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Zeynep Hakguder · Zeynep Hakguder · Zeynep Hakguder · Zeynep Hakguder · Zeynep Hakguder · Zeynep Hakguder
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
 %% Cell type:markdown id: tags:

 # *k*-Nearest Neighbor

 We'll implement *k*-Nearest Neighbor (*k*-NN) algorithm for this assignment. We will use the **madelon** dataset as in Programming Assignment 0.

 A skeleton of a general supervised learning model is provided in "model.ipynb". The functions that will be implemented there will be indicated in this notebook.

 ### Assignment Goals:
 In this assignment, we will:
 * implement 'Euclidean' and 'Manhattan' distance metrics
 * use the validation dataset to find a good value for *k*
 * evaluate our model with respect to performance measures:
    * accuracy, generalization error
    * confusion matrix
    *  Receiver Operating Characteristic (ROC) curve
-* try to assess if *k*-NN is suitable for the dataset you used
+* try to assess if *k*-NN is suitable for the dataset

 ## Note:

 You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.

 %% Cell type:markdown id: tags:

 # GRADING

 You will be graded on parts that are marked with **TODO** comments. Read the comments in the code to make sure you don't miss any.

 ### Mandatory for 478 & 878:

 |   | Tasks                      | 478 | 878 |
 |---|----------------------------|-----|-----|
 | 1 | Implement `distance`       |  15 |  15 |
 | 2 | Implement `k-NN` methods   |  35 |  30 |
 | 3 | Model evaluation           |  25 |  20 |
 | 5 | ROC curve analysis         |  25 |  25 |

 ### Mandatory for 878, bonus for 478

 |   | Tasks          | 478 | 878 |
 |---|----------------|-----|-----|
 | 4 | Optimizing *k* | 10  | 10  |

 ### Bonus for 478/878

 |   | Tasks          | 478 | 878 |
 |---|----------------|-----|-----|
 | 6 | Assess suitability of *k*-NN | 10  | 10  |

 Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 100 points in this assignment and extra 20 bonus points for 478 students and 10 bonus points for 878 students.

 %% Cell type:markdown id: tags:

 # YOUR GRADE

 ### Group Members:

 |   | Tasks                      | Points |
 |---|----------------------------|-----|
 | 1 | Implement `distance`       |     |
 | 2 | Implement `k-NN` methods   |     |
 | 3 | Model evaluation           |     |
 | 4 | Optimizing *k*             |     |
 | 5 | ROC curve analysis         |     |
 | 6 | Assess suitability of *k*-NN|    |



 %% Cell type:markdown id: tags:

 You can use numpy for array operations and matplotlib for plotting for this assignment. Please do not add other libraries.

 %% Cell type:code id: tags:

 ``` python
 import numpy as np
 import matplotlib.pyplot as plt
 ```

 %% Cell type:markdown id: tags:

 Following code makes the Model class and relevant functions available from model.ipynb.

 %% Cell type:code id: tags:

 ``` python
 %run 'model.ipynb'
 ```

 %% Cell type:markdown id: tags:

 ## TASK 1: Implement `distance` function

 %% Cell type:markdown id: tags:

 Choice of distance metric plays an important role in the performance of *k*-NN. Let's start with implementing a distance method  in the "distance" function in **model.ipynb**. It should take two data points and the name of the metric and return a scalar value.

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Euclidean +7.5, +7.5
 * Manhattan +7.5, +7.5

 %% Cell type:markdown id: tags:

 ### Test `distance`

 %% Cell type:code id: tags:

 ``` python
 x = np.array(range(100))
 y = np.array(range(100, 200))
 dist_euclidean = distance(x, y, 'Euclidean')
 dist_manhattan = distance(x, y, 'Manhattan')
 print('Euclidean distance: {}, Manhattan distance: {}'.format(dist_euclidean, dist_manhattan))
 ```

 %% Cell type:markdown id: tags:

 ## TASK 2: Implement *k*-NN Class Methods

 %% Cell type:markdown id: tags:

 We can start implementing our *k*-NN classifier. *k*-NN class inherits Model class. Use the "distance" function you defined above. "fit" method takes *k* as an argument. "predict" takes as input an *mxd* array containing *d*-dimensional *m* feature vectors for examples and for each input point outputs the predicted class and the ratio of positive examples in *k* nearest neighbors.

 %% Cell type:markdown id: tags:

 ### Rubric:
 * correct implementation of fit method +10, +10
 * correct implementation of predict method +25, +20

 %% Cell type:code id: tags:

 ``` python
 class kNN(Model):
    '''
    Inherits Model class. Implements the k-NN algorithm for classification.
    '''

-    def fit(self, training_features, training_labels, classes, k, distance_f,**kwargs):
+    def fit(self, training_features, training_labels, k, distance_f,**kwargs):
        '''
        Fit the model. This is pretty straightforward for k-NN.
        Args:
            training_features: ndarray
            training_labels: ndarray
-            classes: ndarray
-                1D array containing unique classes in the dataset
            k: int
            distance_f: function
            kwargs: dict
                Contains keyword arguments that will be passed to distance_f
        '''
        # TODO
-        # set self.train_features, self.train_labels, self.classes, self.k, self.distance_f, self.distance_metric
+        # set self.train_features, self.train_labels, self.k, self.distance_f, self.distance_metric

        raise NotImplementedError

        return


    def predict(self, test_features):
        '''
        Args:
            test_features: ndarray
                mxd array containing features for the points to be predicted
        Returns:
            preds: ndarray
-                mx2 array containing predicted class and proportion for each test point
+                mx1 array containing proportion of positive class among k nearest neighbors of each test point
        '''
        raise NotImplementedError

        # TODO

-        # for each point in test_features
+        # for each point
        # use your implementation of distance function
        #  distance_f(..., distance_metric)
        # to find the labels of k-nearest neighbors.

-        # you'll need proportion of the dominant class
+        # You'll need proportion of positive examples
        # in k nearest neighbors

        return preds

 ```

 %% Cell type:markdown id: tags:

 ## TASK 3: Build and Evaluate the Model

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Reasonable accuracy values +10, +5
 * Reasonable confidence intervals on the error estimate +10, +10
 * Reasonable confusion matrix +5, +5

 %% Cell type:markdown id: tags:

 Preprocess the data files and partition the data.

 %% Cell type:code id: tags:

 ``` python
 # initialize the model
 my_model = kNN()
 # obtain features and labels from files
 features, labels = preprocess(feature_file=..., label_file=...)
 # get class names (unique entries in labels)
 classes = np.unique(labels)
 # partition the data set
 val_indices, test_indices, train_indices = partition(size=..., t = 0.3, v = 0.1)
 ```

 %% Cell type:markdown id: tags:

 Assign a value to *k* and fit the *k*-NN model.

