Exemplos de calculate_prediction em Python

Exemplo n.º 1

Arquivo: plot_functions.py Projeto: juliettedz/M2_complexity_thesis

def plot_prediction(t, V, data_indexes, kernel_times, delay_indexes, image_indexes, 
                     input_to_image, kernel_to_input, h0, h1, h2, ims, ims2):
   
    prediction = calculate_prediction(data_indexes, kernel_times, delay_indexes, image_indexes, 
                                       input_to_image, kernel_to_input, h0, h1, h2, ims, ims2)
    # Plot data 
    plt.plot(t,V[data_indexes], '-*', label='Data')
    plt.hold('on')
    # Plot prediction
    plt.plot(t,prediction, '-*', label='Prediction')
    plt.legend()

Exemplo n.º 2

Arquivo: analysis_02.py Projeto: juliettedz/M2_complexity_thesis

    h1_sparse = np.load(filename_h1kernel_sparse)
    h2_sparse = np.load(filename_h2kernel_sparse)
    
    
    #### Load the dense part 

    filename_h1kernel_dense = kernels_folder + 'h1' + str(cell_number) + stimuli_type_dense + kernel_format
    filename_h2kernel_dense = kernels_folder + 'h2' + str(cell_number) + stimuli_type_dense + kernel_format

    h1_dense = np.load(filename_h1kernel_dense)
    h2_dense = np.load(filename_h2kernel_dense)
    
    # Calculate the convolutions
    aux_zeros = np.zeros(np.shape(h1_sparse))

    h1_sparse_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                         kernel_to_input, h0, h1_sparse, aux_zeros, ims_sparse, ims_sparse**2)
    
    h2_sparse_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                         kernel_to_input, h0, aux_zeros, h2_sparse, ims_sparse, ims_sparse**2)
    
    h1_dense_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                         kernel_to_input, h0, h1_dense, aux_zeros, ims_dense, ims_dense**2)
    
    h2_dense_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                         kernel_to_input, h0, aux_zeros, h2_dense, ims_dense, ims_dense**2)
    
    ###  Calculate the SI's 
    SI_sparse[cell_number] = np.sum(h1_sparse_convoluted**2) / np.sum( h2_sparse_convoluted**2 + h1_sparse_convoluted**2)
    SI_dense[cell_number] = np.sum(h1_dense_convoluted**2 ) / np.sum( h2_dense_convoluted**2 + h1_dense_convoluted**2)
    
    

Exemplo n.º 3

Arquivo: time_trace_cross_noise.py Projeto: juliettedz/M2_complexity_thesis

filename_h2kernel_sparse = kernels_folder + 'h2' + str(cell_number) + stimuli_type_sparse + kernel_format

h1_sparse = np.load(filename_h1kernel_sparse)
h2_sparse = np.load(filename_h2kernel_sparse)


#### Load the dense part 

filename_h1kernel_dense = kernels_folder + 'h1' + str(cell_number) + stimuli_type_dense + kernel_format
filename_h2kernel_dense = kernels_folder + 'h2' + str(cell_number) + stimuli_type_dense + kernel_format

h1_dense = np.load(filename_h1kernel_dense)
h2_dense = np.load(filename_h2kernel_dense)

# Calculate the convolutions
dense_over_sparsefield_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                     kernel_to_input, h0, h1_sparse, h2_sparse, ims_dense, ims_dense**2)


sparse_over_densefield_convoluted = calculate_prediction(training_indexes, kernel_times, delay_indexes, image_indexes, input_to_image, 
                     kernel_to_input, h0, h1_dense, h2_dense, ims_sparse, ims_sparse**2)


# cut a window of time_window
new_time_trace_SD = dense_over_sparsefield_convoluted[1: time_window]
new_time_trace_DS = sparse_over_densefield_convoluted[1: time_window]

print 'PLOT new time traces'

figure = plt.figure
plt.subplot(211)    
plt.plot(new_time_trace_SD)

Exemplo n.º 4

Arquivo: analysis_02.py Projeto: apdavison/M2_complexity_thesis

    #### Load the dense part

    filename_h1kernel_dense = kernels_folder + 'h1' + str(
        cell_number) + stimuli_type_dense + kernel_format
    filename_h2kernel_dense = kernels_folder + 'h2' + str(
        cell_number) + stimuli_type_dense + kernel_format

    h1_dense = np.load(filename_h1kernel_dense)
    h2_dense = np.load(filename_h2kernel_dense)

    # Calculate the convolutions
    aux_zeros = np.zeros(np.shape(h1_sparse))

    h1_sparse_convoluted = calculate_prediction(training_indexes, kernel_times,
                                                delay_indexes, image_indexes,
                                                input_to_image,
                                                kernel_to_input, h0, h1_sparse,
                                                aux_zeros, ims_sparse,
                                                ims_sparse**2)

    h2_sparse_convoluted = calculate_prediction(training_indexes, kernel_times,
                                                delay_indexes, image_indexes,
                                                input_to_image,
                                                kernel_to_input, h0, aux_zeros,
                                                h2_sparse, ims_sparse,
                                                ims_sparse**2)

    h1_dense_convoluted = calculate_prediction(training_indexes, kernel_times,
                                               delay_indexes, image_indexes,
                                               input_to_image, kernel_to_input,
                                               h0, h1_dense, aux_zeros,
                                               ims_dense, ims_dense**2)