First commit

Exercise3/utils.py

@@ -0,0 +1,104 @@
+import sys
+import numpy as np
+from matplotlib import pyplot
+
+sys.path.append('..')
+from submission import SubmissionBase
+
+
+def displayData(X, example_width=None, figsize=(10, 10)):
+    """
+    Displays 2D data stored in X in a nice grid.
+    """
+    # Compute rows, cols
+    if X.ndim == 2:
+        m, n = X.shape
+    elif X.ndim == 1:
+        n = X.size
+        m = 1
+        X = X[None]  # Promote to a 2 dimensional array
+    else:
+        raise IndexError('Input X should be 1 or 2 dimensional.')
+
+    example_width = example_width or int(np.round(np.sqrt(n)))
+    example_height = n / example_width
+
+    # Compute number of items to display
+    display_rows = int(np.floor(np.sqrt(m)))
+    display_cols = int(np.ceil(m / display_rows))
+
+    fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)
+    fig.subplots_adjust(wspace=0.025, hspace=0.025)
+
+    ax_array = [ax_array] if m == 1 else ax_array.ravel()
+
+    for i, ax in enumerate(ax_array):
+        ax.imshow(X[i].reshape(example_width, example_width, order='F'),
+                  cmap='Greys', extent=[0, 1, 0, 1])
+        ax.axis('off')
+
+
+def sigmoid(z):
+    """
+    Computes the sigmoid of z.
+    """
+    return 1.0 / (1.0 + np.exp(-z))
+
+
+class Grader(SubmissionBase):
+    # Random Test Cases
+    X = np.stack([np.ones(20),
+                  np.exp(1) * np.sin(np.arange(1, 21)),
+                  np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)
+
+    y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
+
+    Xm = np.array([[-1, -1],
+                   [-1, -2],
+                   [-2, -1],
+                   [-2, -2],
+                   [1, 1],
+                   [1, 2],
+                   [2, 1],
+                   [2, 2],
+                   [-1, 1],
+                   [-1, 2],
+                   [-2, 1],
+                   [-2, 2],
+                   [1, -1],
+                   [1, -2],
+                   [-2, -1],
+                   [-2, -2]])
+    ym = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
+
+    t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F'))
+    t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F'))
+
+    def __init__(self):
+        part_names = ['Regularized Logistic Regression',
+                      'One-vs-All Classifier Training',
+                      'One-vs-All Classifier Prediction',
+                      'Neural Network Prediction Function']
+
+        super().__init__('multi-class-classification-and-neural-networks', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 5):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)
+                    res = np.hstack(res).tolist()
+                elif part_id == 2:
+                    res = func(self.Xm, self.ym, 4, 0.1)
+                elif part_id == 3:
+                    res = func(self.t1, self.Xm) + 1
+                elif part_id == 4:
+                    res = func(self.t1, self.t2, self.Xm) + 1
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0