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))
