Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex8-008/mlclass-ex8/selectThreshold.m

@@ -0,0 +1,53 @@
+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
+
+	anom = yval( pval < epsilon );
+
+
+	tp = sum( anom == 1 );
+	fp = sum( anom == 0 );
+	fn = sum( yval( pval > epsilon ) == 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