From c8a372b1f84b7891b9e48b243b09d2fba147660b Mon Sep 17 00:00:00 2001
From: ugurthemaster <ugurthemaster@gmail.com>
Date: Wed, 5 Mar 2014 10:55:33 +0200
Subject: [PATCH] Update plot_svm_regression.py
library imports have been moved to top of the file.
---
examples/svm/plot_svm_regression.py | 9 ++++-----
1 file changed, 4 insertions(+), 5 deletions(-)
diff --git a/examples/svm/plot_svm_regression.py b/examples/svm/plot_svm_regression.py
index c2c1dbcfe0..16551352cb 100644
--- a/examples/svm/plot_svm_regression.py
+++ b/examples/svm/plot_svm_regression.py
@@ -8,10 +8,12 @@ Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
-###############################################################################
-# Generate sample data
import numpy as np
+from sklearn.svm import SVR
+import pylab as pl
+###############################################################################
+# Generate sample data
X = np.sort(5 * np.random.rand(40, 1), axis=0)
y = np.sin(X).ravel()
@@ -21,8 +23,6 @@ y[::5] += 3 * (0.5 - np.random.rand(8))
###############################################################################
# Fit regression model
-from sklearn.svm import SVR
-
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3, degree=2)
@@ -32,7 +32,6 @@ y_poly = svr_poly.fit(X, y).predict(X)
###############################################################################
# look at the results
-import pylab as pl
pl.scatter(X, y, c='k', label='data')
pl.hold('on')
pl.plot(X, y_rbf, c='g', label='RBF model')
--
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