 %% Cell type:code id: tags:

 ``` python
 # pass the training features and labels to the fit method
 kwargs_f = {'metric': 'Euclidean'}
-my_model.fit(training_features=..., training_labels-..., classes, k=10, distance_f=..., **kwargs_f)
+my_model.fit(training_features=..., training_labels-..., k=10, distance_f=..., **kwargs_f)
 ```

 %% Cell type:markdown id: tags:

 ### Computing the confusion matrix for *k* = 10
 Now that we have the true labels and the predicted ones from our model, we can build a confusion matrix and see how accurate our model is. Implement the "conf_matrix" function (in model.ipynb) that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*). It should output a numpy.ndarray. You do not need to change the value of the threshold parameter yet.

 %% Cell type:code id: tags:

 ``` python
 # TODO

 # get model predictions
 pred_ratios = my_model.predict(features[test_indices])

-# For now, we will consider a data point as predicted in a class if more than 0.5
-# of its k-neighbors are in that class.
+# For now, we will consider a data point as predicted in positive class if more than 0.5
+# of its k-neighbors are positive examples.
 threshold = 0.5
 # convert predicted ratios to predicted labels
 pred_labels = None

 # show the distribution of predicted and true labels in a confusion matrix
 confusion = conf_matrix(...)
 confusion
 ```

 %% Cell type:markdown id: tags:

 Evaluate your model on the test data and report your **accuracy**. Also, calculate and report the 95% confidence interval on the generalization **error** estimate.

 %% Cell type:code id: tags:

 ``` python
 # TODO
 # Calculate and report accuracy and generalization error with confidence interval here. Show your work in this cell.

 print('Accuracy: {}'.format(accuracy))
 print('Confidence interval: {}-{}'.format(lower_bound, upper_bound))
 ```

 %% Cell type:markdown id: tags:

 ## TASK 4: Determining *k*

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Accuracies reported with various *k* values +5, +5
 * Confusion matrix for new *k* +5, +5

 %% Cell type:markdown id: tags:

 We can use the validation set to come up with a *k* value that results in better performance in terms of accuracy.

 Below calculate the accuracies for different values of *k* using the validation set. Report a good *k* value and use it in the analyses that follow this section. Report confusion matrix for the new value of *k*.

 %% Cell type:code id: tags:

 ``` python
 # TODO

 # Change values of k.
 # Calculate accuracies for the validation set.
 # Report a good k value.
 # Calculate the confusion matrix for new k.
 ```

 %% Cell type:markdown id: tags:

 ## TASK 5: ROC curve analysis
 * Correct implementation +25, +25

 %% Cell type:markdown id: tags:

 ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. Now, implement, in **model.ipynb**, a "ROC" function. "ROC" takes a list containing different threshold values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities.

 %% Cell type:markdown id: tags:

 Use the *k* value you found above, if you completed TASK 4, else use *k* = 10 to plot the ROC curve for values between 0.1 and 1.0.

 %% Cell type:code id: tags:

 ``` python
 # TODO
 # ROC curve
 roc_sens, roc_spec_ = ROC(true_labels=..., preds=..., np.arange(0.1, 1.0, 0.1))
-plt.plot(roc_sens, roc_spec_)
+plt.plot(roc_spec_,  roc_sens)
 plt.show()
 ```

 %% Cell type:markdown id: tags:

 ## TASK 6: Assess suitability of *k*-NN to your dataset
 * +10, +10

 %% Cell type:markdown id: tags:

 Insert a cell below to write about your understanding of why *k*-NN performed well if it did or why not if it didn't. What properties of the dataset could have affected the performance of the algorithm?

 %% Cell type:markdown id: tags:

 # *k*-Nearest Neighbor

 We'll implement *k*-Nearest Neighbor (*k*-NN) algorithm for this assignment. We will use the **madelon** dataset as in Programming Assignment 0.

 A skeleton of a general supervised learning model is provided in "model.ipynb". The functions that will be implemented there will be indicated in this notebook.

 ### Assignment Goals:
 In this assignment, we will:
 * implement 'Euclidean' and 'Manhattan' distance metrics
 * use the validation dataset to find a good value for *k*
 * evaluate our model with respect to performance measures:
    * accuracy, generalization error
    * confusion matrix
    *  Receiver Operating Characteristic (ROC) curve
-* try to assess if *k*-NN is suitable for the dataset you used
+* try to assess if *k*-NN is suitable for the dataset

 ## Note:

 You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.

 %% Cell type:markdown id: tags:

 # GRADING

 You will be graded on parts that are marked with **TODO** comments. Read the comments in the code to make sure you don't miss any.

 ### Mandatory for 478 & 878:

 |   | Tasks                      | 478 | 878 |
 |---|----------------------------|-----|-----|
 | 1 | Implement `distance`       |  15 |  15 |
 | 2 | Implement `k-NN` methods   |  35 |  30 |
 | 3 | Model evaluation           |  25 |  20 |
 | 5 | ROC curve analysis         |  25 |  25 |

 ### Mandatory for 878, bonus for 478

 |   | Tasks          | 478 | 878 |
 |---|----------------|-----|-----|
 | 4 | Optimizing *k* | 10  | 10  |

 ### Bonus for 478/878

 |   | Tasks          | 478 | 878 |
 |---|----------------|-----|-----|
 | 6 | Assess suitability of *k*-NN | 10  | 10  |

 Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 100 points in this assignment and extra 20 bonus points for 478 students and 10 bonus points for 878 students.

 %% Cell type:markdown id: tags:

 # YOUR GRADE

 ### Group Members:

 |   | Tasks                      | Points |
 |---|----------------------------|-----|
 | 1 | Implement `distance`       |     |
 | 2 | Implement `k-NN` methods   |     |
 | 3 | Model evaluation           |     |
 | 4 | Optimizing *k*             |     |
 | 5 | ROC curve analysis         |     |
 | 6 | Assess suitability of *k*-NN|    |



 %% Cell type:markdown id: tags:

 You can use numpy for array operations and matplotlib for plotting for this assignment. Please do not add other libraries.

 %% Cell type:code id: tags:

 ``` python
 import numpy as np
 import matplotlib.pyplot as plt
 ```

 %% Cell type:markdown id: tags:

 Following code makes the Model class and relevant functions available from model.ipynb.

 %% Cell type:code id: tags:

 ``` python
 %run 'model.ipynb'
 ```

 %% Cell type:markdown id: tags:

 ## TASK 1: Implement `distance` function

 %% Cell type:markdown id: tags:

 Choice of distance metric plays an important role in the performance of *k*-NN. Let's start with implementing a distance method  in the "distance" function in **model.ipynb**. It should take two data points and the name of the metric and return a scalar value.

