jshahbazi

    function convolve(x, h)
        implicit none
        
        !x is the signal array
        !h is the noise/impulse array
        real, dimension(:), allocatable :: convolve, y
        real, dimension(:) :: x, h
        integer :: kernelsize, datasize
        integer :: i,j,k
        

jshahbazi

integer function djb_hash(str) result(hash)
    implicit none
    
    character(len=*),intent(in) :: str
    integer :: hash
    integer :: i

    hash = 5381

    do i=1,len(str)

jshahbazi

real function fmincg(length, nn_params, input_layer_size, hidden_layer_size, num_labels, inputdata, y, lambda)
    implicit none
    
    ! Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
    ! (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
    ! 
    ! Permission is granted for anyone to copy, use, or modify these
    ! programs and accompanying documents for purposes of research or
    ! education, provided this copyright notice is retained, and note is
    ! made of any changes that have been made.

jshahbazi

    subroutine matrix_multiply(first_matrix, first_transposed, second_matrix, second_transposed, result_matrix)
        !This subroutine multiplies two matrices using the Intel MKL xGEMM multiplication routines
        !The routines can be a little tricky, so I've made it a little easier to use them consistently
        !first_matrix and second_matrix are the input matrices
        !first_transposed and second_transposed are integers indicating (with a 1 or 0) whether or not
        !the respective matrix is transposed
        !The resulting matrix is stored in result_matrix, but you'll have to allocate that outside the routine
        !The sGEMM below can be changed to dGEMM for double precision without any other changes to the
        !subroutine call.  You'll then have to change the 'real' variables to 'double precision'.
        

jshahbazi

class AsyncTokenBucket(object):
    """Implementation of an awaitable token bucket that defaults to a single token at a time.
       Based on https://code.activestate.com/recipes/511490-implementation-of-the-token-bucket-algorithm/"""
    
    def __init__(self, max_tokens, fill_rate):
        self.max_tokens = float(max_tokens)
        self.current_tokens = float(max_tokens)
        self.fill_rate = float(fill_rate)
        self.wait_time = (1.0 / self.fill_rate)
        self.timestamp = time.time()