import tensorflow as tf
import numpy as np
import math


def uniform(shape, scale=0.05, name=None):
    """Uniform init."""
    initial = tf.random_uniform(shape, minval=-scale, maxval=scale, dtype=tf.float32)
    return tf.Variable(initial, name=name)


def glorot(shape, name=None):
    """Glorot & Bengio (AISTATS 2010) init."""
    init_range = np.sqrt(6.0/(shape[0]+shape[1]))
    initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32)
    return tf.Variable(initial, name=name)


def zeros(shape, name=None):
    """All zeros."""
    initial = tf.zeros(shape, dtype=tf.float32)
    return tf.Variable(initial, name=name)


def ones(shape, name=None):
    """All ones."""
    initial = tf.ones(shape, dtype=tf.float32)
    return tf.Variable(initial, name=name)


def trunc_normal(shape, name=None, normalize=True):
    initial = tf.Variable(tf.truncated_normal(shape, stddev=1.0 / math.sqrt(shape[0])))
    if not normalize: return initial
    return tf.nn.l2_normalize(initial, 1)