added week4 phase3 summer

Author: abhilashreddys

Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Data/ex3data1.mat

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+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

Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Data/ex3weights.mat

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+## To be done for next week
+
+1. Complete All Video Lectures and Quizes of week4 of the [Coursera Course](https://www.coursera.org/learn/machine-learning).
+2. For python implementation of neural network you can take help from [here](https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6).
+3. Add your python(if you are doing by your own) or jupyter notebook files and create pull request.
+4. Each part of the assignment has equal weightage - total 100 points.
+5. Complete assignment in octave(for the sake of your certification, it is optional and does not consist of any point).

Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Figures/neuralnetwork.png

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+from urllib.parse import urlencode
+from urllib.request import urlopen
+import pickle
+import json
+from collections import OrderedDict
+import numpy as np
+import os
+
+
+class SubmissionBase:
+
+    submit_url = 'https://www-origin.coursera.org/api/' \
+                 'onDemandProgrammingImmediateFormSubmissions.v1'
+    save_file = 'token.pkl'
+
+    def __init__(self, assignment_slug, part_names):
+        self.assignment_slug = assignment_slug
+        self.part_names = part_names
+        self.login = None
+        self.token = None
+        self.functions = OrderedDict()
+        self.args = dict()
+
+    def grade(self):
+        print('\nSubmitting Solutions | Programming Exercise %s\n' % self.assignment_slug)
+        self.login_prompt()
+
+        # Evaluate the different parts of exercise
+        parts = OrderedDict()
+        for part_id, result in self:
+            parts[str(part_id)] = {'output': sprintf('%0.5f ', result)}
+        result, response = self.request(parts)
+        response = json.loads(response.decode("utf-8"))
+
+        # if an error was returned, print it and stop
+        if 'errorMessage' in response:
+            print(response['errorMessage'])
+            return
+
+        # Print the grading table
+        print('%43s | %9s | %-s' % ('Part Name', 'Score', 'Feedback'))
+        print('%43s | %9s | %-s' % ('---------', '-----', '--------'))
+        for part in parts:
+            part_feedback = response['partFeedbacks'][part]
+            part_evaluation = response['partEvaluations'][part]
+            score = '%d / %3d' % (part_evaluation['score'], part_evaluation['maxScore'])
+            print('%43s | %9s | %-s' % (self.part_names[int(part) - 1], score, part_feedback))
+        evaluation = response['evaluation']
+        total_score = '%d / %d' % (evaluation['score'], evaluation['maxScore'])
+        print('                                  --------------------------------')
+        print('%43s | %9s | %-s\n' % (' ', total_score, ' '))
+
+    def login_prompt(self):
+        if os.path.isfile(self.save_file):
+            with open(self.save_file, 'rb') as f:
+                login, token = pickle.load(f)
+            reenter = input('Use token from last successful submission (%s)? (Y/n): ' % login)
+
+            if reenter == '' or reenter[0] == 'Y' or reenter[0] == 'y':
+                self.login, self.token = login, token
+                return
+            else:
+                os.remove(self.save_file)
+
+        self.login = input('Login (email address): ')
+        self.token = input('Token: ')
+
+        # Save the entered credentials
+        if not os.path.isfile(self.save_file):
+            with open(self.save_file, 'wb') as f:
+                pickle.dump((self.login, self.token), f)
+
+    def request(self, parts):
+        params = {
+            'assignmentSlug': self.assignment_slug,
+            'secret': self.token,
+            'parts': parts,
+            'submitterEmail': self.login}
+
+        params = urlencode({'jsonBody': json.dumps(params)}).encode("utf-8")
+        f = urlopen(self.submit_url, params)
+        try:
+            return 0, f.read()
+        finally:
+            f.close()
+
+    def __iter__(self):
+        for part_id in self.functions:
+            yield part_id
+
+    def __setitem__(self, key, value):
+        self.functions[key] = value
+
+
+def sprintf(fmt, arg):
+    """ Emulates (part of) Octave sprintf function. """
+    if isinstance(arg, tuple):
+        # for multiple return values, only use the first one
+        arg = arg[0]
+
+    if isinstance(arg, (np.ndarray, list)):
+        # concatenates all elements, column by column
+        return ' '.join(fmt % e for e in np.asarray(arg).ravel('F'))
+    else:
+        return fmt % arg