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// This code is from https://www.cac.cornell.edu/VW/OpenMP/whileloop.aspx

#include <omp.h>
#include <stdio.h>
int main(int argc, char **argv)
{
  /* OpenMP does not provide a parallel while loop,
     so we're going to have to make one ourselves... */
  int sj, sstop, tn, tj, tstop;
  int foo(int j);

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from __future__ import division
import numpy as np;
import matplotlib.pyplot as plt
import warnings

# Ref: http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
# Ref2:http://python-speech-features.readthedocs.org/en/latest/. However, I checked the library code and found there might be a mistake when using rfft

def preemphasis(signal,coeff=0.95):   
    return numpy.append(signal[0],signal[1:]-coeff*signal[:-1])

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# gist only to keep record from https://www.jiqizhixin.com/articles/bb59b879-e030-400d-abbc-d9c0708266ff

# Random Forest Algorithm on Sonar Dataset
from random import seed
from random import randrange
from csv import reader
from math import sqrt

# Load a CSV file
def load_csv(filename):

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# list_forecasts = [[1,3],[2,5]]

## cover(1pm, 7pm, [[1,3], [5,7]])
## return [[3,5]]

def cover(startTime, endTime, list_forecasts):
    n = len(list_forecasts)
    

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plot_gly_on_map <- function(newDF, global = FALSE, trend = FALSE, outputFile = "test.png"){
	# newDF$value <- newDF$summerSum_UHY - newDF$springSum_UHY
	longRange <- range(newDF$Long)
	latRange <- range(newDF$Lat)
	bbox <- make_bbox(longRange,latRange,f = 0.3)
	# myMap <- get_map(location=bbox, source="stamn",crop=TRUE,color="bw",maptype="terrain")
	myMap <- get_map(location = bbox, maptype="toner-lite", source="stamen",zoom=7,color = "bw",crop=TRUE)
	# ggmap(myMap)
	SU_locations <- unique(newDF[,c("Station","Lat","Long")])

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-- LaunchBar Action Script
-- apple script to append launchbar input to the last line of omnioutliner

on handle_string(_string)
	
	tell application "OmniOutliner 4.6.1"
		set theDocument to the front document
		set newRow to make new row at last row of front document
		set topic of newRow to _string
		outdent newRow

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# This is a pure python K-means implementation
import numpy as np

def calDistance(x,y):
	# return the distance of x and y
	return np.sum((x-y)**2)


def assignClusters(centers,data):
	distance = np.zeros((len(data),len(centers)))

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## Classification starts now
library(MASS)
library(e1071)
library(rda)
####################################
 qda.model=function(traindata){
 	qda.result=qda(Y~.,data=traindata)
 	return(qda.result)
 }

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randomForest_clf <- function(matTrain,trainLabel,matTest,...){
	library(randomForest)
	rf_model<-randomForest(matTrain,trainLabel,ntree=500)
	rf_predict<-predict(rf_model,matTest)
	return(rf_predict)
}


xgboost_clf_1000 <- function(matTrain,trainLabel,matTest,...){
	library(xgboost)

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from __future__ import division
import numpy as np
import chainer  # only for import the data at this script


def softmax(z):
	# z is a vector
    return np.exp(z) / np.sum(np.exp(z))

def sigmoid(x):