MSE/diff falsch bestimmt :o

2 files changed, 11 insertions, 2 deletions

diff --git a/boston.py b/boston.py
index e8382de..dc99fa5 100644
--- a/boston.py
+++ b/boston.py
@@ -4,6 +4,7 @@ from sklearn import preprocessing
 from sklearn.cross_validation import train_test_split
 import tensorflow as tf
 import numpy.random
+import numpy as np
 
 SEED = 42
 numpy.random.seed(SEED)
@@ -73,8 +74,8 @@ with tf.device('/cpu:0'):
                                   tf.log(tf.add(vecLayeroutHidden, 1)))
         scalarLogdiffsum = tf.reduce_sum(tf.abs(vecLogdifference))
         scalarDiffsum = tf.reduce_sum(tf.abs(vecDifference))
-        scalarMSE = tf.reduce_sum(tf.square(vecDifference))
-        scalarError = scalarDiffsum  # scalarLogdiffsum
+        scalarMSE = tf.reduce_sum(tf.square(vecDifference))  # ! do not unscale
+        scalarError = scalarDiffsum
         summaryError = tf.scalar_summary('error', scalarError)
 
     with tf.name_scope('train'):
@@ -123,6 +124,9 @@ with tf.device('/cpu:0'):
     print("difference")
     print("--------------------------------")
     print(target_scaler.inverse_transform(result[0]))
+    diff = target_scaler.inverse_transform(
+            result[1]) - target_scaler.inverse_transform(y_test)
+    print(diff)
     print("--------------------------------")
     print("")
     print("total logarithmic difference")
@@ -130,3 +134,4 @@ with tf.device('/cpu:0'):
         target_scaler.inverse_transform([result[2]])) + str(")"))
     print("MSE")
     print(target_scaler.inverse_transform([result[3]]))
+    print(np.mean(diff ** 2))
diff --git a/linear.py b/linear.py
index 36ef411..f6d1568 100644
--- a/linear.py
+++ b/linear.py
@@ -15,6 +15,10 @@ model = LinearRegression()
 model.fit(data.data, data.target)
 
 p=model.predict(X_test)
+print(p)
+print('diff')
+print('')
+print(y_test - p)
 squares=[]
 for n in range(len(p)):
     squares.append((y_test[n] - p[n])**2)