def main():
import dataset
X, y = dataset.digits()
X_train, X_test, y_train, y_test = testing.hold_out(X, y)
clf = NNRBF(q=1000)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))
def eval_classification(X, y, q=20):
X_train, X_test, y_train, y_test = testing.hold_out(X, y)
W, M = train(X_train, y_train, q=q, activation=processing.sigmoid)
D_teste = predict(X_test, W, M, activation=processing.sigmoid)
y_test = processing.encode_label(y_test)
y_pred = processing.encode_label(D_teste)
acc = round(testing.accuracy(y_test, y_pred), ndigits=2)
return acc
def main():
X, y = load.iris()
X_train, X_test, y_train, y_test = testing.hold_out(X, y)
W = train(X_train, y_train)
y_pred = predict(X_test, W)
y_test = processing.encode_label(y_test)
acc = testing.accuracy(y_test, y_pred)
print("ACC: {:.2f} %".format(acc * 100))
def eval_classification(X, y, q=Q):
X_train, X_test, y_train, y_test = testing.hold_out(X,
y,
test_size=TEST_SIZE)
T, G = train(X_train, y_train, q=q, activation=processing.sigmoid)
D_teste = predict(X_test, T, G, activation=processing.sigmoid)
y_test = processing.encode_label(y_test)
y_pred = processing.encode_label(D_teste)
acc = round(testing.accuracy(y_test, y_pred), ndigits=2)
return acc
def main():
# Train / test ELM
print("-- Extreme Learning Machine")
X, y = load.twomoons()
q = 10
(X_train, X_test, y_train, y_test) = testing.hold_out(X, y)
W, M = elm.train(X_train, y_train, q=10, activation=processing.sigmoid)
y_pred = elm.predict(X_test, W, M, activation=processing.sigmoid)
y_pred = step(y_pred)
acc = round(testing.accuracy(y_test, y_pred), ndigits=2)
acc_text = "ACC={:.2f}%".format(acc * 100)
print("X.shape: ", X.shape)
print("y.shape: ", y.shape)
print("W.shape:", W.shape)
print("M.shape: ", M.shape)
print(acc_text)
## SURFACE DECISON
x1, x2 = X[:, 0], X[:, 1]
x_min = min(min(x1), min(x2))
x_max = max(max(x1), max(x2))
n = 200
linspace = np.linspace(x_min, x_max, n)
points = []
for xi in linspace:
for xj in linspace:
points.append((xi, xj))
X_decision = np.array(points)
y_elm = elm.predict(X_decision, W, M, activation=processing.sigmoid)
x1_curve = []
x2_curve = []
for yi, p in zip(y_elm, points):
if abs(yi) < 0.05:
x1_curve.append(p[0])
x2_curve.append(p[1])
ax = plt.gca()
ax.set_title("Two Moons: ELM / {}".format(acc_text))
ax.set_xlabel("x1")
ax.set_ylabel("x2")
plt.scatter(x1_curve, x2_curve, c='k', s=1)
plt.scatter(x1, x2, c=colorize(y))
plt.show()
plt.close()