def main(): inputs = ReadCSV('./data/input.csv') outputs = ReadCSV('./data/output.csv') ids = make_list(outputs) x = [] y = {} for i in range(3, 24, 3): x.append(i) for hidden_layers in range( 1, 7): #change loop for plot also if you change here print 'Number of hidden layers', hidden_layers y[hidden_layers] = [] for i in x: print 'Number of training tuples', i y[hidden_layers].append( find_accuracy(i, ids, hidden_layers, inputs, outputs)) #print x #print y #Now plot the graph for i in range(1, 7): t = pylab.plot(x, y[i], label="Number of hidden layers " + str(i), linewidth=4, linestyle='-') t = pylab.xlabel("Number of training tuples") t = pylab.ylabel("Accuracy %") t = pylab.title( "Random Subset Sampling (Number of training samples vs Accuracy)") t = pylab.legend(loc='lower right') t = pylab.grid() pylab.show()
def main(): inputs = ReadCSV('./data/input.csv') outputs = ReadCSV('./data/output.csv') test_set = test.keys() train_set = [] for k in inputs.keys(): if k not in test_set: train_set.append(k) print "Number of training samples", len(train_set) print "Number of testing samples", len(test_set) net = buildNetwork(178, 6, 5) ds=SupervisedDataSet(178,5) for id in train_set: ds.addSample(inputs[id],outputs[id]) trainer = BackpropTrainer(net, ds, learningrate=0.001, momentum = 0.001) trainer.trainUntilConvergence(maxEpochs=1000, validationProportion = 0.5) for id in test_set: predicted = net.activate(inputs[id]) actual = outputs[id] print '-----------------------------' print test[id] print '-----------------------------' print 'Trait\t\tPredicted\tActual\tError' for i in range(0,5): error = abs(predicted[i] - actual[i])*100/4.0 print traits[i], '\t', predicted[i], '\t', actual[i], '\t', error,"%"
def main(): inputs = ReadCSV('./data/input.csv') outputs = ReadCSV('./data/output.csv') ids = make_list(outputs) x = [] y = {} for i in range(2, 11, 2): x.append(i) for hidden_layers in range( 1, 6): #change loop for plot also if you change here print 'Number of hidden layers', hidden_layers y[hidden_layers] = [] for i in x: print 'Number of folds', i y[hidden_layers].append( find_accuracy(i, ids, hidden_layers, inputs, outputs)) #print x #print y #Now plot the graph for i in range(1, 6): t = pylab.plot(x, y[i], label="Number of hidden layers " + str(i), linewidth=4, linestyle='-') t = pylab.xlabel("Number of folds") t = pylab.ylabel("Accuracy %") t = pylab.title("K-Fold cross validation (Number of folds vs Accuracy)") t = pylab.legend(loc='lower right') t = pylab.grid() pylab.show()
def main(): inputs = ReadCSV('./data/input.csv') outputs = ReadCSV('./data/output.csv') ids = make_list(outputs) net = constructNet(ids, inputs, outputs) pickle.dump(net, open('neuralNet.sl', 'w'))
def connect(self): if (self.csv_loaded == False): self.plotWidget.clear() line = 1 rfile = ReadCSV.read("test.csv") for row in rfile: if (line == 1): line = line + 1 continue self.upload_data.append(row) Fixer.fixTimeAndAlt(self.upload_data) buff = Buffer() if os.path.exists('data.txt'): os.remove('data.txt') for i in self.upload_data: buff.sendToBuffer(i) buff.sendData() self.data = (DataLoader.read('data.txt')) self.csv_loaded = True self.infocsv.setText(_fromUtf8("Zaladowano")) self.info.setText("") self.wys = self.kat = self.dyst = self.pred = self.odchyl = 0
