def load_dataset(): import s_data_loader as data_loader # dt = data_loader.load_feature_time() dt = data_loader.load_feature() # Mapping table for classes labels = dt.labels x_train = dt.x_train y_train = dt.y_train x_test = dt.x_test y_test = dt.y_test skip_ratio = 1 rx_train = x_train[::skip_ratio] ry_train = y_train[::skip_ratio] rx_test = x_test[::skip_ratio] ry_test = y_test[::skip_ratio] return rx_train, ry_train, rx_test, ry_test, labels, skip_ratio
import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import classification_report, confusion_matrix from s_knn_dtw import KnnDtw import os, sys currentdir = os.path.dirname(os.path.realpath(__file__)) parentdir = os.path.dirname(currentdir) sys.path.append(parentdir) import s_data_loader as data_loader # dt = data_loader.load_feature_time() dt = data_loader.load_feature() # Mapping table for classes labels = dt.labels x_train = dt.x_train y_train = dt.y_train x_test = dt.x_test y_test = dt.y_test #n_neighbors = 1 n_neighbors = 2 #max_warping_window = 10 max_warping_window = 4 skip_ratio = 100 print("skip_ratio: {} n_neighors: {} max_waraping_window: {}".format(skip_ratio, n_neighbors, max_warping_window))