data_generation.py

import numpy as np
import pandas as pd
import os.path

# synthetic case from uds
def correlated_data(m, n, sigma, f):
    l = int(n / 2)
    Z = np.random.normal(0, 1, (m, l))
    A = np.matrix(np.random.uniform(0, 1, (l, l)))
    X1 = Z * A
    B = np.matrix(np.random.uniform(0, 0.5, (l, l)))
    W = X1 * B
    E = np.random.normal(0, sigma, (m, l))
    X2 = f(W) + E
    result = np.append(X1, X2, axis=1)
    print(result)
    return result


def generate_uncorrelated_data(m, n):
    return np.random.normal(0, 1, (m, n))


def func1(X):
    return 2 * X + 1


def func2(X):
    return np.log2(np.abs(X) + 1)


def synthetic_data_uni(m, r, s, sigma=0.1):
    r_dims = np.random.uniform(-0.5, 0.5, (m, r)) if r > 0 else np.empty((m, r))
    parity_dim = -(np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.random.uniform(0, 0.5,
                                                                                                       (m, 1)) if r > 0 else np.empty((m, r))
    s_dims = np.random.uniform(-0.5, 0.5, (m, s))
    data = np.concatenate((r_dims, parity_dim, s_dims), axis=1)
    if sigma:
        e = np.random.normal(0, sigma, (m, r + s + 1))
        data = data + e

    return data


def synthetic_data_uni_negative(m, r, s, sigma=0.1):
    r_dims = np.random.uniform(-0.5, 0.5, (m, r)) if r > 0 else np.empty((m, r))
    parity_dim = (np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.random.uniform(0, 0.5,
                                                                                                       (m, 1)) if r > 0 else np.empty((m, r))
    s_dims = np.random.uniform(-0.5, 0.5, (m, s))
    data = np.concatenate((r_dims, parity_dim, s_dims), axis=1)
    if sigma:
        e = np.random.normal(0, sigma, (m, r + s + 1))
        data = data + e

    return data


def synthetic_data_gauss(m, r, s, sigma=0.1):

    r_dims = np.random.normal(0, 1, (m, r)) if r > 0 else np.empty((m, r))
    parity_dim = -(np.count_nonzero(r_dims > 0, axis=1) % 2 * 2 - 1).reshape(m, 1) * np.abs(np.random.normal(0, 1,
                                                                                                             (m, 1))) if r > 0 else np.empty((m, r))
    s_dims = np.random.normal(0, 1, (m, s))
    data = np.concatenate((r_dims, parity_dim, s_dims), axis=1)
    if sigma:
        e = np.random.normal(0, sigma, (m, r + s + 1))
        data = data + e

    return data


def synthetic_with_nearcopies(m, k, l, sigma=0.1):
    k_dims = np.repeat(np.random.uniform(-0.5, 0, (m, 1)), k, axis=1) if k > 0 else np.empty((m, k))
    l_dims = np.repeat(np.random.uniform(0, 0.5, (m, 1)), l, axis=1) if l > 0 else np.empty((m, l))

    data = np.concatenate((k_dims, l_dims), axis=1)
    if sigma:
        e = np.random.normal(0, sigma, (m, k + l))
        data = data + e

    return data


if __name__ == '__main__':
    rows = 20000
    data__ = np.concatenate((synthetic_with_nearcopies(rows, 2, 0, 0), np.zeros((rows, 1))), axis=1)
    # file = 'synthetic_cases/synthetic_3d_gauss2.csv'
    file = 'synthetic_cases/synthetic_exact_copies2_2.csv'

    if os.path.isfile(file):
        raise ValueError
    pd.DataFrame(data__).to_csv(file, sep=';', header=False, index=False, float_format='%.2f')