stormxuwz

import numpy as np

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

stormxuwz

origin='Madison,Chicago,IL';
destination='N+Michigan+Ave,Chicago,IL';

url1=['https://maps.googleapis.com/maps/api/directions/json?origin=' origin '&destination=' destination '&sensor=false&key= XXXX&departure_time=1343641500&mode=driving'];
direction=urlread(url1);
d=parse_json(direction);
steps=d{1}.routes{1}.legs{1}.steps;
[startpoints,endpoints]=cellfun(@parsecoord,steps);

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]]

stormxuwz

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

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=[]

stormxuwz

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)

stormxuwz

 #### 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")

stormxuwz

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)

stormxuwz

# include <iostream>

using namespace std;

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

stormxuwz

# 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")