# HG changeset patch
# User Jeff Hammel <k0scist@gmail.com>
# Date 1504551203 25200
# Node ID 77f68c241b37d01618e22d6ab545f287a6d6f385
# Parent  22218d90d33f1f4994fc0a474131a70335ba622f
[logistic regression] propagate

diff -r 22218d90d33f -r 77f68c241b37 tests/test_logistic_regression.py
--- a/tests/test_logistic_regression.py	Mon Sep 04 11:38:46 2017 -0700
+++ b/tests/test_logistic_regression.py	Mon Sep 04 11:53:23 2017 -0700
@@ -9,6 +9,7 @@
 import unittest
 from tvii import logistic_regression
 
+
 class LogisticRegresionTests(unittest.TestCase):
 
     def test_cost(self):
@@ -23,5 +24,23 @@
         cost = logistic_regression.cost_function(w, b, X, Y)
         assert abs(cost - expected_cost) < 1e-6
 
+    def test_propagate(self):
+        """test canned logistic regression example"""
+
+        # sample variables
+        w = np.array([[1],[2]])
+        b = 2
+        X = np.array([[1,2],[3,4]])
+        Y = np.array([[1,0]])
+
+        # calculate gradient and cost
+        grads, cost = logistic_regression.propagate(w, b, X, Y)
+
+        # compare to expected,
+        dw_expected = [[ 0.99993216], [ 1.99980262]]
+        db_expected = 0.499935230625
+        cost_expected = 6.000064773192205
+
+
 if __name__ == '__main__':
     unittest.main()
diff -r 22218d90d33f -r 77f68c241b37 tvii/logistic_regression.py
--- a/tvii/logistic_regression.py	Mon Sep 04 11:38:46 2017 -0700
+++ b/tvii/logistic_regression.py	Mon Sep 04 11:53:23 2017 -0700
@@ -43,12 +43,28 @@
     - Write your code step by step for the propagation. np.log(), np.dot()
     """
 
-    m = X.shape[1]
+
 
+    # FORWARD PROPAGATION (FROM X TO COST)
     cost = cost_function(w, b, X, Y)  # compute cost
 
-    A = sigmoid(w.T*X + b)  # compute activation
-    raise NotImplementedError('TODO')
+    # BACKWARD PROPAGATION (TO FIND GRADIENT)
+    m = X.shape[1]
+    A = sigmoid(np.dot(w.T, X) + b)  # compute activation
+    dw = (1./m)*np.dot(X, (A - Y).T)
+    db = (1./m)*np.sum(A - Y)
+
+    # sanity check
+    assert(A.shape[1] == m)
+    assert(dw.shape == w.shape), "dw.shape is {}; w.shape is {}".format(dw.shape, w.shape)
+    assert(db.dtype == float)
+    cost = np.squeeze(cost)
+    assert(cost.shape == ())
+
+    # return gradients
+    grads = {"dw": dw,
+             "db": db}
+    return grads, cost
 
 
 def cost_function(w, b, X, Y):