def predict(self, x):
if hasattr(self, "scaler"):
x = self.scaler.transform(x)
x = add_bias_feature(x)
return [self.predict_single_(x_) for x_ in x]
def fit(self, x, y, plot_cost=False):
if hasattr(self, "scaler"):
x = self.scaler.fit_transform(x)
x = add_bias_feature(x)
# one-vs-all
for class_type in set(y):
cur_y = np.ones(len(y))
cur_y[y != class_type] = 0
self.class_data[class_type] = {"y": cur_y}
gd = GradientDescent(mode='logistic',
track_cost=plot_cost,
learning_rate=self.learning_rate,
penalty=self.penalty,
alpha=self.alpha,
max_iterations=self.max_iterations)
cur_theta = gd.find_theta(x, cur_y)
self.class_data[class_type] = {"theta": cur_theta}
if plot_cost:
cost_x, cost_y = gd.get_cost_history()
plt.plot(cost_x, cost_y, "r-")
plt.title(F"Cost for class '{class_type}' (last value={gd.last_cost:0.6f})")
plt.show()
def predict(self, x):
if self.scale_data:
x = self._scaler.transform(x)
x = add_bias_feature(x)
return np.array([self.predict_single_(x_) for x_ in x])
def fit(self, x, y, plot_cost=False):
x, y = check_X_y(x, y)
if hasattr(self, "scaler"):
x = self.scaler.fit_transform(x)
x = add_bias_feature(x)
gd = GradientDescent(mode='linear',
track_cost=plot_cost,
learning_rate=self.learning_rate,
penalty=self.penalty,
alpha=self.alpha,
max_iterations=self.max_iterations)
theta = gd.find_theta(x, y)
if plot_cost:
cost_x, cost_y = gd.get_cost_history()
plt.plot(cost_x, cost_y, "r-")
plt.title(F"Cost (last value={gd.last_cost:0.6f})")
plt.show()
self._theta = theta
self.intercept_ = theta[0, 0]
self.coef_ = theta[1:].reshape(len(theta) - 1)
check_is_fitted(self, attributes=['intercept_', 'coef_'])
return self
def predict(self, x):
x = check_array(x)
if hasattr(self, "scaler"):
x = self.scaler.transform(x)
x = add_bias_feature(x)
return h(x, self._theta).reshape(len(x))
def predict_proba(self, x):
if hasattr(self, "scaler"):
x = self.scaler.transform(x)
x = add_bias_feature(x)
p = np.empty((len(x), len(self.class_data)))
for i in range(len(x)):
x_ = x[i]
p[i, :] = [h(x_, self.class_data[c]["theta"])[0] for c in self.class_data]
return p
def fit(self, x, y, plot_cost=False):
x, y = check_X_y(x, y)
if self.scale_data:
self._scaler = StandardScaler()
x = self._scaler.fit_transform(x)
x = add_bias_feature(x)
# one-vs-all
for class_type in set(y):
cur_y = np.ones(len(y))
cur_y[y != class_type] = 0
self._class_data[class_type] = {"y": cur_y}
gd = GradientDescent(mode='logistic',
track_cost=plot_cost,
learning_rate=self.learning_rate,
penalty=self.penalty,
alpha=self.alpha,
max_iterations=self.max_iterations)
cur_theta = gd.find_theta(x, cur_y)
self._class_data[class_type] = {"theta": cur_theta}
if plot_cost:
cost_x, cost_y = gd.get_cost_history()
plt.plot(cost_x, cost_y, "r-")
plt.title(F"Cost for class '{class_type}' (last value={gd.last_cost:0.6f})")
plt.show()
coef = [self._class_data[key]["theta"][1:, 0] for key in self._class_data]
intercept = [self._class_data[key]["theta"][0, 0] for key in self._class_data]
self.coef_ = np.array(coef[1:]) if len(coef) == 2 else np.array(coef)
self.intercept_ = np.array(intercept[1:]) if len(intercept) == 2 else np.array(intercept)
check_is_fitted(self, attributes=['intercept_', 'coef_'])
return self