z_helper.py

import numpy as np
from numba import njit


def import_from_csv(path, data_type):
    return np.genfromtxt(path, dtype=data_type, delimiter=',')


def class_to_array(maximum_class, x):
    data = np.zeros(maximum_class) + 0.01
    data[x-1] = 0.99
    return data


def kfold(k, data, seed=99):
    np.random.seed(seed)
    data = np.random.permutation(data)
    fold_size = int(len(data) / k)
    return data[fold_size*2:], data[:fold_size], data[fold_size:fold_size*2]


@njit('float64[:, ::1](float64[:, ::1], boolean)')
def sigmoid(x, derivative):
    if derivative:
        return x * (1.0 - x)
    else:
        return 1.0 / (1.0 + np.exp(-x))


@njit('float64[:, ::1](float64[:, ::1], boolean)')
def relu(x, derivative):
    if derivative:
        return np.where(x <= 0.0, 0.0, 1.0)
    else:
        return np.maximum(0.0, x)


@njit('float64[:, ::1](float64[:, ::1], boolean)')
def leaky_relu(x, derivative):
    if derivative:
        return np.where(x <= 0.0, -0.01*x, 1.0)
    else:
        return np.maximum(-0.01*x, x)


@njit('float64[:, ::1](float64[:, ::1], boolean)')
def softmax(x, derivative):
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()