Added logreg_predictHEADmaster

1 files changed, 44 insertions, 0 deletions

diff --git a/src/logreg_predict.py b/src/logreg_predict.py
new file mode 100755
index 0000000..9982c5b
--- /dev/null
+++ b/src/logreg_predict.py
@@ -0,0 +1,44 @@
+#!/bin/python3
+
+import sys
+
+import numpy as np
+
+from dataset import Dataset
+
+
+def sigmoid(x):
+    return 1.0 / (1.0 * np.exp(-x))
+
+def hypothesis(x, theta):
+    return sigmoid(x.dot(theta))
+
+if __name__ == '__main__':
+    if len(sys.argv) != 3:
+        raise 'Usage: {} dataset_path weights_path'.format(sys.argv[0])
+
+    labels = []
+    thetas = []
+    with open(sys.argv[2]) as f:
+        for line in f:
+            label_end = line.find(":")
+            labels.append(line[:label_end])
+            thetas.append(np.array([float(s) for s in line[label_end + 2:].split(",")]))
+    # print(labels)
+    # print(thetas)
+
+    d = Dataset(sys.argv[1])
+
+    X = d.df_scores
+    X = (X - X.min()) / (X.max() - X.min())
+    X = np.hstack([X, np.ones((X.shape[0], 1))])
+
+    with open("houses.csv", "w") as houses_file:
+        houses_file.write("Index,Hogwarts House\n")
+        for i, x in enumerate(X):
+            hs = []
+            for l, t in zip(labels, thetas):
+                hs.append((l, hypothesis(x, t)))
+
+            predicted, _ = max(hs, key=lambda x: x[1])
+            houses_file.write("{},{}\n".format(i, predicted))