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Euclidean +7.5, +7.5
 * Manhattan +7.5, +7.5

 %% Cell type:markdown id: tags:

 ### Test `distance`

 %% Cell type:code id: tags:

 ``` python
 x = np.array(range(100))
 y = np.array(range(100, 200))
 dist_euclidean = distance(x, y, 'Euclidean')
 dist_manhattan = distance(x, y, 'Manhattan')
 print('Euclidean distance: {}, Manhattan distance: {}'.format(dist_euclidean, dist_manhattan))
 ```

 %% Cell type:markdown id: tags:

 ## TASK 2: Implement *k*-NN Class Methods

 %% Cell type:markdown id: tags:

 We can start implementing our *k*-NN classifier. *k*-NN class inherits Model class. Use the "distance" function you defined above. "fit" method takes *k* as an argument. "predict" takes as input an *mxd* array containing *d*-dimensional *m* feature vectors for examples and for each input point outputs the predicted class and the ratio of positive examples in *k* nearest neighbors.

 %% Cell type:markdown id: tags:

 ### Rubric:
 * correct implementation of fit method +10, +10
 * correct implementation of predict method +25, +20

 %% Cell type:code id: tags:

 ``` python
 class kNN(Model):
    '''
    Inherits Model class. Implements the k-NN algorithm for classification.
    '''

-    def fit(self, training_features, training_labels, classes, k, distance_f,**kwargs):
+    def fit(self, training_features, training_labels, k, distance_f,**kwargs):
        '''
        Fit the model. This is pretty straightforward for k-NN.
        Args:
            training_features: ndarray
            training_labels: ndarray
-            classes: ndarray
-                1D array containing unique classes in the dataset
            k: int
            distance_f: function
            kwargs: dict
                Contains keyword arguments that will be passed to distance_f
        '''
        # TODO
-        # set self.train_features, self.train_labels, self.classes, self.k, self.distance_f, self.distance_metric
+        # set self.train_features, self.train_labels, self.k, self.distance_f, self.distance_metric

        raise NotImplementedError

        return


    def predict(self, test_features):
        '''
        Args:
            test_features: ndarray
                mxd array containing features for the points to be predicted
        Returns:
            preds: ndarray
-                mx2 array containing predicted class and proportion for each test point
+                mx1 array containing proportion of positive class among k nearest neighbors of each test point
        '''
        raise NotImplementedError

        # TODO

-        # for each point in test_features
+        # for each point
        # use your implementation of distance function
        #  distance_f(..., distance_metric)
        # to find the labels of k-nearest neighbors.

-        # you'll need proportion of the dominant class
+        # You'll need proportion of positive examples
        # in k nearest neighbors

        return preds

 ```

 %% Cell type:markdown id: tags:

 ## TASK 3: Build and Evaluate the Model

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Reasonable accuracy values +10, +5
 * Reasonable confidence intervals on the error estimate +10, +10
 * Reasonable confusion matrix +5, +5

 %% Cell type:markdown id: tags:

 Preprocess the data files and partition the data.

 %% Cell type:code id: tags:

 ``` python
 # initialize the model
 my_model = kNN()
 # obtain features and labels from files
 features, labels = preprocess(feature_file=..., label_file=...)
 # get class names (unique entries in labels)
 classes = np.unique(labels)
 # partition the data set
 val_indices, test_indices, train_indices = partition(size=..., t = 0.3, v = 0.1)
 ```

 %% Cell type:markdown id: tags:

 Assign a value to *k* and fit the *k*-NN model.

 %% Cell type:code id: tags:

 ``` python
 # pass the training features and labels to the fit method
 kwargs_f = {'metric': 'Euclidean'}
-my_model.fit(training_features=..., training_labels-..., classes, k=10, distance_f=..., **kwargs_f)
+my_model.fit(training_features=..., training_labels-..., k=10, distance_f=..., **kwargs_f)
 ```

 %% Cell type:markdown id: tags:

 ### Computing the confusion matrix for *k* = 10
 Now that we have the true labels and the predicted ones from our model, we can build a confusion matrix and see how accurate our model is. Implement the "conf_matrix" function (in model.ipynb) that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*). It should output a numpy.ndarray. You do not need to change the value of the threshold parameter yet.

 %% Cell type:code id: tags:

 ``` python
 # TODO

 # get model predictions
 pred_ratios = my_model.predict(features[test_indices])

-# For now, we will consider a data point as predicted in a class if more than 0.5
-# of its k-neighbors are in that class.
+# For now, we will consider a data point as predicted in positive class if more than 0.5
+# of its k-neighbors are positive examples.
 threshold = 0.5
 # convert predicted ratios to predicted labels
 pred_labels = None

 # show the distribution of predicted and true labels in a confusion matrix
 confusion = conf_matrix(...)
 confusion
 ```

 %% Cell type:markdown id: tags:

 Evaluate your model on the test data and report your **accuracy**. Also, calculate and report the 95% confidence interval on the generalization **error** estimate.

 %% Cell type:code id: tags:

 ``` python
 # TODO
 # Calculate and report accuracy and generalization error with confidence interval here. Show your work in this cell.

 print('Accuracy: {}'.format(accuracy))
 print('Confidence interval: {}-{}'.format(lower_bound, upper_bound))
 ```

 %% Cell type:markdown id: tags:

 ## TASK 4: Determining *k*

 %% Cell type:markdown id: tags:

 ### Rubric:
 * Accuracies reported with various *k* values +5, +5
 * Confusion matrix for new *k* +5, +5

 %% Cell type:markdown id: tags:

 We can use the validation set to come up with a *k* value that results in better performance in terms of accuracy.

 Below calculate the accuracies for different values of *k* using the validation set. Report a good *k* value and use it in the analyses that follow this section. Report confusion matrix for the new value of *k*.

 %% Cell type:code id: tags:

 ``` python
 # TODO

 # Change values of k.
 # Calculate accuracies for the validation set.
 # Report a good k value.
 # Calculate the confusion matrix for new k.
 ```

 %% Cell type:markdown id: tags:

 ## TASK 5: ROC curve analysis
 * Correct implementation +25, +25

 %% Cell type:markdown id: tags:

 ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. Now, implement, in **model.ipynb**, a "ROC" function. "ROC" takes a list containing different threshold values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities.

 %% Cell type:markdown id: tags:

 Use the *k* value you found above, if you completed TASK 4, else use *k* = 10 to plot the ROC curve for values between 0.1 and 1.0.

