def fit(self, y_train): """Finds most common element in y_train and saves that as result Args: y_train(list of obj): The target y values (parallel to X_train) The shape of y_train is n_train_samples """ self.result = myutils.findMostFrequent(y_train)
def predict(self, X_test): """Makes predictions for test instances in X_test. Args: X_test(list of list of obj): The list of testing samples The shape of X_test is (n_test_samples, n_features) Returns: y_predicted(list of obj): The predicted target y values (parallel to X_test) """ results = [] for instance in X_test: treeResults = [] for tree in self.best_M_trees: treeResults.append(self.tdidt_predict(self.header, tree, instance)) results.append(myutils.findMostFrequent(treeResults)) return results
def predict(self, X_test): """Makes predictions for test instances in X_test. Args: X_test(list of list of numeric vals): The list of testing samples The shape of X_test is (n_test_samples, n_features) Returns: y_predicted(list of obj): The predicted target y values (parallel to X_test) """ result = [] _, indices = self.kneighbors(X_test) for lis in indices: temp = [] for jj in lis: temp.append(self.y_train[jj]) result.append(myutils.findMostFrequent(temp)) return result