Regression

Continuous supervised learning
discrete: fast slow
continuous

sklearn regression
http://scikit-learn.org/stable/modules/linear_model.html

>>> from sklearn import linear_model
>>> reg = linear_model.LinearRegression()
>>> reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
>>> reg.coef_
array([ 0.5,  0.5])

#!/usr/bin/python

import numpy
import matplotlib
matplotlib.use('agg')

import matplotlib.pyplot as plt
from studentRegression import studentReg
from class_vis import prettyPicture, output_image

from ages_net_worths import ageNetWorthData

ages_train, ages_test, net_worths_train, net_worths_test = ageNetWorthData()

reg = studentReg(ages_train, net_worths_train)

plt.clf()
plt.scatter(ages_train, net_worths_train, color="b", label="train data")
plt.scatter(ages_test, net_worths_test, color="r", label="test data")
plt.plot(ages_test, reg.predict(ages_test), color="black")
plt.legend(loc=2)
plt.xlabel("ages")
plt.ylabel("net worths")

def studentRegression( ages_train, net_worths_train):
	from sklearn.linear_model import LinearRegression
	reg = LinearRegression()
	reg.fit( ages_train, net_worths_train )

	return reg