 %% Cell type:code id: tags:

 ``` python
 # TODO
 # ROC curve
 roc_sens, roc_spec_ = ROC(true_labels=..., preds=..., np.arange(0.1, 1.0, 0.1))
-plt.plot(roc_sens, roc_spec_)
+plt.plot(roc_spec_,  roc_sens)
 plt.show()
 ```

 %% Cell type:markdown id: tags:

 ## TASK 6: Assess suitability of *k*-NN to your dataset
 * +10, +10

 %% Cell type:markdown id: tags:

 Insert a cell below to write about your understanding of why *k*-NN performed well if it did or why not if it didn't. What properties of the dataset could have affected the performance of the algorithm?

--- a/ProgrammingAssignment_2/.gitkeep
+++ b/ProgrammingAssignment_2/.gitkeep
--- a/ProgrammingAssignment_2/ProgrammingAssignment2_LR.ipynb
+++ b/ProgrammingAssignment_2/ProgrammingAssignment2_LR.ipynb
+%% Cell type:markdown id: tags:
+
+# Linear Regression
+
+In the linear regression part of this assignment, we have a small dataset available to us. We won't have examples to spare for validation set, instead we'll use cross-validation to tune hyperparameters.
+
+### Assignment Goals:
+In this assignment, we will:
+* implement linear regression
+    * use gradient descent for optimization
+    * implement regularization techniques
+        * $l_1$/$l_2$ regularization
+        * use cross-validation to find a good regularization parameter $\lambda$
+
+### Note:
+
+You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.
+
+%% Cell type:markdown id: tags:
+
+# GRADING
+
+You will be graded on parts that are marked with **\#TODO** comments. Read the comments in the code to make sure you don't miss any.
+
+### Mandatory for 478 & 878:
+
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 1 | Implement `kfold`          | 20  |  20 |
+| 2 | Implement `mse`            |  10  |  10  |
+| 3 | Implement `fit` method     | 40  | 40  |
+| 4 | Implement `predict` method | 20  | 20  |
+| 5 | Implement `regularization` | 20  | 10   |
+
+### Bonus for 478 & 878
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 3 | `fit` (learning rate)       | 10  | 10  |
+| 6 | Polynomial regression      | 10   | 5   |
+| 7 | Grid search                | 10  | 5  |
+
+Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 140 points in this part of assignment 2 for 478 and 120 points for 878 students.
+
+%% Cell type:markdown id: tags:
+
+You can use numpy for array operations and matplotlib for plotting for this assignment. Please do not add other libraries.
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+%% Cell type:markdown id: tags:
+
+Following code makes the Model class and relevant functions available from "model.ipynb".
+
+%% Cell type:code id: tags:
+
+``` python
+%run 'model.ipynb'
+```
+
+%% Cell type:markdown id: tags:
+
+The target value (house prices in $1,000) is plotted against feature values below.
+
+%% Cell type:code id: tags:
+
+``` python
+features, feature_names, targets = preprocess('../data/housing.data', '../data/housing.names')
+print('There are {} examples with {} features.'.format(features.shape[0], features.shape[1]))
+
+%matplotlib inline
+fig, axs = plt.subplots(4, 4, figsize=(15, 15), facecolor='w', edgecolor='k')
+fig.subplots_adjust(hspace = 0.2, wspace=.20)
+
+# DISREGARD LAST 3 EMPTY PLOTS
+for index, feature_name in enumerate(feature_names):
+
+    axs[index//4][index %4].scatter(features[:, index], targets)
+    axs[index//4][index %4].set_xlabel(feature_name)
+
+fig.text(0.06, 0.5, 'House Value in $1000', ha='center', va='center', rotation='vertical', size=24)
+```
+
+%% Output
+
+    There are 506 examples with 13 features.
+
+    Text(0.06,0.5,'House Value in $1000')
+
+
+
+%% Cell type:markdown id: tags:
+
+## TASK 1: Implement `kfold`
+
+%% Cell type:markdown id: tags:
+
+Implement "kfold" function for $k$-fold cross-validation in "model.ipynb". 5 and 10 are commonly used values for $k$. You can use either one of them.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+ * No intersection between test and train parts +10, +10
+ * No intersection between test folds +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `kfold`
+
+%% Cell type:code id: tags:
+
+``` python
+# Obtain 5 splits of data.
+splits = kfold(targets.shape[0], k=5)
+
+# Check that test folds are completely different
+# Check that for a given i, train and test are completely different
+for i in range(5):
+    intersection = set(splits[i][0]).intersection (set(splits[i][1]))
+    if intersection:
+        print('Test-train splits intersect!')
+    for j in range(5):
+        if i!=j:
+            intersection = set(splits[i][1]).intersection (set(splits[j][1]))
+            if intersection:
+                print('Test splits intersect!')
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 2: Implement `mse`
+
+%% Cell type:markdown id: tags:
+
+We'll  use mean squared error (mse) for linear regression. Next, implement "mse" function in "model.ipynb" that takes predicted and true target values, and returns the "mse" between them.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+ * Correct mse +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `mse`
+
+%% Cell type:code id: tags:
+
+``` python
+mse(np.array([100, 300]), np.array([200, 400]))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASKS 3, 4, 5: Implement `fit`, `predict`, `regularization`
+
+%% Cell type:markdown id: tags:
+
+We can define our linear_regression model class now. Implement the "fit" and "predict" methods.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* fit without regularization +20, +20
+* learning rate interpretation +10, +10 (BONUS for both)
+* $l_1$ regularization +10, +5
+* $l_2$ regularization +10, +5
+* fit works with regularization +20, +20
+* predict +20, +20
+
+%% Cell type:code id: tags:
+
+``` python
+class Linear_Regression(Model):
+
+    # You can disregard regularizer and kwargs for TASK 3
+    def fit(self, X, Y, learning_rate = 0.001, epochs = 2000, regularizer=None, **kwargs):
+        '''
+        Args:
+            learning_rate: float
+                step size for parameter update
+            epochs: int
+                number of updates that will be performed
+            regularizer: str
+                one of l1 or l2
+            lambd: float
+                regularization coefficient
+        '''
+
+        # we will need to add a column of 1's for bias
+        size = X.shape[0]
+        ones = np.ones(size)
+        ones = np.reshape(ones, (size ,-1))
+        features = np.hstack((ones, X))
+
+
+        # theta_hat contains the parameters for the model
+        # initialize theta_hat as zeros
+        # one parameter for each feature and one for bias
+        theta_hat = np.zeros(X.shape[1])
+
+        # TODO
+
+        # for each epoch
+        for epoch in range(epochs):
+            # compute model predictions for training examples
