def run_example():
X, Y, K_true = create_toy_data(N=2500)
nt, nf = K_true.shape
D = create_derivative_matrix(nt, nf, order='C')
prior = SmoothnessPrior(D=D)
model = CbRF(optimize=True, metric='AUC', prior=prior,
verbose=True, n_griditer=3, n_jobs=1)
model.fit(X, Y)
k = model.get_weights()
K = np.reshape(k, K_true.shape)
fig, axarr = plt.subplots(nrows=1, ncols=2)
ax = axarr[0]
ax.set_title('True')
vmax = np.max(np.abs(K_true))
ax.imshow(K_true, interpolation='nearest', vmin=-vmax, vmax=vmax)
ax = axarr[1]
ax.set_title('CbRF')
vmax = np.max(np.abs(K))
ax.imshow(K, interpolation='nearest', vmin=-vmax, vmax=vmax)
plt.show()
def run_example():
X, Y, K_true = create_toy_data(N=2500)
nt, nf = K_true.shape
D = create_derivative_matrix(nt, nf, order='C')
prior = SmoothnessPrior(D=D)
model = CbRF(optimize=True,
metric='AUC',
prior=prior,
verbose=True,
n_griditer=3,
n_jobs=1)
model.fit(X, Y)
k = model.get_weights()
K = np.reshape(k, K_true.shape)
fig, axarr = plt.subplots(nrows=1, ncols=2)
ax = axarr[0]
ax.set_title('True')
vmax = np.max(np.abs(K_true))
ax.imshow(K_true, interpolation='nearest', vmin=-vmax, vmax=vmax)
ax = axarr[1]
ax.set_title('CbRF')
vmax = np.max(np.abs(K))
ax.imshow(K, interpolation='nearest', vmin=-vmax, vmax=vmax)
plt.show()
def run_example():
data_dir = join(split(__file__)[0], 'data')
data_file = 'STRFs_ChirpsBlocks_IC_2012-01-31_50dB_chan01_unit01.h5'
block = load_data(data_dir, data_file)
X, Y, rfsize, axes, fs = convert_data(block)
# Add some models
n_jobs = 3 # number of processes used for hyperparameter optimization
n_griditer = 3
models = []
model = STA()
models.append(model)
model = GaussianGLM(n_griditer=n_griditer,
n_jobs=n_jobs,
optimizer='ridge')
models.append(model)
model = CbRF(n_griditer=n_griditer, n_jobs=n_jobs)
models.append(model)
model = PoissonGLM(n_griditer=n_griditer, n_jobs=n_jobs)
models.append(model)
# Fit models
for model in models:
print 50 * '-'
print "Fitting model:", model.name
print 50 * '-'
model.fit(X, Y)
# Plot STRFs
fig, axarr = plt.subplots(nrows=1,
ncols=len(models),
sharex=True,
sharey=True)
for i, model in enumerate(models):
ax = axarr[i]
ax.set_title(model.name)
W = np.reshape(model.coef_, rfsize)
rf = STRF(W,
fs,
time=axes[0].values.base,
frequency=axes[1].values.base)
rf.show(ax=ax, show_now=False, colorbar=False)
if i > 0:
ax.set_ylabel('')
fig.set_size_inches(8, 2.5)
fig.tight_layout()
plt.show()
def run_example():
X, Y, K_true = create_toy_data(N=25000, nonlin_order=3)
# Estimate STA
model = STA()
print "Estimating STA"
model.fit(X, Y)
k_sta = model.get_weights()
K_sta = np.reshape(k_sta, K_true.shape)
# Fit CbRF parameters
prior = GaussianPrior()
model = CbRF(optimize=True,
metric='AUC',
prior=prior,
verbose=True,
n_griditer=3,
n_jobs=-1,
param_grid={'alpha': 2**np.linspace(-20, 0, 7)})
print "This may take a couple of minutes. Time to grab a tea or coffee ..."
model.fit(X, Y)
k_cbrf = model.get_weights()
K_cbrf = np.reshape(k_cbrf, K_true.shape)
fig, axarr = plt.subplots(nrows=1, ncols=3)
ax = axarr[0]
ax.set_title('True')
vmax = np.max(np.abs(K_true))
ax.imshow(K_true, interpolation='nearest', vmin=-vmax, vmax=vmax)
ax = axarr[1]
ax.set_title('STA')
vmax = np.max(np.abs(K_sta))
ax.imshow(K_sta, interpolation='nearest', vmin=-vmax, vmax=vmax)
ax = axarr[2]
ax.set_title('CbRF')
vmax = np.max(np.abs(K_cbrf))
ax.imshow(K_cbrf, interpolation='nearest', vmin=-vmax, vmax=vmax)
plt.show()
def run_example():
t_spikefilt = 0.05
data_dir = join(split(__file__)[0], 'data')
data_file = 'STRFs_ChirpsBlocks_IC_2012-01-31_50dB_chan01_unit01.h5'
# data_file = 'STRFs_ChirpsBlocks_IC_2012-04-26_Pos_02_30dB_chan01_unit01.h5'
block = load_data(data_dir, data_file)
X, Y, rfsize, axes, fs, n_spikefilt = convert_data(block, t_spikefilt)
# Add some models
n_jobs = 3 # number of workers used for hyperparameter optimization
n_griditer = 3
models = []
# model = STA(n_postfilt=n_postfilt)
# models.append(model)
# model = GaussianGLM(n_griditer=n_griditer, n_jobs=n_jobs,
# optimizer='ridge')
# models.append(model)
model = CbRF(n_griditer=n_griditer, n_jobs=n_jobs, n_spikefilt=n_spikefilt)
models.append(model)
#
# model = PoissonGLM(n_griditer=n_griditer, n_jobs=n_jobs)
# models.append(model)
# Fit models
for model in models:
print 50 * '-'
print "Fitting model:", model.name
print 50 * '-'
model.fit(X, Y)
# Plot STRFs
fig, axarr = plt.subplots(nrows=len(models), ncols=1+int(n_spikefilt > 0))
axarr = np.atleast_2d(axarr)
for i, model in enumerate(models):
ax = axarr[i, 0]
ax.set_title(model.name)
W = np.reshape(model.get_weights(), rfsize)
rf = STRF(W, fs, time=axes[0].values.base,
frequency=axes[1].values.base)
rf.show(ax=ax, show_now=False, colorbar=False)
if n_spikefilt > 0:
# Post-spike filter
ax = axarr[i, 1]
xx = np.linspace(-t_spikefilt, 0, n_spikefilt) * 1000
h = model.get_spikefilt()
ax.plot(xx, h)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Gain (a.u.)')
fig.set_size_inches(8, 4)
fig.tight_layout()
plt.show()