INNER CODE UNIT · Python

y_pred

ankonzoid/LearningX · basic_ML/classification/knn.py:22

        y_pred = -1 * np.zeros(len(X), dtype=int)
        for i in range(N):
            neg_distances = -np.array([np.linalg.norm(X[i] - x_train_j) for x_train_j in self.X_train])
            idx_topk = neg_distances.argsort()[-self.k:][::-1]  # top-k samples in negative distance
            nearby_classes = list(self.y_train[idx_topk])
            y_pred[i] = int(max(set(nearby_classes), key=nearby_classes.count))  # predict modal class
        return y_pred

# Main driver
if __name__ == "__main__":
    
    def load_csv_data(data_csv_filename, mode="clf", verbose=False):
        if verbose:
            print("Loading data from '{}' (mode={})...".format(data_csv_filename, mode))
        df = pd.read_csv(data_csv_filename)  # dataframe
        df_header = df.columns.values  # header
        header = list(df_header)
        N, d = len(df), len(df_header) - 1

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