def connect(self): if (self.csv_loaded == False): self.plotWidget.clear() line = 1 rfile = ReadCSV.read("test.csv") for row in rfile: if(line == 1): line = line+1 continue self.upload_data.append(row) Fixer.fixTimeAndAlt(self.upload_data) buff = Buffer() if os.path.exists('data.txt'): os.remove('data.txt') for i in self.upload_data: buff.sendToBuffer(i) buff.sendData() self.data = (DataLoader.read('data.txt')) self.csv_loaded = True self.infocsv.setText(_fromUtf8("Zaladowano")) self.info.setText("") self.wys = self.kat = self.dyst = self.pred = self.odchyl = 0
def model_train(Save = False, modelname = None): X1_left = ReadCSV('datasets_csv/feature_0630_1700/X1_offline_0630_1700.csv','float64') X1_midle = ReadCSV('datasets_csv/feature_0701_1100/X1_offline_0701_1100.csv','float64') X1_right = ReadCSV('datasets_csv/feature_0701_1400/X1_offline_0701_1400.csv','float64') Y1 = ReadCSV('datasets_csv/feature_0701_1100/Y1_offline_0701_1100.csv','float64') X1 = np.c_[X1_left,X1_midle,X1_right] # X1 = np.c_[X1_left] X1 = list(X1) Y1 = list(Y1) X3_left = ReadCSV('datasets_csv/feature_0630_1700/X3_offline_0630_1700.csv','float64') X3_midle = ReadCSV('datasets_csv/feature_0701_1100/X3_offline_0701_1100.csv','float64') X3_right = ReadCSV('datasets_csv/feature_0701_1400/X3_offline_0701_1400.csv','float64') Y3 = ReadCSV('datasets_csv/feature_0701_1100/Y3_offline_0701_1100.csv','float64') X3 = np.c_[X3_left,X3_midle,X3_right] # X3 = np.c_[X3_left] X3 = list(X3) Y3 = list(Y3) X1.extend(X3) Y1.extend(Y3) del X3,Y3 X4_left = ReadCSV('datasets_csv/feature_0630_1700/X4_offline_0630_1700.csv','float64') X4_midle = ReadCSV('datasets_csv/feature_0701_1100/X4_offline_0701_1100.csv','float64') X4_right = ReadCSV('datasets_csv/feature_0701_1400/X4_offline_0701_1400.csv','float64') Y4 = ReadCSV('datasets_csv/feature_0701_1100/Y4_offline_0701_1100.csv','float64') X4 = np.c_[X4_left,X4_midle,X4_right] # X4 = np.c_[X4_left] X4 = list(X4) Y4 = list(Y4) X1.extend(X4) Y1.extend(Y4) del X4,Y4 X5_left = ReadCSV('datasets_csv/feature_0630_1700/X5_offline_0630_1700.csv','float64') X5_midle = ReadCSV('datasets_csv/feature_0701_1100/X5_offline_0701_1100.csv','float64') X5_right = ReadCSV('datasets_csv/feature_0701_1400/X5_offline_0701_1400.csv','float64') Y5 = ReadCSV('datasets_csv/feature_0701_1100/Y5_offline_0701_1100.csv','float64') X5 = np.c_[X5_left,X5_midle,X5_right] X5 = list(X5) Y5 = list(Y5) X1.extend(X5) Y1.extend(Y5) del X5,Y5 X6_left = ReadCSV('datasets_csv/feature_0630_1700/X6_offline_0630_1700.csv','float64') X6_midle = ReadCSV('datasets_csv/feature_0701_1100/X6_offline_0701_1100.csv','float64') X6_right = ReadCSV('datasets_csv/feature_0701_1400/X6_offline_0701_1400.csv','float64') Y6 = ReadCSV('datasets_csv/feature_0701_1100/Y6_offline_0701_1100.csv','float64') X6 = np.c_[X6_left,X6_midle,X6_right] X6 = list(X6) Y6 = list(Y6) X1.extend(X6) Y1.extend(Y6) del X6,Y6 X7_left = ReadCSV('datasets_csv/feature_0630_1700/X7_offline_0630_1700.csv','float64') X7_midle = ReadCSV('datasets_csv/feature_0701_1100/X7_offline_0701_1100.csv','float64') X7_right = ReadCSV('datasets_csv/feature_0701_1400/X7_offline_0701_1400.csv','float64') Y7 = ReadCSV('datasets_csv/feature_0701_1100/Y7_offline_0701_1100.csv','float64') X7 = np.c_[X7_left,X7_midle,X7_right] X7 = list(X7) Y7 = list(Y7) X1.extend(X7) Y1.extend(Y7) del X7,Y7 X8_left = ReadCSV('datasets_csv/feature_0630_1700/X8_offline_0630_1700.csv','float64') X8_midle = ReadCSV('datasets_csv/feature_0701_1100/X8_offline_0701_1100.csv','float64') X8_right = ReadCSV('datasets_csv/feature_0701_1400/X8_offline_0701_1400.csv','float64') Y8 = ReadCSV('datasets_csv/feature_0701_1100/Y8_offline_0701_1100.csv','float64') X8 = np.c_[X8_left,X8_midle,X8_right] X8 = list(X8) Y8 = list(Y8) X1.extend(X8) Y1.extend(Y8) del X8,Y8 X1 = np.array(X1) Y1 = np.array(Y1) X1,Y1 = DowmSample(X1,Y1,1) model = RandomForestClassifier(n_estimators=100,random_state=1) # model = GradientBoostingClassifier(n_estimators=100,max_leaf_nodes=5, subsample=0.8, random_state=1) # model = LogisticRegression('l2') # model = RVC() y0 = [] for y in Y1: y0.append(y[0]) y1 = np.array(y0) # y1 = y0.reshape(1,len(y0)) print 'size of X1',np.shape(X1) print 'size of y1',np.shape(y1) model.fit(X1, y1.ravel()) model.fit(X1, y1) # 保存模型 if Save == True: f = open(modelname,'w') pickle.dump(model, f) f.close() print '\n -------------- Training is over ----------------------' return model
import sys sys.path.append('./lib') from ReadCSV import ReadCSV print ReadCSV("./data/input.csv")
from ReadCSV import ReadCSV rs = ReadCSV( '/home/dgrfi/MEGA/supersymmetry/7TeVxyz.csv').read_matrix_in_dataframe()
from pybrain.tools.shortcuts import buildNetwork from pybrain.datasets import SupervisedDataSet from pybrain.structure import TanhLayer from pybrain.supervised.trainers import BackpropTrainer import pickle import sys sys.path.append('./lib') from ReadCSV import ReadCSV new_input = ReadCSV("newinput.csv") net = pickle.load(open("neuralNet.sl", "r")) input = new_input[new_input.keys()[0]] traits = net.activate(input) text = "" for trait in traits: text = text + str(trait) + " " print text
def createSample(self): sum_w8 = self.w.sum_weights(self.w.weights) #print(sum_w8) rd = ReadCSV() sample = rd.create_sampleList(self.sample_limit, self.db, sum_w8) return sample
from ReadCSV import ReadCSV from Buffer import Buffer from DataLoader import DataLoader from Fixer import Fixer from Plots import Plots import os if os.path.exists('data.txt'): os.remove('data.txt') upload_data = [] data = [] line = 1 rfile = ReadCSV.read('test.csv') for row in rfile: if(line == 1): line = line+1 continue upload_data.append(row) Fixer.fixTimeAndAlt(upload_data) buff = Buffer() for i in upload_data: buff.sendToBuffer(i) buff.sendData() data = (DataLoader.read('data.txt')) plots = Plots() plots.altitude(data) plots.pitch(data) plots.distance(data) plots.speed(data)
def model_train(Save = False, modelname = None): X1_left = ReadCSV('datasets_csv/feature_0630_1700/X1_offline_0630_1700.csv','float64') X1_midle = ReadCSV('datasets_csv/feature_0701_1100/X1_offline_0701_1100.csv','float64') X1_right = ReadCSV('datasets_csv/feature_0701_1400/X1_offline_0701_1400.csv','float64') Y1 = ReadCSV('datasets_csv/feature_0701_1100/Y1_offline_0701_1100.csv','float64') X1 = np.c_[X1_left,X1_midle,X1_right] # X1 = np.c_[X1_left] X1 = list(X1) Y1 = list(Y1) X2_left = ReadCSV('datasets_csv/feature_0630_1700/X2_offline_0630_1700.csv','float64') X2_midle = ReadCSV('datasets_csv/feature_0701_1100/X2_offline_0701_1100.csv','float64') X2_right = ReadCSV('datasets_csv/feature_0701_1400/X2_offline_0701_1400.csv','float64') Y2 = ReadCSV('datasets_csv/feature_0701_1100/Y2_offline_0701_1100.csv','float64') X2 = np.c_[X2_left,X2_midle,X2_right] # X2 = np.c_[X2_left] X2 = list(X2) Y2 = list(Y2) X1.extend(X2) Y1.extend(Y2) del X2,Y2 X3_left = ReadCSV('datasets_csv/feature_0630_1700/X3_offline_0630_1700.csv','float64') X3_midle = ReadCSV('datasets_csv/feature_0701_1100/X3_offline_0701_1100.csv','float64') X3_right = ReadCSV('datasets_csv/feature_0701_1400/X3_offline_0701_1400.csv','float64') Y3 = ReadCSV('datasets_csv/feature_0701_1100/Y3_offline_0701_1100.csv','float64') X3 = np.c_[X3_left,X3_midle,X3_right] # X3 = np.c_[X3_left] X3 = list(X3) Y3 = list(Y3) X1.extend(X3) Y1.extend(Y3) del X3,Y3 X4_left = ReadCSV('datasets_csv/feature_0630_1700/X4_offline_0630_1700.csv','float64') X4_midle = ReadCSV('datasets_csv/feature_0701_1100/X4_offline_0701_1100.csv','float64') X4_right = ReadCSV('datasets_csv/feature_0701_1400/X4_offline_0701_1400.csv','float64') Y4 = ReadCSV('datasets_csv/feature_0701_1100/Y4_offline_0701_1100.csv','float64') X4 = np.c_[X4_left,X4_midle,X4_right] # X4 = np.c_[X4_left] X4 = list(X4) Y4 = list(Y4) X1.extend(X4) Y1.extend(Y4) del X4,Y4 X5_left = ReadCSV('datasets_csv/feature_0630_1700/X5_offline_0630_1700.csv','float64') X5_midle = ReadCSV('datasets_csv/feature_0701_1100/X5_offline_0701_1100.csv','float64') X5_right = ReadCSV('datasets_csv/feature_0701_1400/X5_offline_0701_1400.csv','float64') Y5 = ReadCSV('datasets_csv/feature_0701_1100/Y5_offline_0701_1100.csv','float64') X5 = np.c_[X5_left,X5_midle,X5_right] X5 = list(X5) Y5 = list(Y5) X1.extend(X5) Y1.extend(Y5) del X5,Y5 X6_left = ReadCSV('datasets_csv/feature_0630_1700/X6_offline_0630_1700.csv','float64') X6_midle = ReadCSV('datasets_csv/feature_0701_1100/X6_offline_0701_1100.csv','float64') X6_right = ReadCSV('datasets_csv/feature_0701_1400/X6_offline_0701_1400.csv','float64') Y6 = ReadCSV('datasets_csv/feature_0701_1100/Y6_offline_0701_1100.csv','float64') X6 = np.c_[X6_left,X6_midle,X6_right] X6 = list(X6) Y6 = list(Y6) X1.extend(X6) Y1.extend(Y6) del X6,Y6 X7_left = ReadCSV('datasets_csv/feature_0630_1700/X7_offline_0630_1700.csv','float64') X7_midle = ReadCSV('datasets_csv/feature_0701_1100/X7_offline_0701_1100.csv','float64') X7_right = ReadCSV('datasets_csv/feature_0701_1400/X7_offline_0701_1400.csv','float64') Y7 = ReadCSV('datasets_csv/feature_0701_1100/Y7_offline_0701_1100.csv','float64') X7 = np.c_[X7_left,X7_midle,X7_right] X7 = list(X7) Y7 = list(Y7) X1.extend(X7) Y1.extend(Y7) del X7,Y7 X8_left = ReadCSV('datasets_csv/feature_0630_1700/X8_offline_0630_1700.csv','float64') X8_midle = ReadCSV('datasets_csv/feature_0701_1100/X8_offline_0701_1100.csv','float64') X8_right = ReadCSV('datasets_csv/feature_0701_1400/X8_offline_0701_1400.csv','float64') Y8 = ReadCSV('datasets_csv/feature_0701_1100/Y8_offline_0701_1100.csv','float64') X8 = np.c_[X8_left,X8_midle,X8_right] X8 = list(X8) Y8 = list(Y8) X1.extend(X8) Y1.extend(Y8) del X8,Y8 X1 = np.array(X1) Y1 = np.array(Y1) X1,Y1 = DowmSample(X1,Y1,8) model = GradientBoostingClassifier(n_estimators=100,max_leaf_nodes=5, learning_rate=0.1, subsample=0.8, random_state=1) model.fit(X1, Y1.ravel()) # 保存模型 if Save == True: f = open(modelname,'w') pickle.dump(model, f) f.close() print '\n ------------- Training is over ----------------------' return model
accuracies = [] for i in range(self.turns): print(" turn ", i) validationSet = self.create_validation_set(div_db, i) #print(len(validationSet)) adaboost_set = [] for j in range(self.turns): if (j != i): adaboost_set += div_db[j] adaboost_set = [self.attr_list] + adaboost_set fscr, accuracy_per_turn = self.fscr_per_turn( adaboost_set, validationSet) #print(fscr) f1Scores.append(fscr) accuracies.append(accuracy_per_turn) #print(len(f1Scores)) fscore = sum(f1Scores) / float(self.turns) acc_score = sum(accuracies) / float(self.turns) print("the f1 score of simulation: ", fscore) print("the accuracy of simulation: ", acc_score) ################################################################################## rd = ReadCSV() db_1 = rd.produceDB() k = KFoldCross(db_1, 5) #print(k.block_size) k.validation()