def measure_error(V, index, kernel_times, delay_indexes, image_indexes,
input_to_image, kernel_to_input, h0, h1, h2, ims, ims2):
'''
Gives the difference between the value and the prediction
'''
return (V[index] -
hypothesis(index, kernel_times, delay_indexes, image_indexes,
input_to_image, kernel_to_input, h0, h1, h2, ims, ims2))
def calculate_prediction(data_indexes, kernel_times, delay_indexes, image_indexes, input_to_image,
kernel_to_input, h0, h1, h2, ims, ims2):
'''
returns an array with the predictions for the indexes given in data_indexes
'''
prediction = np.zeros(data_indexes.size)
# Scale factors
# Calculate prediction
for index_index, index in enumerate(data_indexes):
prediction[index_index] = hypothesis(index, kernel_times, delay_indexes, image_indexes,
input_to_image, kernel_to_input, h0, h1, h2, ims, ims2)
return prediction
def calculate_prediction(data_indexes, kernel_times, delay_indexes,
image_indexes, input_to_image, kernel_to_input, h0,
h1, h2, ims, ims2):
'''
returns an array with the predictions for the indexes given in data_indexes
'''
prediction = np.zeros(data_indexes.size)
# Scale factors
# Calculate prediction
for index_index, index in enumerate(data_indexes):
prediction[index_index] = hypothesis(index, kernel_times,
delay_indexes, image_indexes,
input_to_image, kernel_to_input,
h0, h1, h2, ims, ims2)
return prediction
def measure_error(V, index, kernel_times, delay_indexes, image_indexes, input_to_image, kernel_to_input, h0, h1, h2, ims, ims2):
'''
Gives the difference between the value and the prediction
'''
return (V[index] - hypothesis(index, kernel_times, delay_indexes, image_indexes,
input_to_image, kernel_to_input, h0, h1, h2, ims, ims2))
