[impl] Implement a test for multi_svr

Author: nwtgck

src/multi_svr.py

@@ -70,7 +70,7 @@ def predict(self, X):
   regressor.fit(X, y)
 
   pred_y = regressor.predict(X)
-  err    = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
+  errs   = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
 
   print('pred_y:', pred_y)
-  print('err:', err)
+  print('errs:', errs)

tests/multi_svr_test.py

@@ -1,10 +1,41 @@
 import unittest
+from sklearn import metrics
+
 import multi_svr
 
 class MultiSVRTest(unittest.TestCase):
 
-  def test_dummy(self):
-    self.assertEqual(1, 1) # TODO impl
+  def test_prediction(self):
+    X = [
+      [0, 0],
+      [0, 10],
+      [1, 10],
+      [1, 20],
+      [1, 30],
+      [1, 40]
+    ]
+
+    y = [
+      [0, 0],
+      [0, 10],
+      [2, 10],
+      [2, 20],
+      [2, 30],
+      [2, 40]
+    ]
+
+    #  Create SVR
+    regressor = multi_svr.MutilSVR(kernel='linear')
+    # Fit
+    regressor.fit(X, y)
+    # Predict
+    pred_y = regressor.predict(X)
+    # Calc errors
+    errs = metrics.mean_squared_error(y, pred_y, multioutput='raw_values')
+
+    # Errors should be small
+    assert(errs[0] < 0.05)
+    assert(errs[1] < 0.05)
 
 
 def suite():