From 9e7a660ff665a6db3b2fb1a31301191c1ba5cce2 Mon Sep 17 00:00:00 2001
From: Jack Martin
Date: Tue, 31 Mar 2015 10:38:47 -0400
Subject: [PATCH] added import np statements
---
doc/modules/linear_model.rst | 2 ++
1 file changed, 2 insertions(+)
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
index f243e5d8b077c..ecf2ca22a0a15 100644
--- a/doc/modules/linear_model.rst
+++ b/doc/modules/linear_model.rst
@@ -1073,6 +1073,7 @@ polynomial regression can be created and used as follows::
>>> from sklearn.preprocessing import PolynomialFeatures
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.pipeline import Pipeline
+ >>> import numpy as np
>>> model = Pipeline([('poly', PolynomialFeatures(degree=3)),
... ('linear', LinearRegression(fit_intercept=False))])
>>> # fit to an order-3 polynomial data
@@ -1098,6 +1099,7 @@ This way, we can solve the XOR problem with a linear classifier::
>>> from sklearn.linear_model import Perceptron
>>> from sklearn.preprocessing import PolynomialFeatures
+ >>> import numpy as np
>>> X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
>>> y = X[:, 0] ^ X[:, 1]
>>> X = PolynomialFeatures(interaction_only=True).fit_transform(X)