Add LICENSE.txt with GPL-3.0-or-later license
This commit is contained in:
Lucas Oskorep
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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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of this license document, but changing it is not allowed.
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Preamble
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The GNU General Public License is a free, copyleft license for software.
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By contrast, the GPL guarantees your freedom to share and change all versions.
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To protect your rights, we need to prevent others from denying you these rights.
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Developers that use the GNU GPL protect your rights with two steps:
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(1) assert copyright on the software, and (2) offer you this License.
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Some devices are designed to deny users access to install or run modified versions.
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This is fundamentally incompatible with the aim of protecting users' freedom to change software.
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Finally, every program is threatened constantly by software patents.
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The GPL assures that patents cannot be used to render the program non-free.
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TERMS AND CONDITIONS
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0. Definitions.
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"This License" refers to version 3 of the GNU General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of works.
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"The Program" refers to any copyrightable work licensed under this License.
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1. Source Code.
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The "source code" for a work means the preferred form for making modifications.
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2. Basic Permissions.
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All rights granted under this License are granted for the term of copyright on the Program,
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and are irrevocable provided the stated conditions are met.
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3. Protecting Users' Legal Rights From Anti-Circumvention Law.
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No covered work shall be deemed part of an effective technological measure.
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4. Conveying Verbatim Copies.
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You may convey verbatim copies of the Program's source code as you receive it.
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5. Conveying Modified Source Versions.
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You may convey a work based on the Program under section 4, provided that you also meet all conditions:
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a) The work must carry prominent notices stating that you modified it.
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b) You must license the entire work under this License.
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6. Conveying Non-Source Forms.
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You may convey a covered work in object code form under sections 4 and 5, provided that you also convey
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the machine-readable Corresponding Source under the terms of this License.
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7. Additional Terms.
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"Additional permissions" are terms that supplement the terms of this License by making exceptions.
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8. Termination.
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You may not propagate or modify a covered work except as expressly provided under this License.
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9. Acceptance Not Required for Having Copies.
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You are not required to accept this License in order to receive or run a copy of the Program.
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10. Automatic Licensing of Downstream Recipients.
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Each time you convey a covered work, the recipient automatically receives a license from the original licensors.
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11. Patents.
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A "contributor" is a copyright holder who authorizes use under this License of the Program.
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12. No Surrender of Others' Freedom.
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If conditions are imposed on you that contradict the conditions of this License, they do not excuse you from the conditions.
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13. Use with the GNU Affero General Public License.
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You have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License.
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14. Revised Versions of this License.
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The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time.
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15. Disclaimer of Warranty.
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THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
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16. Limitation of Liability.
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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER BE LIABLE TO YOU FOR DAMAGES.
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17. Interpretation of Sections 15 and 16.
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If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms,
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reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability.
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END OF TERMS AND CONDITIONS
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How to Apply These Terms to Your New Programs
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If you develop a new program, and you want it to be of the greatest possible use to the public,
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the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
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To do so, attach the following notices to the program:
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<one line to give the program's name and a brief idea of what it does.>
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Copyright (C) <year> <name of author>
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
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========================================================================
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Copyright (C) YOUR NAME 2024
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Licensed under the GNU General Public License Version 3 (GPL-3.0-or-later).
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GPL v3 as published by the Free Software Foundation,
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either version 3 of the License, or (at your option) any later version.
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See <https://www.gnu.org/licenses/> for full license text.
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-231
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import numpy as np
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import pandas as pd
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from sklearn.cluster import KMeans
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from sklearn.metrics import confusion_matrix
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.naive_bayes import GaussianNB
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import matplotlib.pyplot as plt
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from sklearn import svm
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from pprint import pprint
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import os
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import seaborn as sn
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def normalized(a, axis=-1, order=2):
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return a / sum(a)
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def get_activities(dir):
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activity_data_train = {}
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activity_data_test = {}
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i= 0
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for root, dirs, files in os.walk(dir):
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activity_name = root.split('\\')[1]
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activity_data_train[activity_name] = []
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activity_data_test[activity_name] = []
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for i in range(int(.8 * len(files))):
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data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
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activity_data_train[activity_name].append(data)
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i+=1
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while i < len(files):
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data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
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activity_data_test[activity_name].append(data)
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i+=1
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return activity_data_train, activity_data_test
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def quantize_data(data, vector_len):
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del data[""]
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print("quantizing", data)
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print(vector_len)
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quantized_data = {}
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total = 0
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total2 = 0
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for key, value in data.items():
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print(key, len(value))
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quantized_data[key] = {}
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print(vector_len)
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for i in range(len(value)):
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total2+= len(value[i])/vector_len
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while(len(value[i]) %vector_len != 0):
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value[i] = value[i][:-1]
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print(len(value[i]), vector_len)
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new_data = np.split(value[i], len(value[i]) / vector_len)
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# if len(new_data[len(new_data) - 1]) != vector_len:
