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I have finish modifying the GUI, added k input option.

The single/multiple parameters with automatic/manual splicing method works

GUI/Learn.py

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+"""
+CS501 Group 16
+Fall 2018
+Modified/adapted from code baseline by Thomas Shaw
+
+This module imports the data and executes the algorithms.
+
+Re-used code from: https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping
+credit: Mark Dregan
+
+*Some modifications made for implementation in Python3 and custom data input, plotting
+
+"""
+def learn(filename):
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    from knndtw import KnnDtw
+    from knndtw import ProgressBar
+
+    # dataparam = 'mavlink_attitude_t_yaw angle'
+    dataparam = filename
+
+    trainingdatafile = 'Data/train_' + dataparam + '.txt'
+    traininglabelfile = 'Data/train_labels.txt'
+
+    # testdatafile = 'Data/test_' + dataparam + '.txt'
+    # testlabelfile = 'Data/test_labels.txt'
+
+    # Import the HAR dataset
+    x_train_file = open(trainingdatafile, 'r')
+    y_train_file = open(traininglabelfile, 'r')
+
+    # x_test_file = open(testdatafile, 'r')
+    # y_test_file = open(testlabelfile, 'r')
+
+    # Create empty lists
+    x_train = []
+    y_train = []
+    # x_test = []
+    # y_test = []
+
+    # Mapping table for classes
+    labels = {1: 'Hover', 2: 'Impact (tapping)', 3: 'Wind'}
+
+    # Loop through datasets
+    for x in x_train_file:
+        x_train.append([float(ts) for ts in x.split()])
+
+    for y in y_train_file:
+        y_train.append(int(y.rstrip('\n')))
+
+    # for x in x_test_file:
+    #     x_test.append([float(ts) for ts in x.split()])
+    #
+    # for y in y_test_file:
+    #     y_test.append(int(y.rstrip('\n')))
+
+    # close data files
+    x_train_file.close()
+    y_train_file.close()
+    # x_test_file.close()
+    # y_test_file.close()
+
+    # Convert to numpy for efficiency
+    x_train = np.array(x_train)
+    y_train = np.array(y_train)
+    # x_test = np.array(x_test)
+    # y_test = np.array(y_test)
+
+    # Analyze dataset
+    m = KnnDtw(n_neighbors=1, max_warping_window=100)
+    m.fit(x_train, y_train)
+    # label, proba = m.predict(x_test)  Result
+
+    # Classification report
+    # from sklearn.metrics import classification_report, confusion_matrix
+    # print(classification_report(label, y_test,
+    #                             target_names=[l for l in labels.values()]))
+    #
+    # print(confusion_matrix(label, y_test))
+
+
+    def traning_data_plot():
+        # plot train data
+        plot1 = plt.figure(figsize=(11, 7))
+        colors = ['#D62728', '#2C9F2C', '#FD7F23', '#1F77B4', '#9467BD',
+                  '#8C564A', '#7F7F7F', '#1FBECF', '#E377C2', '#BCBD27',
+                  '#D62728', '#2C9F2C']
+        for i, r in enumerate([0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]):
+            plt.subplot(7, 2, i + 1)
+            plt.plot(x_train[r], label=labels[y_train[r]], color=colors[i], linewidth=2)
+            plt.xlabel('Samples @50Hz')
+            plt.legend(loc='upper left')
+            plt.tight_layout()
+        plt.show()
+    return x_train,y_train
+
+
+    # print (x_train)
+    # print (y_train)
+    # traning_data_plot()
+
+    # def test_data_plot():
+    #     # Plot Test data
+    #     plot2 = plt.figure(figsize=(11, 7))
+    #     colors = ['#D62728', '#2C9F2C', '#FD7F23', '#1F77B4', '#9467BD',
+    #               '#8C564A', '#7F7F7F', '#1FBECF', '#E377C2', '#BCBD27']
+    #     for i, r in enumerate([0, 1, 2, 3, 4, 5]):
+    #         plt.subplot(3, 2, i + 1)
+    #         plt.plot(x_test[r], label=labels[y_test[r]], color=colors[i], linewidth=2)
+    #         plt.xlabel('Samples @50Hz')
+    #         plt.legend(loc='upper left')
+    #         plt.tight_layout()
+    #     plt.show()
+
+    # def confusion_matrix_plot():
+    #     # Confusion Matrix
+    #     conf_mat = confusion_matrix(label, y_test)
+    #
+    #     fig = plt.figure(figsize=(3, 3))
+    #     width = np.shape(conf_mat)[1]
+    #     height = np.shape(conf_mat)[0]
+    #
+    #     res = plt.imshow(np.array(conf_mat), cmap=plt.cm.summer, interpolation='nearest')
+    #     for i, row in enumerate(conf_mat):
+    #         for j, c in enumerate(row):
+    #             if c > 0:
+    #                 plt.text(j - .2, i + .1, c, fontsize=16)
+    #
+    #     plt.title('Confusion Matrix for ' + dataparam)
+    #     plt.xlabel('Data')
+    #     plt.ylabel('ML Identification')
+    #     _ = plt.xticks(range(3), [l for l in labels.values()], rotation=90)
+    #     _ = plt.yticks(range(3), [l for l in labels.values()])
+    #     plt.show()
+
+
+
+
+
+
+