def fit(self, X, Y): [Y_new, self.old_to_new, self.new_to_old, num_class] = target_process.translate(Y) self.classifier = pysol.SOL('ogd', num_class, **self.params) self.classifier.fit(X, Y_new)
def fit(self, X, Y): [Y_new, self.old_to_new, self.new_to_old, num_class] = target_process.translate(Y) try: train_accuracy = self.classifier.fit(X, Y_new) return train_accuracy except AttributeError as e: self.classifier = pysol.SOL('stg', num_class, **self.params) train_accuracy = self.classifier.fit(X, Y_new) return train_accuracy
def fit(self, X, Y): num_class = len(set(Y)) try: return self.classifier.fit(X, Y) except AttributeError as e: self.classifier = pysol.SOL('ogd', num_class, **self.params) accuracy, update, data, iter, err, time = self.classifier.fit(X, Y) plt.figure plt.plot(data, err, "r*") plt.plot(data, err, label="test", color="red", linewidth=2) plt.xlabel("data") plt.ylabel("error") plt.legend() plt.show() return accuracy
def fit(self, X, Y): [Y_new, self.old_to_new, self.new_to_old, num_class] = target_process.translate(Y) try: train_accuracy, update, data, iter, err, time = self.classifier.fit( X, Y_new) self.drawFit(data, err, "data", "error", data, update, "data", "update", data, time, "data", "time", "Ogd") return train_accuracy except AttributeError as e: self.classifier = pysol.SOL('ogd', num_class, **self.params) train_accuracy, update, data, iter, err, time = self.classifier.fit( X, Y_new) self.drawFit(data, err, "data", "error", data, update, "data", "update", data, time, "data", "time", "Ogd") return train_accuracy
import pysol