myusuf3

To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes git add .gitmodules
• Delete the relevant section from .git/config.
• Run git rm --cached path_to_submodule (no trailing slash).
• Run rm -rf .git/modules/path_to_submodule (no trailing slash).
• Commit git commit -m "Removed submodule "
• Delete the now untracked submodule files rm -rf path_to_submodule

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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zchee

See also, http://libraryofalexandria.io/cgo/

Using Go cgo

cgo has a lot of trap.
but Not "C" pkg also directory in $GOROOT/src. IDE's(vim) Goto command not works.

So, Here collect materials.

zonque

/*
 * Minimalistic implementation of the XModem/YModem protocol suite, including
 * a compact version of an CRC16 algorithm. The code is just enough to upload
 * an image to an MCU that bootstraps itself over an UART.
 *
 * Copyright (c) 2014 Daniel Mack <github@zonque.org>
 *
 * License: MIT
 */

rygorous

#define _CRT_SECURE_NO_DEPRECATE

#include <stdio.h>
#include <string.h>
#include <Windows.h>

// This allocates a "magic ring buffer" that is mapped twice, with the two
// copies being contiguous in (virtual) memory. The advantage of this is
// that this allows any function that expects data to be contiguous in
// memory to read from (or write to) such a buffer. It also means that

bmcfee

bmcfee

import numpy as np
import scipy.linalg
import scipy.stats

def ks_key(X):
    '''Estimate the key from a pitch class distribution
    
    Parameters
    ----------
    X : np.ndarray, shape=(12,)

keunwoochoi

cqt_filter_fft = librosa.constantq.__cqt_filter_fft

class PseudoCqt():
    """A class to compute pseudo-CQT with Pytorch.
    Written by Keunwoo Choi
    API (+implementations) follows librosa (https://librosa.github.io/librosa/generated/librosa.core.pseudo_cqt.html)
    
    Usage:
        src, _ = librosa.load(filename)
        src_tensor = torch.tensor(src)

wassname

from torch.nn.modules.utils import _pair

class CausalConv2d(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1, bias=True):
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        dilation = _pair(dilation)
        if padding is None:
            padding = [int((kernel_size[i] -1) * dilation[i]) for i in range(len(kernel_size))]

aagontuk

/*
 * [PROG]		: Red Black Tree
 * [AUTHOR]		: Ashfaqur Rahman <sajib.finix@gmail.com>
 * [PURPOSE]		: Red-Black tree is an algorithm for creating a balanced
 * 			  binary search tree data structure. Implementing a red-balck tree
 * 			  data structure is the purpose of this program.
 * 
 * [DESCRIPTION]	: Its almost like the normal binary search tree data structure. But
 * 			  for keeping the tree balanced an extra color field is introduced to each node.
 *			  This tree will mantain bellow properties.