First commit

Exercise1/utils.py

@@ -0,0 +1,48 @@
+import numpy as np
+import sys
+sys.path.append('..')
+
+from submission import SubmissionBase
+
+
+class Grader(SubmissionBase):
+    X1 = np.column_stack((np.ones(20), np.exp(1) + np.exp(2) * np.linspace(0.1, 2, 20)))
+    Y1 = X1[:, 1] + np.sin(X1[:, 0]) + np.cos(X1[:, 1])
+    X2 = np.column_stack((X1, X1[:, 1]**0.5, X1[:, 1]**0.25))
+    Y2 = np.power(Y1, 0.5) + Y1
+
+    def __init__(self):
+        part_names = ['Warm up exercise',
+                      'Computing Cost (for one variable)',
+                      'Gradient Descent (for one variable)',
+                      'Feature Normalization',
+                      'Computing Cost (for multiple variables)',
+                      'Gradient Descent (for multiple variables)',
+                      'Normal Equations']
+        super().__init__('linear-regression', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 8):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func()
+                elif part_id == 2:
+                    res = func(self.X1, self.Y1, np.array([0.5, -0.5]))
+                elif part_id == 3:
+                    res = func(self.X1, self.Y1, np.array([0.5, -0.5]), 0.01, 10)
+                elif part_id == 4:
+                    res = func(self.X2[:, 1:4])
+                elif part_id == 5:
+                    res = func(self.X2, self.Y2, np.array([0.1, 0.2, 0.3, 0.4]))
+                elif part_id == 6:
+                    res = func(self.X2, self.Y2, np.array([-0.1, -0.2, -0.3, -0.4]), 0.01, 10)
+                elif part_id == 7:
+                    res = func(self.X2, self.Y2)
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0