"""
# -*- 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
#
"""

__all__ = [
    "init_biases",
    "init_layers_biases",
    "init_weights",
    "init_layers_weights",
    "seed",
    "random",
    "standard_normal",
]

import numpy as np

def init_biases(nr_neurons, initial=0.0):
    return np.full((1, nr_neurons), initial)

def init_layers_biases(layout, initial=0.0):
    return [ init_biases(nr_neurons, initial)
             for nr_neurons in layout ]

def init_weights(nr_inputs, nr_neurons):
    return (standard_normal((nr_inputs, nr_neurons)) *
            np.sqrt(2.0 / nr_inputs))

def init_layers_weights(nr_inputs, layout):
    layer_inputs = [nr_inputs] + list(layout[:-1])
    return [ init_weights(nr_inputs=nr_inputs, nr_neurons=nr_neurons)
             for nr_inputs, nr_neurons in zip(layer_inputs, layout) ]

def seed(s):
    np.random.seed(s)

def random(shape):
    return np.random.random_sample(shape)

def standard_normal(shape):
    return np.random.standard_normal(shape)

# vim: ts=4 sw=4 expandtab