From 74b9312a5337bf1a3c0ad1f569eafecfc5a91446 Mon Sep 17 00:00:00 2001
From: Zeynep Hakguder <zhakguder@cse.unl.edu>
Date: Wed, 6 Jun 2018 18:06:20 +0000
Subject: [PATCH] Update ProgrammingAssignment1.ipynb
---
ProgrammingAssignment_1/ProgrammingAssignment1.ipynb | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
index 1571a60..3c16e40 100644
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
@@ -163,7 +163,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can start implementing our *k*-NN classifier. *k*-NN class inherits Model class. Use the \"distance\" function you defined above. \"fit\" method takes *k* as an argument. \"predict\" takes as input an *mxd* array containing *d*-dimensional *m* feature vectors for examples and outputs the predicted class and the ratio of positive examples in *k* nearest neighbors."
+ "We can start implementing our *k*-NN classifier. *k*-NN class inherits Model class. Use the \"distance\" function you defined above. \"fit\" method takes *k* as an argument. \"predict\" takes as input an *mxd* array containing *d*-dimensional *m* feature vectors for examples and for each input point outputs the predicted class and the ratio of positive examples in *k* nearest neighbors."
]
},
{
@@ -213,7 +213,8 @@
" test_features: ndarray\n",
" mxd array containing features for the points to be predicted\n",
" Returns: \n",
- " ndarray\n",
+ " preds: ndarray\n",
+ " mx2 array containing predicted class and proportion for each test point\n",
" '''\n",
" raise NotImplementedError\n",
" \n",
--
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