Matplotlib: Linear regression

Here is the data to be used as an example.

x y 1 2 2 1 3 4 4 3 5 6 6 5 7 8

Here is the Python code.

import matplotlib.pyplot as plt
import rsPlot

import numpy as np
from sklearn.linear_model import LinearRegression

dataList1 = [
	[1,2],
	[2,1],
	[3,4],
	[4,3],
	[5,6],
	[6,5],
	[7,8],
	]

xData0 = []
yData0 = []
for x1, y1 in dataList1:
	xData0.append(x1)
	yData0.append(y1)
xData1 = np.array(xData0).reshape(-1, 1)
yData1 = np.array(yData0).reshape(-1, 1)

reg1 = LinearRegression()
reg1.fit(xData1,yData1)
slope1 = float(reg1.coef_)
coef1 = float(reg1.coef_)
intercept1 = float(reg1.intercept_)
equation1 = "y == {0:1.1f} * x + {1:0.2f}".format(slope1,intercept1)

print("")
print("Regression line:")
print("")
print("\t{0:s}".format(equation1))
print("")
print("\tSlope: {0:0.2f}".format(slope1))
print("\tIntercept: {0:0.2f}".format(intercept1))
print("\tCorrelation coefficient: {0:0.2f}".format(coef1))
print("")
print("Predictions:")
print("")
print("\t{0:>5} {1:>5} {2:>5}-actual".format("x", "y", "y"))
for x1, y1 in dataList1:
	y1 = slope1 * x1 + intercept1
	print("\t{0:5.1f} {1:5.1f} {2:5.1f}".format(x1,y1,y1))
print("")

yPredict1 = reg1.predict(xData1)


plot1 = rsPlot.plot("regr21-1")
ax1 = plot1.ax1
plt.title("data : x vs. y")
plt.xlabel("x")
plt.ylabel("y")
ax1.scatter(xData1,yData1,color="#990099")
ax1.plot(xData1,yPredict1,color="#000099",label=equation1)
ax1.legend(fontsize=10)
plot1.save()

Here is the output of the Python code.

(module rsPlot imported)

Regression line:

	y == 1.0 * x + 0.14

	Slope: 1.00
	Intercept: 0.14
	Correlation coefficient: 1.00

Predictions:

	    x     y     y-actual
	  1.0   1.1   1.1
	  2.0   2.1   2.1
	  3.0   3.1   3.1
	  4.0   4.1   4.1
	  5.0   5.1   5.1
	  6.0   6.1   6.1
	  7.0   7.1   7.1

	./regr21-1-01.png : 559 x 462 , 21,634 bytes