def SmoothKRR():
y = np.genfromtxt('F:\PY\data\stock.txt',
skip_header=1,
dtype=None,
delimiter='\t',
usecols=(1))
targetValues = smooth(y, len(y))
np.random.seed(10)
trainingPoints = np.arange(28).reshape(-1, 1)
testPoints = np.arange(29).reshape(-1, 1)
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = mlpy.KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print resultPoints
plt.step(trainingPoints, targetValues, 'o')
plt.step(testPoints, resultPoints)
plt.show()
def _q20():
import mlpy
X_train, y_train, X_test, y_test = load_dataset()
for gamma in [32, 2, 0.125]:
rbf = mlpy.KernelGaussian(sigma=(2 * gamma) ** 0.5)
for lamda in [0.001, 1, 1000]:
clf = mlpy.KernelRidge(lmb=lamda, kernel=rbf)
clf.learn(X_train, y_train)
err = 0.0
for i in xrange(len(X_test)):
if sign(clf.pred(X_test[i])) != y_test[i]:
err += 1.0
print gamma, lamda, err / len(X_test)
def f(TrainIn, TrainOut, TestIn):
print "init......"
x = numpy.array(TrainIn)
y = numpy.array(TrainOut)
t = numpy.array(TestIn)
print "learn......"
k = mlpy.kernel_gaussian(x, x, sigma=1)
kt = mlpy.kernel_gaussian(t, x, sigma=1)
krr = mlpy.KernelRidge(lmb=0.01)
krr.learn(k, y)
print "out......"
re = krr.pred(t)
return re
y = np.convolve(w / w.sum(), s, mode='same')
return y[window_len:-window_len + 1]
y = np.genfromtxt("Gold.csv",
skip_header=1,
dtype=None,
delimiter=',',
usecols=(1))
targetValues = smooth(y, len(y))
np.random.seed(10)
trainingPoints = np.arange(125).reshape(-1, 1)
testPoints = np.arange(126).reshape(-1, 1)
knl = mlpy.kernel_gaussian(trainingPoints, trainingPoints, sigma=1)
knlTest = mlpy.kernel_gaussian(testPoints, trainingPoints, sigma=1)
knlRidge = mlpy.KernelRidge(lmb=0.01, kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print(resultPoints)
plt.step(trainingPoints, targetValues, 'o')
plt.step(testPoints, resultPoints)
plt.show()
import numpy as np import matplotlib.pyplot as plt import mlpy np.random.seed(0) x = np.arange(0, 2, 0.05).reshape(-1, 1) # training points y = np.ravel(np.exp(x)) + np.random.normal(1, 0.2, x.shape[0]) # target values xt = np.arange(0, 2, 0.01).reshape(-1, 1) # testing points K = mlpy.kernel_gaussian(x, x, sigma=1) # training kernel matrix Kt = mlpy.kernel_gaussian(xt, x, sigma=1) # testing kernel matrix krr = mlpy.KernelRidge(lmb=0.01) krr.learn(K, y) yt = krr.pred(Kt) fig = plt.figure(1) plot1 = plt.plot(x[:, 0], y, 'o') plot2 = plt.plot(xt[:, 0], yt) plt.show()
source = []
target = []
for s in sentences:
words = []
nwords = []
for k in range(len(s) - 1):
words.append(lut[s[k]])
nwords.append(lut[s[k + 1]])
source.append(np.r_[words])
target.extend(np.r_[nwords])
for b in source[:10]:
reservoir.execute(b)
initial_state = reservoir.states[-1]
states = []
for k in range(len(source)):
reservoir.states = np.c_[initial_state].T
states.append(reservoir.execute(source[k]))
X = np.vstack(states)
import mlpy
K = mlpy.kernel_gaussian(X.T, X.T, sigma=1)
readout = mlpy.KernelRidge(lmb=0.01)
readout.learn(K, np.array(target)[:, 0])
kernel_distrib = readout.pred(X)
y = np.convolve(w / w.sum(), s, mode='same')
return y[window_len:-window_len + 1]
y = np.genfromtxt("/Users/hanzhao/PycharmProjects/MLstudy/file/Gold.csv",
skip_header=1,
dtype=None,
delimiter=',',
usecols=(1))
targetValues = smooth(y, len(y))
np.random.seed(10)
trainingPoints = np.arange(125).reshape(-1, 1)
testPoints = np.arange(126).reshape(-1, 1)
kg = KernelGaussian()
knl = kg.kernel(trainingPoints, trainingPoints)
knlTest = kg.kernel(testPoints, trainingPoints)
knlRidge = mlpy.KernelRidge(kernel=None)
knlRidge.learn(knl, targetValues)
resultPoints = knlRidge.pred(knlTest)
print(resultPoints)
plt.step(trainingPoints, targetValues, 'o')
plt.step(testPoints, resultPoints)
plt.show()