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test_function_1 <- function(a=1){
		cat("a is here",a)  
}

test_function_2 <- function(b,...){
	test_function_1(...)
}

test <- function(a,b){
	test_function_2(b,a)

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import statsmodels.tsa as st
import numpy as np
import numpy.linalg as nl
import statsmodels.tsa as tsa

def solver_leastSquare(A,y):
	return nl.lstsq(A,y);

def read_data(filename):
	x=[]

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def omega(N,k,n):
	return np.exp(-2*np.pi/N*k*n*1j)

def hanWindow(N):
	diag=np.array([np.sin(np.pi*i/(N-1))**2 for i in range(N)])
	H=np.diag(diag)
	return H

def createF(N):
	F=np.zeros((N,N),dtype=complex)

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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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 #### LDA,QDA, RDA, naive baye functions ####
 
 lda.model=function(traindata){
 	lda.result=lda(Y~.,data=traindata)
	return(lda.result)
 }
 
 lda.pred=function(model,dataSets.X){
   predresult=lapply(dataSets.X,function(X) predict(model,X)$class) 
	 errorInfo(predresult,"lda")

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import urllib2;
import numpy as np;
import json;
from pyproj import Geod;

def decode_line(encoded):
    """copied from https://github.com/jconst/Byway-Server/blob/master/polyline.py
    """

    encoded_len = len(encoded)

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# include <iostream>

using namespace std;

struct person{
	person * previousPerson;
	person * nextPerson;
	int sword;
	int num;
};

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# http://stackoverflow.com/questions/10628847/geom-boxplot-with-precomputed-values

library(ggplot2)

DF <- data.frame(x=c("A","B"), min=c(1,2), low=c(2,3), mid=c(3,4), top=c(4,5), max=c(5,6))

ggplot(DF, aes(x=x, ymin = min, lower = low, middle = mid, upper = top, ymax = max)) +
  geom_boxplot(stat = "identity")

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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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import numpy as np
from scipy.io.wavfile import read,write
import matplotlib.pyplot as plt 
from matplotlib.pylab import *

def omega(N,k,n):
	return np.exp(-2*np.pi/N*k*n*1j)

def hanWindow(N):
	diag=np.array([np.sin(np.pi*i/(N-1))**2 for i in range(N)])