Update model.ipynb

ea5105e3 · Zeynep Hakguder · e696f5f2 · ea5105e3
Commit ea5105e3 authored 6 years ago by Zeynep Hakguder
--- a/ProgrammingAssignment_3/model.ipynb
+++ b/ProgrammingAssignment_3/model.ipynb
@@ -86,7 +86,7 @@
    "                fp += 1                            \n",
    "    \n",
    "    # returns the confusion matrix as numpy.ndarray\n",
-    "    return np.array([tp,fp, fn, tn])"
+    "    return np.array([[tp,fp], [fn, tn]])"
   ]
  }
 ],

 %% Cell type:markdown id: tags:

 # JUPYTER NOTEBOOK TIPS

 Each rectangular box is called a cell.
 * ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
 * alt+ENTER evaluates the current cell and adds a new cell below it.
 * If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.

 %% Cell type:markdown id: tags:

 # Supervised Learning Model Skeleton

 We'll use this skeleton for implementing different supervised learning algorithms.

 %% Cell type:code id: tags:

 ``` python
 class Model:

    def fit(self):

        raise NotImplementedError

    def predict(self, test_points):
        raise NotImplementedError
 ```

 %% Cell type:markdown id: tags:

 ## General supervised learning performance related functions

 %% Cell type:markdown id: tags:

 "conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*).

 %% Cell type:code id: tags:

 ``` python
 def conf_matrix(true, pred):
    '''
    Args:
        true:  ndarray
            nx1 array of true labels for test set
        pred: ndarray
            nx1 array of predicted labels for test set
    Returns:
        ndarray
    '''

    tp = tn = fp = fn = 0
    # calculate true positives (tp), true negatives(tn)
    # false positives (fp) and false negatives (fn)

    size = len(true)
    for i in range(size):
        if true[i]==1:
            if pred[i] == 1:
                tp += 1
            else:
                fn += 1
        else:
            if pred[i] == 0:
                tn += 1
            else:
                fp += 1

    # returns the confusion matrix as numpy.ndarray
-    return np.array([tp,fp, fn, tn])
+    return np.array([[tp,fp], [fn, tn]])
 ```