utils.py

__author__ = 'Pouya Bashivan'

import math as m
import numpy as np
np.random.seed(123)
import scipy.io
from sklearn.decomposition import PCA

def cart2sph(x, y, z):
    """
    Transform Cartesian coordinates to spherical
    :param x: X coordinate
    :param y: Y coordinate
    :param z: Z coordinate
    :return: radius, elevation, azimuth
    """
    x2_y2 = x**2 + y**2
    r = m.sqrt(x2_y2 + z**2)                    # r
    elev = m.atan2(z, m.sqrt(x2_y2))            # Elevation
    az = m.atan2(y, x)                          # Azimuth
    return r, elev, az


def pol2cart(theta, rho):
    """
    Transform polar coordinates to Cartesian
    :param theta: angle value
    :param rho: radius value
    :return: X, Y
    """
    return rho * m.cos(theta), rho * m.sin(theta)


def augment_EEG(data, stdMult, pca=False, n_components=2):
    """
    Augment data by adding normal noise to each feature.

    :param data: EEG feature data as a matrix (n_samples x n_features)
    :param stdMult: Multiplier for std of added noise
    :param pca: if True will perform PCA on data and add noise proportional to PCA components.
    :param n_components: Number of components to consider when using PCA.
    :return: Augmented data as a matrix (n_samples x n_features)
    """
    augData = np.zeros(data.shape)
    if pca:
        pca = PCA(n_components=n_components)
        pca.fit(data)
        components = pca.components_
        variances = pca.explained_variance_ratio_
        coeffs = np.random.normal(scale=stdMult, size=pca.n_components) * variances
        for s, sample in enumerate(data):
            augData[s, :] = sample + (components * coeffs.reshape((n_components, -1))).sum(axis=0)
    else:
        # Add Gaussian noise with std determined by weighted std of each feature
        for f, feat in enumerate(data.transpose()):
            augData[:, f] = feat + np.random.normal(scale=stdMult*np.std(feat), size=feat.size)
    return augData


def augment_EEG_image(image, stdMult, pca=False, n_components=2):
    """
    Augment data by adding normal noise to each feature.

    :param image: EEG feature data as a a colored image [n_samples, n_colors, W, H]
    :param stdMult: Multiplier for std of added noise
    :param pca: if True will perform PCA on data and add noise proportional to PCA components.
    :param n_components: Number of components to consider when using PCA.
    :return: Augmented data as a matrix (n_samples x n_features)
    """
    augData = np.zeros((data.shape[0], data.shape[1], data.shape[2] * data.shape[3]))
    for c in xrange(image.shape[1]):
        reshData = np.reshape(data['featMat'][:, c, :, :], (data['featMat'].shape[0], -1))
        if pca:
            augData[:, c, :] = augment_EEG(reshData, stdMult, pca=True, n_components=n_components)
        else:
            augData[:, c, :] = augment_EEG(reshData, stdMult, pca=False)
    return np.reshape(augData, data['featMat'].shape)


def load_data(data_file):
    """
    Loads the data from MAT file. MAT file should contain two
    variables. 'featMat' which contains the feature matrix in the
    shape of [samples, features] and 'labels' which contains the output
    labels as a vector. Label numbers are assumed to start from 1.

    Parameters
    ----------
    data_file: str

    Returns
    -------
    data: array_like
    """
    print("Loading data from %s" % (data_file))

    dataMat = scipy.io.loadmat(data_file, mat_dtype=True)
    data = dataMat['featMat']
    labels = dataMat['labels']
    # indices = np.random.permutation(labels.shape[1])      # shuffling indices

    print("Data loading complete. Shape is %r" % (dataMat['featMat'].shape,))
    # return data[indices, :, :, :].astype(np.uint8), labels[:, indices].T - 1        # Shuffled indices
    return dataMat['featMat'], dataMat['labels'].T - 1   # Sequential indices


def reformatInput(data, labels, indices):
    """
    Receives the the indices for train and test datasets.
    Outputs the train, validation, and test data and label datasets.
    """

    trainIndices = indices[0][len(indices[1]):]
    validIndices = indices[0][:len(indices[1])]
    testIndices = indices[1]
    # Shuffling training data
    # shuffledIndices = np.random.permutation(len(trainIndices))
    # trainIndices = trainIndices[shuffledIndices]
    if data.ndim == 4:
        return [(data[trainIndices], np.squeeze(labels[trainIndices]).astype(np.int32)),
                (data[validIndices], np.squeeze(labels[validIndices]).astype(np.int32)),
                (data[testIndices], np.squeeze(labels[testIndices]).astype(np.int32))]
    elif data.ndim == 5:
        return [(data[:, trainIndices], np.squeeze(labels[trainIndices]).astype(np.int32)),
                (data[:, validIndices], np.squeeze(labels[validIndices]).astype(np.int32)),
                (data[:, testIndices], np.squeeze(labels[testIndices]).astype(np.int32))]


if __name__ == '__main__':
    data = np.random.normal(size=(100, 10))
    print 'Original: {0}'.format(data)
    print 'Augmented: {0}'.format(augment_EEG(data, 0.1, pca=True))