linear regression, smaller improvements

2 files changed, 25 insertions, 4 deletions

diff --git a/boston.py b/boston.py
index d6cf0d9..7968dec 100644
--- a/boston.py
+++ b/boston.py
@@ -5,7 +5,7 @@ from sklearn.cross_validation import train_test_split
 import tensorflow as tf
 import numpy.random
 
-SEED = 1337
+SEED = 42
 numpy.random.seed(SEED)
 tf.set_random_seed(SEED)
 
@@ -32,7 +32,7 @@ DECAYRATE = 0.95
 DECAYSTEPS = 10
 
 RUNNAME = 'B'+str(BATCH)+'S'+str(STEPS)+'H'+str(HIDDEN)+'LR'+str(LEARNINGRATE)\
-        + 'DR'+str(DECAYRATE)+'DS'+str(DECAYSTEPS)
+        + 'DR'+str(DECAYRATE)+'DS'+str(DECAYSTEPS)+'SD'+str(SEED)
 
 with tf.device('/cpu:0'):
     sess = tf.InteractiveSession()
@@ -64,7 +64,6 @@ with tf.device('/cpu:0'):
         matLayerinHidden = tf.matmul(matLayerout, matWeightsHidden,
                                      name='hidden_input')
         matLayeroutHidden = tf.sigmoid(matLayerinHidden, name='hidden_output')
-        # TODO in Facharbeit Matrixprodukt erwähnen
         # only vec because OUT=1
         vecLayeroutHidden = tf.reshape(matLayeroutHidden, [-1])
 
@@ -130,4 +129,4 @@ with tf.device('/cpu:0'):
     print(str(result[2]) + " (" + str(
         target_scaler.inverse_transform([result[2]])) + str(")"))
     print("MSE")
-    print(result[3])
+    print(target_scaler.inverse_transform([result[3]]))
diff --git a/linear.py b/linear.py
new file mode 100644
index 0000000..36ef411
--- /dev/null
+++ b/linear.py
@@ -0,0 +1,22 @@
+#!/usr/bin/python3
+from sklearn.datasets import *
+from sklearn.linear_model import LinearRegression
+from sklearn.cross_validation import train_test_split
+import numpy as np
+
+SEED = 42
+np.random.seed(SEED)
+
+data = load_boston()
+
+X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=0.85)
+
+model = LinearRegression()
+model.fit(data.data, data.target)
+
+p=model.predict(X_test)
+squares=[]
+for n in range(len(p)):
+    squares.append((y_test[n] - p[n])**2)
+
+print(np.mean(squares))