# This is a sample Python script.

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import numpy as np


def conv2d(z, h):
    sub_shape = h.shape
    view_shape = tuple(np.subtract(z.shape, sub_shape) + 1) + sub_shape
    strides = z.strides + z.strides
    sub_matrices = np.lib.stride_tricks.as_strided(z, view_shape, strides)
    m = np.einsum('ij,klij->kl', h, sub_matrices)

    return m


def state_expand(state, h, ovl_init=0):
    hw, hh = h.shape
    xw, xh = state.shape
    ovl_w = int((hw-1)/2)
    ovl_h = int((hh-1)/2)
    z = np.ones((xw+2*ovl_w, xh+2*ovl_h))*ovl_init
    z[ovl_w:ovl_w+xw, ovl_h:ovl_w+xh] = state

    return z


def state_init_incremental(size):
    state = np.zeros((size, size))
    k = 1
    m, n = np.shape(state)
    for i in range(0, m):
        for j in range(0, n):
            state[i, j] = k
            k += 1

    return state


def state_init_constant(size, ic=0):
    h = np.ones((size, size))*ic
    return h


def h_init(s=1):
    h = np.array([[1,1,1], [1,0,1], [1,1,1]])*s
    return h


# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    state = state_init_incremental(5)
    state = state_init_constant(5, 0)
    h = h_init(1.0/8)
    print (h)
    for i in range(0,10,1):
        state[0, 0] = 0
        if i == 0:
            state[0, 0] = 1
        state_ex = state_expand(state, h)
        state = 0.5*state + 0.5*conv2d(state_ex, h)
        print(state)


# See PyCharm help at https://www.jetbrains.com/help/pycharm/