n_trees = 200
ploty = [-6, 6, 100]
plotx = [-6, 6, 100]
X, Y = make_spiral(n_arms=n_classes, noise=.4)
##############################################################################
parameters = {'kernel': ['rbf'],
'C': [1, 10, 100, 1000, 10000, 100000],
'gamma': [10 ** x for x in range(-5, 3)],
'probability':[True]}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())
svmp = probaproxy(clf.predict_proba)
plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)
plt.title('RBF kernel SVM $(\gamma=%d, C=%f)$' % (clf.best_params_['gamma'], clf.best_params_['C']))
plt.savefig('spiral_svm.png')
plt.show()
##############################################################################
parameters = {'C': [1, 10, 100, 1000, 10000],
'probability': [True]}
svr = svm.SVC(kernel='linear', max_iter=20000)
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())
svmp = probaproxy(clf.predict_proba)
h = SimpleNode(sx, 2, 4)
h2 = SimpleNode(h, 4, 2)
out = SimpleNode(h2, 2, 1, nlin=pynnet.nlins.sigmoid)
cost = errors.mse(out, sy)
theano.config.blas.ldflags = ''
eval = theano.function([sx], out.output)
test = theano.function([sx, sy], cost.output)
train = theano.function([sx, sy],
cost.output,
updates=get_updates(cost.params, cost.output, 0.01))
print("Error at start:", test(X, Y))
for i in range(200000):
train(X, Y)
print("Error after 200000:", test(X, Y))
def pfunc(x):
return 1. - eval(x)
clp = probaproxy(pfunc)
plt.figure()
point_prob_plot(clp, X, Y, plotx, ploty)
plt.title('ANN (2 hidden layers, 4, 2)')
plt.savefig('spiral_ann.png')
plt.show()
plotx = [-6, 6, 100]
X, Y = make_spiral(n_arms=n_classes, noise=.4)
##############################################################################
parameters = {
'kernel': ['rbf'],
'C': [1, 10, 100, 1000, 10000, 100000],
'gamma': [10**x for x in range(-5, 3)],
'probability': [True]
}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())
svmp = probaproxy(clf.predict_proba)
plt.figure()
point_prob_plot(svmp, X, Y, plotx, ploty)
plt.title('RBF kernel SVM $(\gamma=%d, C=%f)$' %
(clf.best_params_['gamma'], clf.best_params_['C']))
plt.savefig('spiral_svm.png')
plt.show()
##############################################################################
parameters = {'C': [1, 10, 100, 1000, 10000], 'probability': [True]}
svr = svm.SVC(kernel='linear', max_iter=20000)
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(X, Y.ravel())
svmp = probaproxy(clf.predict_proba)
#%% Build ANN.
sx = theano.tensor.matrix('x')
sy = theano.tensor.matrix('y')
h = SimpleNode(sx, 2, 4)
h2 = SimpleNode(h, 4, 2)
out = SimpleNode(h2, 2, 1, nlin=pynnet.nlins.sigmoid)
cost = errors.mse(out, sy)
theano.config.blas.ldflags=''
eval = theano.function([sx], out.output)
test = theano.function([sx, sy], cost.output)
train = theano.function([sx, sy], cost.output,
updates=get_updates(cost.params, cost.output, 0.01))
print("Error at start:", test(X, Y))
for i in range(200000):
train(X, Y)
print("Error after 200000:", test(X, Y))
def pfunc(x):
return 1. - eval(x)
clp = probaproxy(pfunc)
plt.figure()
point_prob_plot(clp, X, Y, plotx, ploty)
plt.title('ANN (2 hidden layers, 4, 2)')
plt.savefig('spiral_ann.png')
plt.show()
