from sklearn import neighbors
import json
import directoryGetter as dg
from sklearn.externals import joblib
# testing
print 'testing kNN model ... ...'
knn = joblib.load('jingju_pitchContoursClassificationModel.pkl')
testingFeaturesFolder = './testingFeatures/'
testingFeaturesfilenames = dg.jsonFilenameGetter(testingFeaturesFolder)
for testingFilename in testingFeaturesfilenames:
with open(testingFilename) as data_file:
data = json.load(data_file)
X = data['featureVec']
y = data['toRmIndex']
classification = knn.predict(X) # 0: voice 1: nonvoice
classification = classification.tolist()
outputFilename = testingFilename[:-5] + '_classification.json'
with open(outputFilename, 'w') as outfile:
data = {'featureVec': X, 'toRmIndex': y, 'classification': classification}
json.dump(data, outfile)
# -*- coding: utf-8 -*-
import contourReader as cr
import featuresCalc as fc
import directoryGetter as dg
import matplotlib.pyplot as plt
import json
voicePath = '/home/rgong/MTG/jingjuMelody/voicePitchContours'
nonvoicePath = '/home/rgong/MTG/jingjuMelody/nonvoicePitchContours'
filenamesvoicePath = dg.jsonFilenameGetter(voicePath)
filenamesnonvoicePath = dg.jsonFilenameGetter(nonvoicePath)
allfilenames = filenamesvoicePath + filenamesnonvoicePath
# it contains each feature as a list [lengthContours, meanPitchContour ... mfcc1, mfcc2 ...]
featureList = []
for ii in range(19):
featureList.append([])
featureVec = []
target = []
featureMeanSd = []
def featureListCreate(featureList, lengthContour, meanPitchContour, sdPitchContour, totalSalience, meanSalience, sdSalience, mfccs, voicing):
'''
reshape features into featureList variable
'''
for ii in range(len(lengthContour)):
featureList[0].append(lengthContour[ii])
