utils.py

import numpy as np
import sys


def hot_encoding(data, num_label):
    return np.eye(num_label)[data]


def classification_rate(Y, P):
    n_correct = 0
    n_total = 0
    misclassified = {
        '0': 0,
        '1': 0,
        '2': 0,
        '3': 0,
        '4': 0,
        '5': 0,
        '6': 0,
        '7': 0,
        '8': 0,
        '8': 0,
        '9': 0
    }
    for i in range(len(Y)):
        n_total += 1
        if Y[i] == P[i]:
            n_correct += 1
        else:
            misclassified[str(Y[i])] += 1
    return misclassified, float(n_correct) / n_total


def prepare_X(data):
    # X = np.transpose(data)
    X = data / 255.
    return X


def prepare_Y(data, hot_encoding_labels):
    # Y = np.transpose(data)
    Y_E = hot_encoding(data, hot_encoding_labels)

    return data, Y_E


def unison_shuffled_copies(a, b):
    assert len(a) == len(b)
    p = np.random.permutation(len(a))
    return a[p], b[p]


def ValueInvert(array):
    # Flatten the array for looping
    flatarray = array.flatten()

    # Apply transformation to flattened array
    for i in range(flatarray.size):
        flatarray[i] = 255 - flatarray[i]

    # Return the transformed array, with the original shape
    return flatarray.reshape(array.shape)


def read_variable_from_console():
    # test_name, num_hidden_layer, learning rate
    return str(sys.argv[1]), int(sys.argv[2]), float(sys.argv[3])


def softmax(X):
    expA = np.exp(X)
    return expA / expA.sum(axis=1, keepdims=True)