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testClasses.py
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from cv2 import *
import cv2.ml as ml
from Classes import *
import numpy as np
#def ler_arquivo(address):
#arquivo = open(address,"r") ##
#bd = [] ##
#obj = 0 ##
#for line in arquivo: ##
#bd.append([]) ##
#lines = line.split(",") ##
#for attr in lines: ##
#if(len(attr)>1): bd[obj].append(float(attr)) ##
#obj = obj+1 ##
#return bd ##
#experimento = Experiment()
#for percent in [1,2]:
#oDataSet = DataSet()
#bd = ler_arquivo("Classes/Classificators/FEATURES_M{:d}.txt".format(percent))
#for i in bd:
#oDataSet.add_sample_of_attribute(np.array(i[0:-1]), np.array(i[-1]))
#for it in range(50):
#oData = Data(7, 13)
#oData.confusion_matrix = 13 * np.random.rand(7, 7)
#a = np.arange(350)
#np.random.shuffle(a)
#t = a[:37*7]
#b= []
#for i in a:
#if not i in t:
#b.append(i)
#oDataSet.append(oData, t, b)
#experimento.add_data_set(oDataSet, description="Dataset do M={:d}:".format(percent))
#print experimento
#entrada = [[2,1,3,0],[0,1,1,3],[1,3,1,2],[0,1,0,2]]
#np_entrada = np.array(entrada)
#print np_entrada
#print np.vstack((np_entrada,np_entrada[0]))
#gl = GLCM(np_entrada, 2)
#gl.generateCoOccurenceHorizontal()
#gl.normalizeCoOccurence()
#gl.calculateAttributes()
#for nbits in [8]:
#arrayGLCM = np.zeros((0,25))
#for i in [4]:
#fname = "../isolador-multiclasse/base/tempo/filtrado_lucas/{}b/Classe_{}.txt.gz".format(nbits,i)
#array = np.loadtxt(fname, delimiter=",")
#array[139,1477] = 255
#print "Creating array of objects..."
#glArray = np.array([GLCM(np.matrix(x), nbits) for x in np.loadtxt(fname, delimiter=",")],dtype="object")
#print "generate co occurence matrix..."
#[gl.generateCoOccurenceHorizontal() for gl in glArray]
#print "Normalizing co occurence ..."
#[gl.normalizeCoOccurence() for gl in glArray]
#print "Calculating attributes..."
#[gl.calculateAttributes() for gl in glArray]
#print "Exporting attributes"
#for gl in glArray:
#arrayGLCM = np.vstack((arrayGLCM,gl.exportToClassfier(float(i+1))))
#print "Finish!"
#print arrayGLCM
#print "\n"
#np.savetxt("GLCM_FILES/{}b.txt.gz".format(nbits),arrayGLCM,delimiter = ",", fmt="%.10e")
#print "File Generated successful"
#for nbits in [5,6,7,8]:
#fname = "../SVM_ISOLADOR/GLCM_FILES/{}b.txt.gz".format(nbits)
#array = np.loadtxt(fname, delimiter=",")
#obDataSet = DataSet()
#for j in range(1,6):
#ar = array[array[:,-1]==j]
#np.random.shuffle(ar)
#ar = ar[:200]
#for i in ar:
#obDataSet.add_sample_of_attribute(i)
##obDataSet.normalize_data_set()
#for itIndex in range(niterations):
#obData = Data(5, 50, samples=200)
#obData.radndomTrainingTestPerClass()
#svm = cv2.SVM()
#obData.params = dict(kernel_type = cv2.SVM_RBF,svm_type = cv2.SVM_C_SVC,gamma=2.0,nu = 0.0,p = 0.0, coef0 = 0)
#svm.train(np.float32(obDataSet.attributes[obData.Training_indexes]),np.float32(obDataSet.labels[obData.Training_indexes]),None,None,obData.params)
#svm.save("asd.txt")
#results = svm.predict_all(np.float32(obDataSet.attributes[obData.Testing_indexes]),np.float32(obDataSet.labels[obData.Testing_indexes]))
#obData.set_results_from_classifier(results, obDataSet.labels[obData.Testing_indexes])
#obDataSet.append(obData)
#exp.add_data_set(obDataSet, description="Test for {}bits database: ".format(nbits))
#exp.save("file.txt")
#print exp
#print exp
#labelNames = [""]
oDataSet = DataSet()
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oDataSet.add_sample_of_attribute(np.array([1.1, 1, 1, 1, 1, 1, 'First_Class']))
oDataSet.add_sample_of_attribute(np.array([2.1, 2, 2, 2, 2, 2, 'Second_Class']))
oData = Data(2, 2, samples=6)
oData.random_training_test_by_percent(np.array([6, 6]), 0.50)
svm = ml.SVM_create()
svm = svm.create()
svm.trainAuto(np.float32(oDataSet.attributes[oData.Training_indexes]), ml.ROW_SAMPLE, np.int32(oDataSet.labels[oData.Training_indexes]), kFold=2)
oData.insert_model(svm)
print (oData.model)
results = []
for i in (oDataSet.attributes[oData.Testing_indexes]):
res,cls = svm.predict(np.float32([i]))
print (res, cls)
results.append(cls[0])
oData.set_results_from_classifier(results, oDataSet.labels[oData.Testing_indexes])
oDataSet.append(oData)
print(oData)