def main():
# # x, y = DataUtil.gen_xor(100, one_hot=False)
# x, y = DataUtil.gen_spin(20, 4, 2, 2, one_hot=False)
# # x, y = DataUtil.gen_two_clusters(n_dim=2, one_hot=False)
# y[y == 0] = -1
#
# svm = SKSVM()
# # svm = SKSVM(kernel="poly", degree=12)
# svm.fit(x, y)
# svm.estimate(x, y)
# svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm.support_)
#
# svm = SVM()
# _logs = [_log[0] for _log in svm.fit(x, y, metrics=["acc"])]
# svm.estimate(x, y)
# svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
(x_train,
y_train), (x_test,
y_test), *_ = DataUtil.get_dataset("mushroom",
"../_Data/mushroom.txt",
train_num=100,
quantize=True,
tar_idx=0)
y_train[y_train == 0] = -1
y_test[y_test == 0] = -1
svm = SKSVM()
svm.fit(x_train, y_train)
svm.estimate(x_train, y_train)
svm.estimate(x_test, y_test)
svm = SVM()
_logs = [
_log[0] for _log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
# svm.fit(x_train, y_train, p=12)
svm.estimate(x_train, y_train)
svm.estimate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(_logs)), _logs)
plt.show()
svm.show_timing_log()
def main():
# x, y = DataUtil.gen_xor(100, one_hot=False)
x, y = DataUtil.gen_spiral(20, 4, 2, 2, one_hot=False)
# x, y = DataUtil.gen_two_clusters(n_dim=2, one_hot=False)
y[y == 0] = -1
animation_params = {
"show": False,
"mp4": False,
"period": 50,
"dense": 400,
"draw_background": True
}
svm = SVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=600)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
svm = GDSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=10000)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
if TorchSVM is not None:
svm = TorchSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12)
svm.evaluate(x, y)
svm.visualize2d(x,
y,
padding=0.1,
dense=400,
emphasize=svm["alpha"] > 0)
svm = TFSVM()
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400)
svm = SKSVM()
# svm = SKSVM(kernel="poly", degree=12)
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm.support_)
(x_train,
y_train), (x_test,
y_test), *_ = DataUtil.get_dataset("mushroom",
"../_Data/mushroom.txt",
n_train=100,
quantize=True,
tar_idx=0)
y_train[y_train == 0] = -1
y_test[y_test == 0] = -1
svm = SKSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TFSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
if TorchSVM is not None:
svm = TorchSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TorchSVM()
logs = [
log[0] for log in svm.fit(x_train,
y_train,
metrics=["acc"],
x_test=x_test,
y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = SVM()
logs = [
log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = GDSVM()
logs = [
log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test)
]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm.show_timing_log()
def main():
# x, y = DataUtil.gen_xor(100, one_hot=False)
x, y = DataUtil.gen_spiral(20, 4, 2, 2, one_hot=False)
# x, y = DataUtil.gen_two_clusters(n_dim=2, one_hot=False)
y[y == 0] = -1
animation_params = {
"show": False, "mp4": False, "period": 50,
"dense": 400, "draw_background": True
}
svm = SVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=600)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
svm = GDSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12, epoch=10000)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
if TorchSVM is not None:
svm = TorchSVM(animation_params=animation_params)
svm.fit(x, y, kernel="poly", p=12)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm["alpha"] > 0)
svm = TFSVM()
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400)
svm = SKSVM()
# svm = SKSVM(kernel="poly", degree=12)
svm.fit(x, y)
svm.evaluate(x, y)
svm.visualize2d(x, y, padding=0.1, dense=400, emphasize=svm.support_)
(x_train, y_train), (x_test, y_test), *_ = DataUtil.get_dataset(
"mushroom", "../_Data/mushroom.txt", n_train=100, quantize=True, tar_idx=0)
y_train[y_train == 0] = -1
y_test[y_test == 0] = -1
svm = SKSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TFSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
if TorchSVM is not None:
svm = TorchSVM()
svm.fit(x_train, y_train)
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
svm = TorchSVM()
logs = [log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test
)]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = SVM()
logs = [log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test
)]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm = GDSVM()
logs = [log[0] for log in svm.fit(
x_train, y_train, metrics=["acc"], x_test=x_test, y_test=y_test
)]
svm.evaluate(x_train, y_train)
svm.evaluate(x_test, y_test)
plt.figure()
plt.title(svm.title)
plt.plot(range(len(logs)), logs)
plt.show()
svm.show_timing_log()