+            y_hat = None
+
+            if regularizer = None:
+
+                # use mse function to find the cost
+                cost = mse(y_hat, Y)
+
+                # You can use below print statement to monitor cost
+                #print('Current cost is {}'.format(cost))
+                # calculate gradients wrt theta
+                grad_theta = None
+                # update theta
+                theta_hat = None
+                raise NotImplementedError
+
+            else:
+                # take regularization into account
+                # you will need to compute the gradient of the regularization term
+                raise NotImplementedError
+
+        # update the model parameters to be used in predict method
+        self.theta = theta_hat
+
+
+    def predict(self, test_features):
+
+        # obtain test features for current fold
+        # do not forget to add a column for bias
+        # as in fit method
+
+        # TODO
+
+        # get predictions from model
+        y_hat = None
+        raise NotImplementedError
+
+        return y_hat
+
+```
+
+%% Cell type:markdown id: tags:
+
+Initialize and fit the model. During training monitor your cost function. Experiment with different learning rates. Insert a cell below and summarize and briefly interpret your observations.
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize and fit the model
+my_model = Linear_Regression()
+# change lr to try different learning rates
+lr = 0.0001
+my_model.fit(features[splits[0][0]], targets[splits[0][0]], learning_rate = lr)
+```
+
+%% Cell type:markdown id: tags:
+
+Define "regularization" function which implements $l_1$ and $l_2$ regularization in "model.ipynb".
+
+%% Cell type:code id: tags:
+
+``` python
+weights = list(np.arange(0, 1.1 , 0.1))
+for method in ['l1', 'l2']:
+    print(regularization(weights, method=method))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 6: Polynomial Regression
+
+%% Cell type:markdown id: tags:
+
+Do you think the dataset would benefit from polynomial regression? Please briefly explain why or why not.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* Sound reasoning +10, +5
+
+%% Cell type:markdown id: tags:
+
+## TASK 7: Grid Search
+
+%% Cell type:markdown id: tags:
+
+Using cross-validation, try different values of $\lambda$ for $l_1$ and $l_2$ regularization to find good $\lambda$ values that result in low average _mse_.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* Different methods are tried with different values of $\lambda$ +10, +5
+
+%% Cell type:markdown id: tags:
+
+### Test: Grid Search
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize the model
+my_model = Linear_Regression()
+
+# two regularization methods
+for method in ['l1', 'l2']:
+    # different lambda
+    for lmbd in np.arange(0, 1, 0.1):
+
+        k_fold_mse = 0
+        fit_kwargs={'method': method}
+
+        for k in range(5):
+
+            # fit on training
+            my_model.fit(features[splits[k][0]], targets[splits[k][0]], lambd = lmbd)
+            # predict test
+            pred = my_model.predict(features[splits[k][1]])
+            k_fold_mse += mse(pred,targets[splits[k][1]])
+        print(k_fold_mse/5)
+```
+%% Cell type:markdown id: tags:
+
+# Linear Regression
+
+In the linear regression part of this assignment, we have a small dataset available to us. We won't have examples to spare for validation set, instead we'll use cross-validation to tune hyperparameters.
+
+### Assignment Goals:
+In this assignment, we will:
+* implement linear regression
+    * use gradient descent for optimization
+    * implement regularization techniques
+        * $l_1$/$l_2$ regularization
+        * use cross-validation to find a good regularization parameter $\lambda$
+
+### Note:
+
+You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.
+
+%% Cell type:markdown id: tags:
+
+# GRADING
+
+You will be graded on parts that are marked with **\#TODO** comments. Read the comments in the code to make sure you don't miss any.
+
+### Mandatory for 478 & 878:
+
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 1 | Implement `kfold`          | 20  |  20 |
+| 2 | Implement `mse`            |  10  |  10  |
+| 3 | Implement `fit` method     | 40  | 40  |
+| 4 | Implement `predict` method | 20  | 20  |
+| 5 | Implement `regularization` | 20  | 10   |
+
+### Bonus for 478 & 878
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 3 | `fit` (learning rate)       | 10  | 10  |
+| 6 | Polynomial regression      | 10   | 5   |
+| 7 | Grid search                | 10  | 5  |
+
+Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 140 points in this part of assignment 2 for 478 and 120 points for 878 students.
+
+%% Cell type:markdown id: tags:
+
+You can use numpy for array operations and matplotlib for plotting for this assignment. Please do not add other libraries.
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+%% Cell type:markdown id: tags:
+
+Following code makes the Model class and relevant functions available from "model.ipynb".
+
+%% Cell type:code id: tags:
+
+``` python
+%run 'model.ipynb'
+```
+
+%% Cell type:markdown id: tags:
+
+The target value (house prices in $1,000) is plotted against feature values below.
+
+%% Cell type:code id: tags:
+
+``` python
+features, feature_names, targets = preprocess('../data/housing.data', '../data/housing.names')
+print('There are {} examples with {} features.'.format(features.shape[0], features.shape[1]))
+
+%matplotlib inline
+fig, axs = plt.subplots(4, 4, figsize=(15, 15), facecolor='w', edgecolor='k')
+fig.subplots_adjust(hspace = 0.2, wspace=.20)
+
+# DISREGARD LAST 3 EMPTY PLOTS
+for index, feature_name in enumerate(feature_names):
+
+    axs[index//4][index %4].scatter(features[:, index], targets)
+    axs[index//4][index %4].set_xlabel(feature_name)
+
+fig.text(0.06, 0.5, 'House Value in $1000', ha='center', va='center', rotation='vertical', size=24)
+```
+
+%% Output
+
+    There are 506 examples with 13 features.
+
+    Text(0.06,0.5,'House Value in $1000')
+
+
+
+%% Cell type:markdown id: tags:
+
+## TASK 1: Implement `kfold`
+
+%% Cell type:markdown id: tags:
+
+Implement "kfold" function for $k$-fold cross-validation in "model.ipynb". 5 and 10 are commonly used values for $k$. You can use either one of them.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+ * No intersection between test and train parts +10, +10
+ * No intersection between test folds +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `kfold`
+
+%% Cell type:code id: tags:
+
+``` python
+# Obtain 5 splits of data.
+splits = kfold(targets.shape[0], k=5)
+
+# Check that test folds are completely different
+# Check that for a given i, train and test are completely different
