keunwoochoi

def istft(stft_matrix, hop_length=None, win_length=None, window='hann',
          center=True, normalized=False, onesided=True, length=None):
    """stft_matrix = (batch, freq, time, complex) 
    
    All based on librosa
        - http://librosa.github.io/librosa/_modules/librosa/core/spectrum.html#istft
    What's missing?
        - normalize by sum of squared window --> do we need it here?
        Actually the result is ok by simply dividing y by 2. 
    """

danyashorokh

import pandas as pd

# Calculate information value
def calc_iv(df, feature, target, pr=0):

    lst = []

    for i in range(df[feature].nunique()):
        val = list(df[feature].unique())[i]
        lst.append([feature, val, df[df[feature] == val].count()[feature], df[(df[feature] == val) & (df[target] == 1)].count()[feature]])

joshbuchea

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

khalladay

typedef unsigned char      uint8;
typedef unsigned short     uint16;
typedef unsigned int          uint32;

#define REG_DISPLAYCONTROL	*((volatile uint32*)(0x04000000))
#define VIDEOMODE_3     	               0x0003
#define BGMODE_2 			       0x0400

#define SCREENBUFFER                    ((volatile uint16*)0x06000000)
#define SCREEN_W                            240

YohanObadia

import numpy as np
import pandas as pd
from collections import defaultdict
from scipy.stats import hmean
from scipy.spatial.distance import cdist
from scipy import stats
import numbers


def weighted_hamming(data):

timmolderez

If you'd like to use a .jar file in your project, but it's not available in any Maven repository, 
you can get around this by creating your own local repository. This is done as follows:

1 - To configure the local repository, add the following section to your pom.xml (inside the <project> tag):
  
  <repositories>
      <repository>
        <id>in-project</id>
        <name>In Project Repo</name>
        <url>file://${project.basedir}/libs</url>

voxxit

Run Book / Operations Manual

1. Table of Contents
2. System Overview
   • Service Overview
   • Contributing Applications, Daemons, and Windows Services
   • Hours of Operation
   • Execution Design
   • Infrastructure and Network Design
   • Resilience, Fault Tolerance and High-Availability

cjzamora

Source: http://datahugger.org/datascience/setting-up-hadoop-v2-with-spark-v1-on-osx-using-homebrew/

This post builds on the previous setup Hadoop (v1) guide, to explain how to setup a single node Hadoop (v2) cluster with Spark (v1) on OSX (10.9.5).

Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. The Apache Hadoop framework is composed of the following core modules:

HDFS (Distributed File System): a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster.
YARN (Yet A

gajus

http://jhusain.github.io/learnrx/

Like an Event, an Observable is a sequence of values that a data producer pushes to the consumer. However unlike an Event, an Observable can signal to a listener that it has completed, and will send no more data.

Querying Arrays only gives us a snapshot. By contrast, querying Observables allows us to create data sets that react and update as the system changes over time. This enables a very powerful type of programming known as reactive programming.

Disposing of a Subscription object unsubscribes from the event and prevents memory leaks. Disposing of a subscription is the asynchronous equivalent of stopping half-way through a counting for loop.

If we convert Events to Observable Objects, we can use powerful functions to transform them.

ShamylZakariya

func debounce( delay:NSTimeInterval, #queue:dispatch_queue_t, action: (()->()) ) -> ()->() {
	
	var lastFireTime:dispatch_time_t = 0
	let dispatchDelay = Int64(delay * Double(NSEC_PER_SEC))
	
	return {
		lastFireTime = dispatch_time(DISPATCH_TIME_NOW,0)
		dispatch_after(
			dispatch_time(
				DISPATCH_TIME_NOW,