Calculation of explainable variance etc.

ve.py

# Load data
responses_val_raw = np.load(os.path.join(path, 'raw_validation_set.npy'))
prediction = mymodel.prediction()
reps, num_imgs, num_neurons = responses_val_raw.shape

# Calculate normalized noise power
obs_var_raw = (responses_val_raw.var(axis=0, ddof=1)).mean(axis=0)
total_var_raw = responses_val_raw.reshape([-1, num_neurons]).var(axis=0, ddof=1)
nnp = obs_var / total_var

# Calculate fraction of explainable variance explained
#   Linden & Sahani calculate it in a different way, but the result is the same.
#   I'm not 100% sure why it comes out the same, though.
obs_var = (responses_val_raw.var(axis=0, ddof=1) / reps).mean(axis=0)   # Linden & Sahani call this 'noise power'
total_var = responses_val_raw.mean(axis=0).var(axis=0, ddof=1)
explainable_var = total_var - obs_var   # Linden & Sahani call this 'signal power'
mse = ((prediction - responses_val_raw.mean(axis=0)) ** 2).mean(axis=0)
eve = (total_var - mse) / explainable_var