+for i in range(5):
+    intersection = set(splits[i][0]).intersection (set(splits[i][1]))
+    if intersection:
+        print('Test-train splits intersect!')
+    for j in range(5):
+        if i!=j:
+            intersection = set(splits[i][1]).intersection (set(splits[j][1]))
+            if intersection:
+                print('Test splits intersect!')
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 2: Implement `mse`
+
+%% Cell type:markdown id: tags:
+
+We'll  use mean squared error (mse) for linear regression. Next, implement "mse" function in "model.ipynb" that takes predicted and true target values, and returns the "mse" between them.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+ * Correct mse +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `mse`
+
+%% Cell type:code id: tags:
+
+``` python
+mse(np.array([100, 300]), np.array([200, 400]))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASKS 3, 4, 5: Implement `fit`, `predict`, `regularization`
+
+%% Cell type:markdown id: tags:
+
+We can define our linear_regression model class now. Implement the "fit" and "predict" methods.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* fit without regularization +20, +20
+* learning rate interpretation +10, +10 (BONUS for both)
+* $l_1$ regularization +10, +5
+* $l_2$ regularization +10, +5
+* fit works with regularization +20, +20
+* predict +20, +20
+
+%% Cell type:code id: tags:
+
+``` python
+class Linear_Regression(Model):
+
+    # You can disregard regularizer and kwargs for TASK 3
+    def fit(self, X, Y, learning_rate = 0.001, epochs = 2000, regularizer=None, **kwargs):
+        '''
+        Args:
+            learning_rate: float
+                step size for parameter update
+            epochs: int
+                number of updates that will be performed
+            regularizer: str
+                one of l1 or l2
+            lambd: float
+                regularization coefficient
+        '''
+
+        # we will need to add a column of 1's for bias
+        size = X.shape[0]
+        ones = np.ones(size)
+        ones = np.reshape(ones, (size ,-1))
+        features = np.hstack((ones, X))
+
+
+        # theta_hat contains the parameters for the model
+        # initialize theta_hat as zeros
+        # one parameter for each feature and one for bias
+        theta_hat = np.zeros(X.shape[1])
+
+        # TODO
+
+        # for each epoch
+        for epoch in range(epochs):
+            # compute model predictions for training examples
+            y_hat = None
+
+            if regularizer = None:
+
+                # use mse function to find the cost
+                cost = mse(y_hat, Y)
+
+                # You can use below print statement to monitor cost
+                #print('Current cost is {}'.format(cost))
+                # calculate gradients wrt theta
+                grad_theta = None
+                # update theta
+                theta_hat = None
+                raise NotImplementedError
+
+            else:
+                # take regularization into account
+                # you will need to compute the gradient of the regularization term
+                raise NotImplementedError
+
+        # update the model parameters to be used in predict method
+        self.theta = theta_hat
+
+
+    def predict(self, test_features):
+
+        # obtain test features for current fold
+        # do not forget to add a column for bias
+        # as in fit method
+
+        # TODO
+
+        # get predictions from model
+        y_hat = None
+        raise NotImplementedError
+
+        return y_hat
+
+```
+
+%% Cell type:markdown id: tags:
+
+Initialize and fit the model. During training monitor your cost function. Experiment with different learning rates. Insert a cell below and summarize and briefly interpret your observations.
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize and fit the model
+my_model = Linear_Regression()
+# change lr to try different learning rates
+lr = 0.0001
+my_model.fit(features[splits[0][0]], targets[splits[0][0]], learning_rate = lr)
+```
+
+%% Cell type:markdown id: tags:
+
+Define "regularization" function which implements $l_1$ and $l_2$ regularization in "model.ipynb".
+
+%% Cell type:code id: tags:
+
+``` python
+weights = list(np.arange(0, 1.1 , 0.1))
+for method in ['l1', 'l2']:
+    print(regularization(weights, method=method))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 6: Polynomial Regression
+
+%% Cell type:markdown id: tags:
+
+Do you think the dataset would benefit from polynomial regression? Please briefly explain why or why not.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* Sound reasoning +10, +5
+
+%% Cell type:markdown id: tags:
+
+## TASK 7: Grid Search
+
+%% Cell type:markdown id: tags:
+
+Using cross-validation, try different values of $\lambda$ for $l_1$ and $l_2$ regularization to find good $\lambda$ values that result in low average _mse_.
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* Different methods are tried with different values of $\lambda$ +10, +5
+
+%% Cell type:markdown id: tags:
+
+### Test: Grid Search
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize the model
+my_model = Linear_Regression()
+
+# two regularization methods
+for method in ['l1', 'l2']:
+    # different lambda
+    for lmbd in np.arange(0, 1, 0.1):
+
+        k_fold_mse = 0
+        fit_kwargs={'method': method}
+
+        for k in range(5):
+
+            # fit on training
+            my_model.fit(features[splits[k][0]], targets[splits[k][0]], lambd = lmbd)
+            # predict test
+            pred = my_model.predict(features[splits[k][1]])
+            k_fold_mse += mse(pred,targets[splits[k][1]])
+        print(k_fold_mse/5)
+```
--- a/ProgrammingAssignment_2/model.ipynb
+++ b/ProgrammingAssignment_2/model.ipynb
+%% Cell type:markdown id: tags:
+
+# JUPYTER NOTEBOOK TIPS
+
+Each rectangular box is called a cell.
+* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
+* alt+ENTER evaluates the current cell and adds a new cell below it.
+* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
+
+%% Cell type:markdown id: tags:
+
+# Supervised Learning Model Skeleton
+
+We'll use this skeleton for implementing different supervised learning algorithms.
+
+%% Cell type:code id: tags:
+
+``` python
+class Model:
+
+    def fit(self):
+
+        raise NotImplementedError
+
+    def predict(self, test_points):
+        raise NotImplementedError
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def preprocess(data_f, feature_names_f):
+    '''
+    data_f: where to read the dataset from
+    feature_names_f: where to read the feature names from
+    Returns:
+        features: ndarray
+            nxd array containing `float` feature values
+        labels: ndarray
