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'))
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
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()