# Import ica function
from app import ica_infomax
import numpy as np
import matplotlib.pyplot as plt
import time


def main():
    # Define matrix dimensions
    Nobs = 1000  # Number of observation
    Nvars = 50000  # Number of variables
    Ncomp = 100  # Number of components

    # Simulated true sources
    S_true = np.random.logistic(0, 1, (Ncomp, Nvars))
    # Simulated true mixing
    A_true = np.random.normal(0, 1, (Nobs, Ncomp))
    # print(A_true.shape)
    # X = AS
    X = np.dot(A_true, S_true)
    # add some noise
    X = X + np.random.normal(0, 1, X.shape)
    # print(X.shape)
    # apply ICA on X and ask for 2 components

    model = ica_infomax.ica(Ncomp)

    start = time.time()
    A, S = model.fit(X)
    total = time.time() - start
    print('total time: {}'.format(total))
    # compare if our estimates are accurate
    # correlate A with Atrue and take
    aCorr = np.abs(np.corrcoef(A.T, A_true.T)[ :Ncomp, Ncomp: ]).max(axis=0).mean()
    sCorr = np.abs(np.corrcoef(S, S_true)[ :Ncomp, Ncomp: ]).max(axis=0).mean()

    print("Accuracy of estimated sources: %.2f" % sCorr)
    print("Accuracy of estimated mixing: %.2f" % aCorr)


if __name__ == "__main__":
    # import theano.sandbox.cuda
    #
    # theano.sandbox.cuda.use('gpu')

    main()