"""
# -*- coding: utf-8 -*-
#
# Copyright 2021 Michael Büsch <m@bues.ch>
#
# Licensed under the Apache License version 2.0
# or the MIT license, at your option.
# SPDX-License-Identifier: Apache-2.0 OR MIT
#
"""

from mlplib.activation import *
from mlplib.forward import *
from mlplib.init import *
from mlplib.parameters import *
import numpy as np

def test_forward_prop_singleinput():
    seed(42)
    x = np.array([1, 2])
    inputs = 2
    layout = (3, 1)
    params = Parameters(
        weights=init_layers_weights(inputs, layout),
        biases=init_layers_biases(layout),
        actvns=[
            Sigmoid(),
            Sigmoid(),
        ],
    )
    yh, state = forward_prop(x, params, store_netstate=True)
    assert np.allclose(yh, np.array([0.77996251]))

def test_forward_prop_multiinput():
    seed(43)
    x = np.array([[1, 2],
                  [9, 10],
                  [8, 7],
                  [6, 5]])
    inputs = 2
    layout = (3, 1)
    params = Parameters(
        weights=init_layers_weights(inputs, layout),
        biases=init_layers_biases(layout),
        actvns=[
            Sigmoid(),
            Sigmoid(),
        ],
    )
    yh, state = forward_prop(x, params, store_netstate=True)
    assert np.allclose(yh,
        np.array([[0.81728153],
                  [0.73177059],
                  [0.60556236],
                  [0.62344552],]))

def test_forward_prop_nostore():
    seed(43)
    x = np.array([[1, 2],
                  [9, 10],
                  [8, 7],
                  [6, 5]])
    inputs = 2
    layout = (3, 1)
    params = Parameters(
        weights=init_layers_weights(inputs, layout),
        biases=init_layers_biases(layout),
        actvns=[
            Sigmoid(),
            Sigmoid(),
        ],
    )
    yh = forward_prop(x, params)
    assert np.allclose(yh,
        np.array([[0.81728153],
                  [0.73177059],
                  [0.60556236],
                  [0.62344552],]))

# vim: ts=4 sw=4 expandtab