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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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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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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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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)

stormxuwz

from __future__ import division
import numpy as np


class OnlineClf(object):
	
	def __init__(self,iterNum,R):
		self.w=None
		self.iterNum=iterNum
		self.misNum=[[0,0]]

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import numpy as np

def slidingWindow(x,max_error):
	n=len(x)
	leftNode=0
	segmentList=[]
	print n
	
	while leftNode<n-1:
		print leftNode

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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):

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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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## 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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# 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)))