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