+            1D array containing `float` label
+    '''
+    # You might find np.genfromtxt useful for reading in the file. Be careful with the file delimiter,
+    # e.g. for comma-separated files use delimiter=',' argument.
+
+    data = np.genfromtxt(data_f)
+    features = data[:,:-1]
+    target = data[:,-1]
+    feature_names = np.genfromtxt(feature_names_f, dtype='unicode')
+
+    return features, feature_names, target
+```
+
+%% Cell type:markdown id: tags:
+
+In cases where data is not abundantly available, we resort to getting an error estimate from average of error on different splits of dataset. In this case, every fold of data is used for testing and for training in turns, i.e. assuming we split our data into 3 folds, we'd
+* train our model on fold-1+fold-2 and test on fold-3
+* train our model on fold-1+fold-3 and test on fold-2
+* train our model on fold-2+fold-3 and test on fold-1.
+
+We'd use the average of the error we obtained in three runs as our error estimate.
+
+Implement function "kfold" below.
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: Programming Assignment 2
+
+def kfold(size, k):
+
+    '''
+    Args:
+        size: int
+            number of examples in the dataset that you want to split into k
+        k: int
+            Number of desired splits in data.(Assume test set is already separated.)
+        Returns:
+        fold_dict: dict
+            A dictionary with integer keys corresponding to folds. Values are (training_indices, val_indices).
+
+        val_indices: ndarray
+            1/k of training indices randomly chosen and separates them as validation partition.
+        train_indices: ndarray
+            Remaining 1-(1/k) of the indices.
+
+            e.g. fold_dict = {0: (train_0_indices, val_0_indices),
+            1: (train_1_indices, val_1_indices), 2: (train_2_indices, val_2_indices)} for k = 3
+    '''
+
+    return fold_dict
+```
+
+%% Cell type:markdown id: tags:
+
+Implement "mse" and regularization functions. They will be used in the fit method of linear regression.
+
+%% Cell type:code id: tags:
+
+``` python
+#TODO: Programming Assignment 2
+def mse(y_pred, y_true):
+    '''
+    Args:
+        y_hat: ndarray
+            1D array containing data with `float` type. Values predicted by our method
+        y_true: ndarray
+            1D array containing data with `float` type. True y values
+    Returns:
+        cost: ndarray
+            1D array containing mean squared error between y_pred and y_true.
+
+    '''
+    raise NotImplementedError
+
+    return cost
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+#TODO: Programming Assignment 2
+def regularization(weights, method):
+    '''
+    Args:
+        weights: ndarray
+            1D array with `float` entries
+        method: str
+    Returns:
+        value: float
+            A single value. Regularization term that will be used in cost function in fit.
+    '''
+    if method == "l1":
+        value = None
+    elif method == "l2":
+        value = None
+    raise NotImplementedError
+    return value
+```
+%% Cell type:markdown id: tags:
+
+# JUPYTER NOTEBOOK TIPS
+
+Each rectangular box is called a cell.
+* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
+* alt+ENTER evaluates the current cell and adds a new cell below it.
+* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
+
+%% Cell type:markdown id: tags:
+
+# Supervised Learning Model Skeleton
+
+We'll use this skeleton for implementing different supervised learning algorithms.
+
+%% Cell type:code id: tags:
+
+``` python
+class Model:
+
+    def fit(self):
+
+        raise NotImplementedError
+
+    def predict(self, test_points):
+        raise NotImplementedError
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def preprocess(data_f, feature_names_f):
+    '''
+    data_f: where to read the dataset from
+    feature_names_f: where to read the feature names from
+    Returns:
+        features: ndarray
+            nxd array containing `float` feature values
+        labels: ndarray
+            1D array containing `float` label
+    '''
+    # You might find np.genfromtxt useful for reading in the file. Be careful with the file delimiter,
+    # e.g. for comma-separated files use delimiter=',' argument.
+
+    data = np.genfromtxt(data_f)
+    features = data[:,:-1]
+    target = data[:,-1]
+    feature_names = np.genfromtxt(feature_names_f, dtype='unicode')
+
+    return features, feature_names, target
+```
+
+%% Cell type:markdown id: tags:
+
+In cases where data is not abundantly available, we resort to getting an error estimate from average of error on different splits of dataset. In this case, every fold of data is used for testing and for training in turns, i.e. assuming we split our data into 3 folds, we'd
+* train our model on fold-1+fold-2 and test on fold-3
+* train our model on fold-1+fold-3 and test on fold-2
+* train our model on fold-2+fold-3 and test on fold-1.
+
+We'd use the average of the error we obtained in three runs as our error estimate.
+
+Implement function "kfold" below.
+
+%% Cell type:code id: tags:
+
+``` python
+# TODO: Programming Assignment 2
+
+def kfold(size, k):
+
+    '''
+    Args:
+        size: int
+            number of examples in the dataset that you want to split into k
+        k: int
+            Number of desired splits in data.(Assume test set is already separated.)
+        Returns:
+        fold_dict: dict
+            A dictionary with integer keys corresponding to folds. Values are (training_indices, val_indices).
+
+        val_indices: ndarray
+            1/k of training indices randomly chosen and separates them as validation partition.
+        train_indices: ndarray
+            Remaining 1-(1/k) of the indices.
+
+            e.g. fold_dict = {0: (train_0_indices, val_0_indices),
+            1: (train_1_indices, val_1_indices), 2: (train_2_indices, val_2_indices)} for k = 3
+    '''
+
+    return fold_dict
+```
+
+%% Cell type:markdown id: tags:
+
+Implement "mse" and regularization functions. They will be used in the fit method of linear regression.
+
+%% Cell type:code id: tags:
+
+``` python
+#TODO: Programming Assignment 2
+def mse(y_pred, y_true):
+    '''
+    Args:
+        y_hat: ndarray
+            1D array containing data with `float` type. Values predicted by our method
+        y_true: ndarray
+            1D array containing data with `float` type. True y values
+    Returns:
+        cost: ndarray
+            1D array containing mean squared error between y_pred and y_true.
+
+    '''
+    raise NotImplementedError
+
+    return cost
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+#TODO: Programming Assignment 2
+def regularization(weights, method):
+    '''
+    Args:
+        weights: ndarray
