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Add LICENSE.txt with GPL-3.0-or-later license

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Lucas Oskorep
2026-02-27 19:12:01 -05:00
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LICENSE.txt
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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for software.
By contrast, the GPL guarantees your freedom to share and change all versions.
To protect your rights, we need to prevent others from denying you these rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License.
Some devices are designed to deny users access to install or run modified versions.
This is fundamentally incompatible with the aim of protecting users' freedom to change software.
Finally, every program is threatened constantly by software patents.
The GPL assures that patents cannot be used to render the program non-free.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of works.
"The Program" refers to any copyrightable work licensed under this License.
1. Source Code.
The "source code" for a work means the preferred form for making modifications.
2. Basic Permissions.
All rights granted under this License are granted for the term of copyright on the Program,
and are irrevocable provided the stated conditions are met.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological measure.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you receive it.
5. Conveying Modified Source Versions.
You may convey a work based on the Program under section 4, provided that you also meet all conditions:
a) The work must carry prominent notices stating that you modified it.
b) You must license the entire work under this License.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under sections 4 and 5, provided that you also convey
the machine-readable Corresponding Source under the terms of this License.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this License by making exceptions.
8. Termination.
You may not propagate or modify a covered work except as expressly provided under this License.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or run a copy of the Program.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically receives a license from the original licensors.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this License of the Program.
12. No Surrender of Others' Freedom.
If conditions are imposed on you that contradict the conditions of this License, they do not excuse you from the conditions.
13. Use with the GNU Affero General Public License.
You have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER BE LIABLE TO YOU FOR DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public,
the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program:
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.
========================================================================
Copyright (C) YOUR NAME 2024
Licensed under the GNU General Public License Version 3 (GPL-3.0-or-later).
This program is free software: you can redistribute it and/or modify
it under the terms of the GPL v3 as published by the Free Software Foundation,
either version 3 of the License, or (at your option) any later version.
See <https://www.gnu.org/licenses/> for full license text.
test.json
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train_model.py
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import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
from sklearn import svm
from pprint import pprint
import os
import seaborn as sn
def normalized(a, axis=-1, order=2):
return a / sum(a)
def get_activities(dir):
activity_data_train = {}
activity_data_test = {}
i= 0
for root, dirs, files in os.walk(dir):
activity_name = root.split('\\')[1]
activity_data_train[activity_name] = []
activity_data_test[activity_name] = []
for i in range(int(.8 * len(files))):
data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
activity_data_train[activity_name].append(data)
i+=1
while i < len(files):
data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
activity_data_test[activity_name].append(data)
i+=1
return activity_data_train, activity_data_test
def quantize_data(data, vector_len):
del data[""]
print("quantizing", data)
print(vector_len)
quantized_data = {}
total = 0
total2 = 0
for key, value in data.items():
print(key, len(value))
quantized_data[key] = {}
print(vector_len)
for i in range(len(value)):
total2+= len(value[i])/vector_len
while(len(value[i]) %vector_len != 0):
value[i] = value[i][:-1]
print(len(value[i]), vector_len)
new_data = np.split(value[i], len(value[i]) / vector_len)
# if len(new_data[len(new_data) - 1]) != vector_len:
# print(vector_len,len(new_data[len(new_data) - 1]))
# del new_data[len(new_data) - 1]
quantized_data[key][i] = new_data
total+= len(new_data)
print("Total is:", total, total2)
return quantized_data
lengths = [4]
# for i in range(3,4):
# lengths.append((i+1)*21)
data_train, data_test = get_activities('data\\')
print("----------------")
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
# print(X)
clusters =[1000]# [600,600,600,1000,1000,1000,1400,1400,1400]
q_count = 0
n_count = 0
accuracies = []
for quantize_length in lengths:
accuracies.append([])
for num_clusters in clusters:
qdata_test = quantize_data(data_test, quantize_length)
print("----------------")
qdata_train = quantize_data(data_train, quantize_length)
print("----------------")
#fill kmeans_data with all of the data of all of the exercises reguardless of what they are
k_means_data = None
for exercise_name, exercise_data in qdata_train.items():
for example_number, example_data in exercise_data.items():
if k_means_data is None:
k_means_data = np.array(example_data)
else:
# print(k_means_data.shape)
# print( np.atleast_1d(part).shape)
# print( np.atleast_2d(part).shape)
k_means_data = np.concatenate((k_means_data,np.array(example_data)), axis=0)
print(len(k_means_data[0]))
print(len(k_means_data[:,0]))
print("training the model")
kmeans = KMeans(n_clusters=num_clusters, n_init=15, max_iter= 500).fit(k_means_data)
print("Done training model")
centers = kmeans.cluster_centers_
#Time to run the training by again and make the histograms for each training data point.
classifier_training_X = []
classifier_training_Y = []
# print("creating histograms for training")
for exercise_name, exercise_data in qdata_train.items():
for example_number, example_data in exercise_data.items():
center_histo = np.zeros((num_clusters,))
center_indices = kmeans.predict(example_data)
for i in center_indices:
center_histo[i] += 1
classifier_training_X.append(normalized(center_histo))
classifier_training_Y.append(exercise_name)
# print("done")
classifier_test_X = []
classifier_test_Y = []
# print("creating histograms for testing")
for exercise_name, exercise_data in qdata_test.items():
for example_number, example_data in exercise_data.items():
center_histo = np.zeros((num_clusters,))
center_indices = kmeans.predict(example_data)
for i in center_indices:
center_histo[i] += 1
classifier_test_X.append(normalized(center_histo))
classifier_test_Y.append(exercise_name)
# print(classifier_test_Y)
print("done")
forest_average = []
for g in range(1):
clf = RandomForestClassifier(n_estimators=(50)*2)
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
print(results)
i = 0
#
#
con_mat = confusion_matrix(results, classifier_test_Y)
print(con_mat)
np.savetxt("con_mat.csv", con_mat, '%5.0f', delimiter=",\t")
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
# print(right/len(results))
# print((g+1)*2)
forest_average.append(right/len(results))
print("Random Forest Average Accuracy:")
print(sum(forest_average) / len(forest_average))
print("SVM Average Accuracy:")
clf = svm.LinearSVC()
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
# print(results)
i = 0
con_mat = confusion_matrix(results, classifier_test_Y)
# print(con_mat)
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
print(right/len(results))
print("Gaussian Naive Bayes Average Accuracy:")
clf = GaussianNB()
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
# print(results)
i = 0
con_mat = confusion_matrix(results, classifier_test_Y)
# print(con_mat)
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
print(right/len(results))
accuracies[q_count].append(sum(forest_average) / len(forest_average))
print(num_clusters)
print(quantize_length)
n_count+=1
n_count = 0
q_count +=1
#Following code copied froms tack overflow for printing out the confusion matrix
#convert the string into an array
df_cm = pd.DataFrame(con_mat, index = [i for i in "ABCDEFGHIJKLMN"],
columns = [i for i in "ABCDEFGHIJKLMN"])
plt.figure(figsize = (10,7))
sn.heatmap(df_cm, annot=True)
pprint(accuracies)
plt.show()