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# print(vector_len,len(new_data[len(new_data) - 1]))
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# del new_data[len(new_data) - 1]
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quantized_data[key][i] = new_data
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total+= len(new_data)
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print("Total is:", total, total2)
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return quantized_data
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lengths = [4]
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# for i in range(3,4):
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# lengths.append((i+1)*21)
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data_train, data_test = get_activities('data\\')
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print("----------------")
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X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
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# print(X)
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clusters =[1000]# [600,600,600,1000,1000,1000,1400,1400,1400]
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q_count = 0
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n_count = 0
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accuracies = []
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for quantize_length in lengths:
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accuracies.append([])
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for num_clusters in clusters:
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qdata_test = quantize_data(data_test, quantize_length)
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print("----------------")
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qdata_train = quantize_data(data_train, quantize_length)
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print("----------------")
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#fill kmeans_data with all of the data of all of the exercises reguardless of what they are
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k_means_data = None
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for exercise_name, exercise_data in qdata_train.items():
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for example_number, example_data in exercise_data.items():
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if k_means_data is None:
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k_means_data = np.array(example_data)
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else:
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# print(k_means_data.shape)
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# print( np.atleast_1d(part).shape)
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# print( np.atleast_2d(part).shape)
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k_means_data = np.concatenate((k_means_data,np.array(example_data)), axis=0)
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print(len(k_means_data[0]))
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print(len(k_means_data[:,0]))
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print("training the model")
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kmeans = KMeans(n_clusters=num_clusters, n_init=15, max_iter= 500).fit(k_means_data)
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print("Done training model")
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centers = kmeans.cluster_centers_
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#Time to run the training by again and make the histograms for each training data point.
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classifier_training_X = []
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classifier_training_Y = []
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# print("creating histograms for training")
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for exercise_name, exercise_data in qdata_train.items():
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for example_number, example_data in exercise_data.items():
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center_histo = np.zeros((num_clusters,))
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center_indices = kmeans.predict(example_data)
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for i in center_indices:
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center_histo[i] += 1
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classifier_training_X.append(normalized(center_histo))
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classifier_training_Y.append(exercise_name)
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# print("done")
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classifier_test_X = []
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classifier_test_Y = []
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# print("creating histograms for testing")
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for exercise_name, exercise_data in qdata_test.items():
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for example_number, example_data in exercise_data.items():
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center_histo = np.zeros((num_clusters,))
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center_indices = kmeans.predict(example_data)
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for i in center_indices:
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center_histo[i] += 1
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classifier_test_X.append(normalized(center_histo))
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classifier_test_Y.append(exercise_name)
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# print(classifier_test_Y)
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print("done")
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forest_average = []
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for g in range(1):
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clf = RandomForestClassifier(n_estimators=(50)*2)
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clf.fit(classifier_training_X, classifier_training_Y)
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results = clf.predict(classifier_test_X)
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print(results)
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i = 0
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#
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#
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con_mat = confusion_matrix(results, classifier_test_Y)
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print(con_mat)
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np.savetxt("con_mat.csv", con_mat, '%5.0f', delimiter=",\t")
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wrong = 0
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right = 0
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for i in range(len(con_mat)):
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for j in range(len(con_mat[0])):
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if i != j:
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wrong += con_mat[i][j]
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else:
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right += con_mat[i][j]
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# print(right/len(results))
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# print((g+1)*2)
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forest_average.append(right/len(results))
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print("Random Forest Average Accuracy:")
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print(sum(forest_average) / len(forest_average))
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print("SVM Average Accuracy:")
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clf = svm.LinearSVC()
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clf.fit(classifier_training_X, classifier_training_Y)
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results = clf.predict(classifier_test_X)
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# print(results)
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i = 0
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con_mat = confusion_matrix(results, classifier_test_Y)
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# print(con_mat)
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wrong = 0
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right = 0
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for i in range(len(con_mat)):
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for j in range(len(con_mat[0])):
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if i != j:
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wrong += con_mat[i][j]
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else:
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right += con_mat[i][j]
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print(right/len(results))
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print("Gaussian Naive Bayes Average Accuracy:")
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clf = GaussianNB()
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clf.fit(classifier_training_X, classifier_training_Y)
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results = clf.predict(classifier_test_X)
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# print(results)
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i = 0
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con_mat = confusion_matrix(results, classifier_test_Y)
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# print(con_mat)
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wrong = 0
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right = 0
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for i in range(len(con_mat)):
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for j in range(len(con_mat[0])):
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if i != j:
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wrong += con_mat[i][j]
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else:
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right += con_mat[i][j]
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print(right/len(results))
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accuracies[q_count].append(sum(forest_average) / len(forest_average))
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print(num_clusters)
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print(quantize_length)
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n_count+=1
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n_count = 0
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q_count +=1
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#Following code copied froms tack overflow for printing out the confusion matrix
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#convert the string into an array
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df_cm = pd.DataFrame(con_mat, index = [i for i in "ABCDEFGHIJKLMN"],
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columns = [i for i in "ABCDEFGHIJKLMN"])
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plt.figure(figsize = (10,7))
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sn.heatmap(df_cm, annot=True)
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pprint(accuracies)
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plt.show()
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