From a4df87c964bb1e2c2b2b9e3e0651602d8e3c4366 Mon Sep 17 00:00:00 2001
From: Zeynep Hakguder <zhakguder@cse.unl.edu>
Date: Thu, 7 Jun 2018 23:00:31 +0000
Subject: [PATCH] Update ProgrammingAssignment1.ipynb
---
ProgrammingAssignment_1/ProgrammingAssignment1.ipynb | 8 +++-----
1 file changed, 3 insertions(+), 5 deletions(-)
diff --git a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
index bcd01b0..e826ff3 100644
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
@@ -188,21 +188,19 @@
" Inherits Model class. Implements the k-NN algorithm for classification.\n",
" '''\n",
" \n",
- " def fit(self, training_features, training_labels, classes, k, distance_f,**kwargs):\n",
+ " def fit(self, training_features, training_labels, k, distance_f,**kwargs):\n",
" '''\n",
" Fit the model. This is pretty straightforward for k-NN.\n",
" Args:\n",
" training_features: ndarray\n",
" training_labels: ndarray\n",
- " classes: ndarray\n",
- " 1D array containing unique classes in the dataset\n",
" k: int\n",
" distance_f: function\n",
" kwargs: dict\n",
" Contains keyword arguments that will be passed to distance_f\n",
" '''\n",
" # TODO\n",
- " # set self.train_features, self.train_labels, self.classes, self.k, self.distance_f, self.distance_metric\n",
+ " # set self.train_features, self.train_labels, self.k, self.distance_f, self.distance_metric\n",
" \n",
" raise NotImplementedError\n",
"\n",
@@ -216,7 +214,7 @@
" mxd array containing features for the points to be predicted\n",
" Returns: \n",
" preds: ndarray\n",
- " mx1 array containing proportion of positive class for each test point\n",
+ " mx1 array containing proportion of positive class among k nearest neighbors of each test point\n",
" '''\n",
" raise NotImplementedError\n",
" \n",
--
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