3), (0, 1))
plt.imshow(predicted_filter, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Predicted Filter')
ax3 = plt.subplot2grid((2, 3), (0, 2))
plt.imshow(filter_diff, aspect='auto', interpolation='nearest')
plt.colorbar()
plt.title('Differences')
ax4 = plt.subplot2grid((2, 3), (1, 0), colspan=3)
plt.plot(output, 'k-', linewidth=2.0)
plt.plot(predicted_output, 'r-', linewidth=2.0)
def __init__(self, input, target, ndelays=None, bias=0.0, group_index=None, lags=None):
ConvolutionalInCrowdModel.__init__(self, input, target, ndelays, bias, group_index, lags)