def gradientDescentLinear(theta, data, learnRate=1.0):
counter = 0
# print("Gradient descent running!\n")
# print("Initial theta:\n{}".format(theta.getData()))
while hL.costFunction(theta, data) > 0.0001:
tmpTheta = list()
for index, t in enumerate(theta.getData()):
tmpTheta.append(gradientDescentLinearIteration(theta, data,
index, learnRate))
theta.read(tmpTheta)
#print("Theta after {0} iterations: \n{1}".format(counter,
#theta.getData()))
counter += 1
if counter > 10000:
break
return theta
for item in data.getData():
x=item.getX()
y=item.getY()
X.append(x[1])
Y.append(y)
print(x,y)
theta = th.theta(dimension=2)
theta.printData()
ex1 = data.getExample(0)
index = 0
print("Linear hypothesis value:\n{}".format(hL.hypothesisLinearTrain(theta,
ex1)))
print("Simple cost for hypothesis:\n{}".format(hL.cost(theta, ex1)))
print("Number of examples:\n{}".format(data.getNumberOfExamples()))
print("Total cost for hypothesis:\n{}".format(hL.costFunction(theta, data)))
print("Cost function derivative for index {0}:\n{1}\
".format(index, hL.costFunctionDerivative(theta, data, index)))
theta = grad.gradientDescentLinear(theta, data, 0.01)
print(theta.getData())
t = theta.getData()
a = np.arange(min(X), max(X), 0.1)
b = list(map(lambda x: t[0]+t[1]*x, a ))
pylab.plot(X,Y,'o')
pylab.plot(a,b)
pylab.show()
