Exemple #1
0
def main():
	g = gd()
	patientv = [pinfo[1:] for pname, pinfo in g.REMISSED_PATIENTS.items()]
	print len(patientv[0])
	m =  model(len(patientv[0])-2)
	train_in = [x[:265] for x in patientv[:120]]
	train_out = [x[266] for x in patientv[:120]]
	train_set= zip(train_out,train_in)
	m.train(train_set,0.01)
	test = [x[:266] for x in patientv[120:]]
	for x in test:
		x = np.insert(x,0,1)
		print m.reg(x)
Exemple #2
0
def main():
    from read import getData as gd
    g = gd()

    tset = g.get_trainset(0.8, 265, vectorize=True)
    train_data = tset[0]
    test_data = tset[1]

    n = Network([265, 100, 100, 2])
    # train
    n.train(train_data, 2000, 20, 0.0001)
    for i in xrange(len(test_data)):
        print n.feed(test_data[i][1])
        print test_data[i][0]
Exemple #3
0
	def cost_derivative(self,x,y):
		return (y - self.reg(x))
	def cost_func(self,train_set):
		suma = []
		for y,x in train_set:
			x = np.insert(x,0,1)
			a = 1.0/2.0 *(y-self.reg(x))**2
			suma.append(a)
		sum = np.sum(suma)
		return 1.0/len(train_set) * sum
	def reg(self,x):
		output = np.dot(self.parameters.transpose(),x)
		#print output
		return output

from read import getData as gd
if __name__ =='__main__':
	g = gd(scale=True)
	train_data = g.get_trainset(0.8,266)
	train_set = train_data[0]
	test_set = train_data[1]
	n = RegModel(266)
	y = test_set[0][1]
	y = np.insert(y,0,1)
	n.train(train_set,0.000001,1000)
	x = test_set[0][0]
	print n.reg(y)
	print x
	with open("parameters.txt",'w') as f:
		f.write(np.array_str(n.parameters))