Hey I want to do linear regression and create a plot on which will be also equation of my model. I have following code:
from sklearn.linear_model import LinearRegression
X = np.array((1,2, 3, 4))
Y = np.array((3, 1, 4, 5))
X = X.reshape((-1, 1))
model = LinearRegression().fit(X, Y)
plt.scatter(X, Y, color='g')
plt.plot(X, model.predict(X),color='k')
print(model.coef_[0], model.intercept_)
How to write equation on my plot automatically?
Matplotlib has extensive text support, including support for mathematical expressions, truetype support for raster and vector outputs, newline separated text with arbitrary rotations, and unicode support.
From the official documentation the following commands are used to create text in the pyplot interface and the object-oriented API:
| pyplot API | OO API | description |
|---|---|---|
| text | text | Add text at an arbitrary location of the Axes. |
| annotate | annotate | Add an annotation, with an optional arrow, at an arbitrary location of the Axes. |
| xlabel | set_xlabel | Add a label to the Axes's x-axis. |
| ylabel | set_ylabel | Add a label to the Axes's y-axis. |
| title | set_title | Add a title to the Axes. |
| figtext | text | Add text at an arbitrary location of the Figure. |
| suptitle | suptitle | Add a title to the Figure. |
from sklearn.linear_model import LinearRegression
import numpy as np
import matplotlib.pyplot as plt
X = np.array((1,2, 3, 4))
Y = np.array((3, 1, 4, 5))
X = X.reshape((-1, 1))
model = LinearRegression().fit(X, Y)
fig = plt.figure()
ax = fig.add_subplot()
plt.scatter(X, Y, color='g')
plt.plot(X, model.predict(X),color='k')
ax.text(1, 4, r'LR equation: $Y = a + bX$', fontsize=10)
print(model.coef_[0], model.intercept_)
Plot:
Y = model.coef_[0] x + model.intercept_ instead of Y = a + bX and I want to do it automatically and dont write it by hand
ax.text(1, 4, f'$Y = {model.coef_[0]} * x + {model.intercept_}$', fontsize=10). Round ax.text(1, 4, f'$Y = {round(model.coef_[0], 2)} * x + {model.intercept_}$', fontsize=10)