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