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Merge pull request tensorflow#2113 from jazcollins/master
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remove unused function in plot_lfads
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David Sussillo authored Aug 3, 2017
2 parents f4b94e7 + 597fb3e commit 5afeec2
Showing 1 changed file with 0 additions and 42 deletions.
42 changes: 0 additions & 42 deletions lfads/plot_lfads.py
Original file line number Diff line number Diff line change
Expand Up @@ -51,48 +51,6 @@ def all_plot(d, full_name="", exclude="", nspaces=0):
_plot_item(v, name=k, full_name=full_name+"/"+k, nspaces=nspaces+4)


def plot_priors():
g0s_prior_mean_bxn = train_modelvals['prior_g0_mean']
g0s_prior_var_bxn = train_modelvals['prior_g0_var']
g0s_post_mean_bxn = train_modelvals['posterior_g0_mean']
g0s_post_var_bxn = train_modelvals['posterior_g0_var']

plt.figure(figsize=(10,4), tight_layout=True);
plt.subplot(1,2,1)
plt.hist(g0s_post_mean_bxn.flatten(), bins=20, color='b');
plt.hist(g0s_prior_mean_bxn.flatten(), bins=20, color='g');

plt.title('Histogram of Prior/Posterior Mean Values')
plt.subplot(1,2,2)
plt.hist((g0s_post_var_bxn.flatten()), bins=20, color='b');
plt.hist((g0s_prior_var_bxn.flatten()), bins=20, color='g');
plt.title('Histogram of Prior/Posterior Log Variance Values')

plt.figure(figsize=(10,10), tight_layout=True)
plt.subplot(2,2,1)
plt.imshow(g0s_prior_mean_bxn.T, interpolation='nearest', cmap='jet')
plt.colorbar(fraction=0.025, pad=0.04)
plt.title('Prior g0 means')

plt.subplot(2,2,2)
plt.imshow(g0s_post_mean_bxn.T, interpolation='nearest', cmap='jet')
plt.colorbar(fraction=0.025, pad=0.04)
plt.title('Posterior g0 means');

plt.subplot(2,2,3)
plt.imshow(g0s_prior_var_bxn.T, interpolation='nearest', cmap='jet')
plt.colorbar(fraction=0.025, pad=0.04)
plt.title('Prior g0 variance Values')

plt.subplot(2,2,4)
plt.imshow(g0s_post_var_bxn.T, interpolation='nearest', cmap='jet')
plt.colorbar(fraction=0.025, pad=0.04)
plt.title('Posterior g0 variance Values')

plt.figure(figsize=(10,5))
plt.stem(np.sort(np.log(g0s_post_mean_bxn.std(axis=0))));
plt.title('Log standard deviation of h0 means');


def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0,
color='r', title=None):
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