Select threshold

ex8/selectThreshold.m

@@ -1,46 +1,44 @@
-function [bestEpsilon bestF1] = selectThreshold(yval, pval)
-%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
-%outliers
-%   [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
-%   threshold to use for selecting outliers based on the results from a
-%   validation set (pval) and the ground truth (yval).
-%
-
-bestEpsilon = 0;
-bestF1 = 0;
-F1 = 0;
-
-stepsize = (max(pval) - min(pval)) / 1000;
-for epsilon = min(pval):stepsize:max(pval)
-    
-    % ====================== YOUR CODE HERE ======================
-    % Instructions: Compute the F1 score of choosing epsilon as the
-    %               threshold and place the value in F1. The code at the
-    %               end of the loop will compare the F1 score for this
-    %               choice of epsilon and set it to be the best epsilon if
-    %               it is better than the current choice of epsilon.
-    %               
-    % Note: You can use predictions = (pval < epsilon) to get a binary vector
-    %       of 0's and 1's of the outlier predictions
-
-
-
-
-
-
-
-
-
-
-
-
-
-    % =============================================================
-
-    if F1 > bestF1
-       bestF1 = F1;
-       bestEpsilon = epsilon;
-    end
-end
-
-end
+function [bestEpsilon bestF1] = selectThreshold(yval, pval)
+%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
+%outliers
+%   [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
+%   threshold to use for selecting outliers based on the results from a
+%   validation set (pval) and the ground truth (yval).
+%
+
+bestEpsilon = 0;
+bestF1 = 0;
+F1 = 0;
+
+stepsize = (max(pval) - min(pval)) / 1000;
+for epsilon = min(pval):stepsize:max(pval)
+
+    % ====================== YOUR CODE HERE ======================
+    % Instructions: Compute the F1 score of choosing epsilon as the
+    %               threshold and place the value in F1. The code at the
+    %               end of the loop will compare the F1 score for this
+    %               choice of epsilon and set it to be the best epsilon if
+    %               it is better than the current choice of epsilon.
+    %
+    % Note: You can use predictions = (pval < epsilon) to get a binary vector
+    %       of 0's and 1's of the outlier predictions
+
+    predictions = (pval < epsilon);
+
+    tp = sum((predictions == 1) & (yval == 1));
+    fp = sum((predictions == 1) & (yval == 0));
+    fn = sum((predictions == 0) & (yval == 1));
+    prec = tp/(tp+fp);
+    rec = tp/(tp+fn);
+
+    F1 = (2*prec*rec)/(prec+rec);
+
+    % =============================================================
+
+    if F1 > bestF1
+       bestF1 = F1;
+       bestEpsilon = epsilon;
+    end
+end
+
+end