def residualize(X, ps):
    numObs = X.shape[0]
    resHat = np.zeros((numObs,1))
    hOptimal = LLR.optimalBandwidthSelection(X, ps)
    # for each observations, get prediction
    for iObs in range(numObs):
        xFit = LLR.polynomialFit(X,ps, ps[iObs], hOptimal)
        resHat[iObs] = X[iObs] - xFit[0]
    return resHat
def residualize(X, ps):
    numObs = X.shape[0]
    resHat = np.zeros((numObs, 1))
    hOptimal = LLR.optimalBandwidthSelection(X, ps)
    # for each observations, get prediction
    for iObs in range(numObs):
        xFit = LLR.polynomialFit(X, ps, ps[iObs], hOptimal)
        resHat[iObs] = X[iObs] - xFit[0]
    return resHat
def LLRKpLIVOptimalBandwidth(yTilda, ps):
    hOptimal = LLR.optimalBandwidthSelection(yTilda, ps)
    return hOptimal
import localLinearRegression as LLR
import numpy as np

dataList = []
f = open('D:/gitRepo/grmEstimatorToolbox/mteApproach/data/testData.csv','rb')
import csv
readed = csv.reader(f)
for row in readed:
    dataList.append(row)

'''
    array - the list
'''
data = np.array(dataList, dtype = float)
Y = data[:,0:1]**(1./3.)
X = data[:,1:2]   

'''
    for unit test purpose, specify the hVec
'''
hVec = np.arange(20,100,1)
hOptimal = LLR.optimalBandwidthSelection(Y, X, hVec)
assert abs(hOptimal - 93)<1

hOptimal = LLR.optimalBandwidthSelection(Y, X)
assert abs(hOptimal - 84)<1

print('localLinearRegression passes unit test')
    
    
def LLRKpLIVOptimalBandwidth(yTilda, ps):
    hOptimal = LLR.optimalBandwidthSelection(yTilda, ps)
    return hOptimal
Beispiel #6
0
#print test1, test2

#from mpi4py import MPI
#import numpy

#comm = MPI.COMM_WORLD
#rank = comm.Get_rank()

#if rank == 0:
#    data = numpy.arange(1000, dtype = 'i')
#    comm.Send([data, MPI.INT], dest = 1, tag = 77)
#elif rank == 1:
#    data = numpy.empty(1000, dtype = 'i')
#    comm.Recv([data, MPI.INT], source = 0, tag = 77)


import sampleSimulation as ss


import localLinearRegression as LLR
import numpy
Y,D,X,Z = ss.underiv_data(500)
h = numpy.arange(0.05,2,0.05)

LLR.optimalBandwidthSelection(Y, X[:,0:1], h)


# Retrieves the result calculated by job1
# The value of job1() is the same as sum_primes(100)
# If the job has not been finished yet, execution will wait here until result is available