sergeyprokudin

#http://www.svcl.ucsd.edu/courses/ece161c/handouts/DCT.pdf

import numpy as np
import matplotlib.pyplot as plt

pi = np.pi

N = 50 # timesteps
k = 10 # n basis functions

sergeyprokudin

import keras.backend as K
import numpy as np


def gaussian_nll(ytrue, ypreds):
    """Keras implmementation of multivariate Gaussian negative loglikelihood loss function. 
    This implementation implies diagonal covariance matrix.
    
    Parameters
    ----------

sergeyprokudin

import numpy as np


def count_conv_params_flops(conv_layer, verbose=1):
    # out shape is  n_cells_dim1 * (n_cells_dim2 * n_cells_dim3)
    out_shape = conv_layer.output.shape.as_list()
    n_cells_total = np.prod(out_shape[1:-1])

    n_conv_params_total = conv_layer.count_params()

sergeyprokudin

import os

SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
PROJECT_DIR = os.path.dirname(SCRIPT_DIR)
LOG_DIR = os.path.join(PROJECT_DIR, 'logs')

sergeyprokudin

def random_number_generator(arg1, arg2):
    """
    Summary line.

    Extended description of function.

    Parameters
    ----------
    arg1 : int
        Description of arg1

sergeyprokudin

def sample_dball_uniform(n_points=1000, n_dims=3):
    """Sample uniformly from d-dimensional ball
    
    The code is inspired by this small note:
    https://blogs.sas.com/content/iml/2016/04/06/generate-points-uniformly-in-ball.html
    https://www.sciencedirect.com/science/article/pii/S0047259X10001211
    
    Parameters
    ----------
    n_points : int

sergeyprokudin

import numpy as np

def hist_pdf(x, data, n_bins=10, minv=None, maxv=None):
    """Histogram density estimator
    
    [minv, minv+delta, , minv+delta*2, ..., 1]
    
    for any x in B_l=[minv+delta*j, minv+delta*(j+1)] density is estimated in the following way
    
    p(x) = p(x | x \in B_l) * p(x \in B_l) = (1/delta) * (\sum_{x_j}{I(x_j \in B_l)} / N)

sergeyprokudin

#!/usr/bin/env python
# coding: utf-8

# In[32]:


import numpy as np

def hist_pdf(x, data, n_bins=10, minv=None, maxv=None):
    """Histogram density estimator

sergeyprokudin

def is_permuted_palindrome(s):
    """Checks if the string is a permuted version of a palindrome
    
    Upper case do not matter, spaces can be ignored.
    
    Example:
    
    "cat cat" -> Yes (palindrome: "cat tac")
    "cat do"  -> No
    

sergeyprokudin

def set_bit(x, i):
    bi = 0b1 << i
    return x | bi

def xor_bit(x, i):
    bi = 0b1 << i
    return x ^ bi

def clear_bit(x, i):
    bi = ~(0b1 << i)