def KRR():
np.random.seed(10)
targetValues = np.genfromtxt("F:\PY\data\stock.txt", skip_header=1, dtype=None, delimiter="\t", usecols=(1))
mse = {}
for i in range(20, 50):
trainingPoints = np.arange(i - 1).reshape(-1, 1)
testPoints = np.arange(i).reshape(-1, 1)
# training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
# testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
err = 0
for j in range(1, 6):
knlRidge.learn(knl, targetValues[-(i + j - 1) : -j])
resultPoints = knlRidge.pred(knlTest)
err += (resultPoints[-1] - targetValues[-j]) ** 2
print i, resultPoints[-1], targetValues[-j]
mse[i] = square(err / 5)
print mse[i]
sortedMse = sorted(mse.iteritems(), key=operator.itemgetter(1), reverse=False)
print sortedMse[0]
def KRRTest():
np.random.seed(10)
targetValues = np.genfromtxt('F:\PY\data\stock.txt',
skip_header=1,
dtype=None,
delimiter='\t',
usecols=(1))
trainingPoints = np.arange(46).reshape(-1, 1)
testPoints = np.arange(47).reshape(-1, 1)
#training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
#testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues[-46:])
resultPoints = knlRidge.pred(knlTest)
print targetValues.size, resultPoints
fig = plt.figure(1)
plot1 = plt.plot(trainingPoints, targetValues[-46:], 'o')
plot2 = plt.plot(testPoints, resultPoints)
plt.show()
def KRR():
np.random.seed(10)
targetValues = np.genfromtxt('F:\PY\data\stock.txt',
skip_header=1,
dtype=None,
delimiter='\t',
usecols=(1))
mse = {}
for i in range(20, 50):
trainingPoints = np.arange(i - 1).reshape(-1, 1)
testPoints = np.arange(i).reshape(-1, 1)
#training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
#testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
err = 0
for j in range(1, 6):
knlRidge.learn(knl, targetValues[-(i + j - 1):-j])
resultPoints = knlRidge.pred(knlTest)
err += (resultPoints[-1] - targetValues[-j])**2
print i, resultPoints[-1], targetValues[-j]
mse[i] = square(err / 5)
print mse[i]
sortedMse = sorted(mse.iteritems(),
key=operator.itemgetter(1),
reverse=False)
print sortedMse[0]
def KRR():
np.random.seed(10)
targetValues = np.genfromtxt('F:\PY\data\Gold.csv',
skip_header=1,
dtype=None,
delimiter=',',
usecols=(1))
trainingPoints = np.arange(125).reshape(-1, 1)
testPoints = np.arange(126).reshape(-1, 1)
#training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
#testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print(resultPoints)
fig = plt.figure(1)
plot1 = plt.plot(trainingPoints, targetValues, 'o')
plot2 = plt.plot(testPoints, resultPoints)
plt.show()
def KRRProTy():
np.random.seed(10)
targetValues = np.genfromtxt("F:\PY\data\stockPT.txt", skip_header=1, dtype=None, delimiter="\t", usecols=(1))
trainingPoints = np.arange(targetValues.size).reshape(-1, 1)
testPoints = np.arange(targetValues.size + 1).reshape(-1, 1)
# training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
# testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print targetValues.size, resultPoints
fig = plt.figure(1)
plot1 = plt.plot(trainingPoints, targetValues, "o")
plot2 = plt.plot(testPoints, resultPoints)
plt.show()
from mlpy import KernelRidge
import matplotlib.pyplot as plt
np.random.seed(10)
targetValues = np.genfromtxt("Gold.csv",
skip_header=1,
dtype=None,
delimiter=',',
usecols=(1))
trainingPoints = np.arange(125).reshape(-1, 1)
testPoints = np.arange(126).reshape(-1, 1)
#training kernel matrix
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
#testing kernel matrix
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print(resultPoints)
fig = plt.figure(1)
plot1 = plt.plot(trainingPoints, targetValues, 'o')
plot2 = plt.plot(testPoints, resultPoints)
plt.show()