Shaun Reed
940d035638
+ Neural network CLI + Hidden Markov Model CLI + K-Means clustering CLI + Linear regression CLI + Screenshots, updated README instructions |
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| .. | ||
| README.md | ||
| input.txt | ||
| linear-regression.py | ||
| screenshot.png | ||
README.md
Install required dependencies for matplotlib GUI frontend and all pip other packages for this project
sudo apt install python3-tk
python3.9 -m pip install -r requirements.txt
Given a set of tuple (X,Y) data points as [(X, Y), .., (X, Y)], determine the
best fitting line plot, and then apply this projection to predict the dependent Y
value using an independent GIVEN_X value.
python3.9 linear-regression.py -h
usage: linear-regression.py [-h] [--silent] [--file [FILE_PATH]] [GIVEN_X] [X,Y ...]
Find most fitting line plot for given data points and predict value given some X
positional arguments:
GIVEN_X Value for X for prediction using linear regression
(default: '4.5')
X,Y A list of data points separated by spaces as: x,y x,y x,y ...
(default: '[(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)]')
optional arguments:
-h, --help show this help message and exit
--silent When this flag is set, line plot visualization will not be shown
(default: 'False')
--file [FILE_PATH], -f [FILE_PATH]
Optionally provide file for data to be read from. Each point must be on it's own line with format x,y
Running linear regression program
python3.9 linear-regression.py --file ./input.txt --silent
Finding fitting line plot for given data [(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)]
points_avg: (5.117647058823529, 5.235294117647059)
variance: (241.76470588235296, 193.05882352941177)
sigma: (3.887196176892422, 3.4736402333270258)
covariance: 0.8455882352941174
correlation: 0.0626235432924427
Our line Y = BX + A must pass through the point (5.117647058823529, 5.235294117647059)
Y = (0.05596107055961069)X + 4.9489051094890515
For X = 4.5, Y is predicted to be 5.200729927007299
By default, the following linear regression is calculated and displayed
python3.9 linear-regression.py
Finding fitting line plot for given data [(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)]
points_avg: (4.0, 8.857142857142858)
variance: (28.0, 104.85714285714286)
sigma: (2.160246899469287, 4.180453381654971)
covariance: 8.666666666666666
correlation: 0.9596775116832306
Our line Y = BX + A must pass through the point (4.0, 8.857142857142858)
Y = (1.8571428571428565)X + 1.4285714285714315
For X = 4.5, Y is predicted to be 9.785714285714285