+            1D array with `float` entries
+        method: str
+    Returns:
+        value: float
+            A single value. Regularization term that will be used in cost function in fit.
+    '''
+    if method == "l1":
+        value = None
+    elif method == "l2":
+        value = None
+    raise NotImplementedError
+    return value
+```
--- a/ProgrammingAssignment_3/.gitkeep
+++ b/ProgrammingAssignment_3/.gitkeep
--- a/ProgrammingAssignment_3/ProgrammingAssignment3_NB.ipynb
+++ b/ProgrammingAssignment_3/ProgrammingAssignment3_NB.ipynb
+%% Cell type:markdown id: tags:
+
+# Naive Bayes Spam Classifier
+
+In this part of the assignment, we will
+
+* implement a Naive Bayes spam classifier
+    * address sparse data problem with **pseudocounts** (**$m$-estimate**)
+
+A skeleton of a general supervised learning model is provided in "model.ipynb". We won't make any implementations for this part of homework 3. A confusion matrix implementation is provided for you in "model.ipynb".
+
+### Note:
+
+You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.
+
+%% Cell type:markdown id: tags:
+
+# GRADING
+
+You will be graded on parts that are marked with **\#TODO** comments. Read the comments in the code to make sure you don't miss any.
+
+### Mandatory for 478 & 878:
+
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 1 | Implement `fit` method     | 25  |  25 |
+| 2 | Implement `predict` method |  25 |  25 |
+
+Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 50 points in this part of assignment 2 for both 478 and 878 students.
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import json
+%run 'model.ipynb'
+```
+
+%% Cell type:markdown id: tags:
+
+We will use the Enron spam/ham dataset for spam filtering. The emails are already tokenized. Below code reads in the processed data. There are 33,702 emails: 17157 spam and 16545 ham.
+
+**Please do not change the order of the test indices as you'll be graded on results for the first 5 test examples.**
+
+%% Cell type:code id: tags:
+
+``` python
+# load tokenized email texts
+with open('../data/enron_text.json') as f:
+    X = json.load(f)
+# load email labels
+with open('../data/enron_label.json') as f:
+    Y = json.load(f)
+with open('../data/enron_split.json') as f:
+    indices = json.load(f)
+
+indices.keys()
+```
+
+%% Output
+
+    dict_keys(['training', 'test'])
+
+%% Cell type:markdown id: tags:
+
+X is a list of lists. Each of its 33,702 entries correspond to a tokenized email. Y is a list. Each entry is a label for the tokenized email at the corresponding position in X. 1 denotes spam, 0 denotes ham. Below are 5 random emails and their corresponding labels.
+
+%% Cell type:code id: tags:
+
+``` python
+# number of emails
+size = len(Y)
+# randomly select and print some emails
+ind_list = np.random.choice(size, 5)
+for ind in ind_list:
+    print('Tokenized text: {}'.format(X[ind]))
+    print('Label: {}'.format(Y[ind]))
+```
+
+%% Output
+
+    Tokenized text: ['subject', 're', 'power', 'crisis', 'in', 'the', 'west', 'tim', 'belden', 's', 'office', 'referred', 'me', 'to', 'you', 'has', 'grant', 'masson', 'been', 'replaced', 'original', 'message', 'from', 'vince', 'j', 'kaminski', 'enron', 'com', 'mailto', 'vince', 'j', 'kaminski', 'enron', 'com', 'sent', 'monday', 'november', '06', '2000', '11', '00', 'am', 'to', 'niam', 'infocastinc', 'com', 'subject', 're', 'power', 'crisis', 'in', 'the', 'west', 'nia', 'please', 'contact', 'tim', 'belden', 'in', 'our', 'portland', 'office', 'his', 'phone', 'number', 'is', '503', '464', '3820', 'vince', 'nia', 'mansell', 'on', '11', '03', '2000', '12', '46', '52', 'pm', 'to', 'vkamins', 'enron', 'com', 'cc', 'subject', 'power', 'crisis', 'in', 'the', 'west', 'dear', 'vince', 'i', 'spoke', 'with', 'you', 'briefly', 'yesterday', 'regarding', 'grant', 'masson', 'you', 'informed', 'me', 'that', 'he', 'is', 'no', 'longer', 'an', 'enron', 'employee', 'i', 'have', 'also', 'been', 'informed', 'that', 'grant', 'has', 'not', 'yet', 'been', 'replaced', 'i', 'am', 'inquiring', 'because', 'infocast', 'would', 'like', 'to', 'have', 'an', 'enron', 'representative', 'speak', 'at', 'an', 'upcoming', 'conference', 'entitled', 'power', 'crisis', 'in', 'the', 'west', 'status', 'it', 'is', 'certainly', 'going', 'to', 'be', 'an', 'exciting', 'conference', 'due', 'to', 'all', 'of', 'the', 'controversy', 'surrounding', 'the', 'situation', 'in', 'san', 'diego', 'kind', 'regards', 'nia', 'mansell', 'infocast', 'conference', 'manager', '818', '888', '4445', 'ext', '45', '818', '888', '4440', 'fax', 'niam', 'com', 'see', 'attached', 'file', 'power', 'crisis', 'in', 'the', 'west', 'invite', 'doc', '']
+    Label: 0
+    Tokenized text: ['subject', 're', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'fyi', 'forwarded', 'by', 'karen', 'marshall', 'hou', 'ect', 'on', '01', '18', '2001', '03', '07', 'pm', 'angela', 'dorsey', 'on', '01', '18', '2001', '02', '53', '59', 'pm', 'to', 'cc', 'subject', 're', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'karen', 'thanks', 'for', 'the', 'extra', 'support', 'in', 'getting', 'the', 'word', 'out', 'i', 've', 'had', 'a', 'couple', 'rsvp', 's', 'from', 'enron', 'sincerely', 'angela', 'original', 'message', 'from', 'karen', 'marshall', 'enron', 'com', 'mailto', 'karen', 'marshall', 'enron', 'com', 'sent', 'wednesday', 'january', '17', '2001', '7', '59', 'pm', 'to', 'david', 'haug', 'enron', 'com', 'gary', 'hickerson', 'enron', 'com', 'cchilde', 'enron', 'com', 'thomas', 'suffield', 'enron', 'com', 'ben', 'f', 'glisan', 'enron', 'com', 'ermes', 'melinchon', 'enron', 'com', 'hal', 'elrod', 'enron', 'com', 'clay', 'spears', 'enron', 'com', 'kelly', 'mahmoud', 'enron', 'com', 'ellen', 'fowler', 'enron', 'com', 'kevin', 'kuykendall', 'enron', 'com', 'fred', 'mitro', 'enron', 'com', 'kyle', 'kettler', 'enron', 'com', 'jeff', 'bartlett', 'enron', 'com', 'paul', 'j', 'broderick', 'enron', 'com', 'john', 'house', 'enron', 'com', 'george', 'mccormick', 'enron', 'com', 'guido', 'caranti', 'enron', 'com', 'ken', 'sissingh', 'enron', 'com', 'gwynn', 'gorsuch', 'enron', 'com', 'mark', 'gandy', 'enron', 'com', 'shawn', 'cumberland', 'enron', 'com', 'jennifer', 'martinez', 'enron', 'com', 'sean', 'keenan', 'enron', 'com', 'webb', 'jennings', 'enron', 'com', 'brian', 'hendon', 'enron', 'com', 'billy', 'braddock', 'enron', 'com', 'paul', 'burkhart', 'enron', 'com', 'garrett', 'tripp', 'enron', 'com', 'john', 'massey', 'enron', 'com', 'v', 'charles', 'weldon', 'enron', 'com', 'phayes', 'enron', 'com', 'ross', 'mesquita', 'enron', 'com', 'david', 'mitchell', 'enron', 'com', 'brian', 'kerrigan', 'enron', 'com', 'mark', 'gandy', 'enron', 'com', 'jennifer', 'martinez', 'enron', 'com', 'sean', 'keenan', 'enron', 'com', 'webb', 'jennings', 'enron', 'com', 'brian', 'hendon', 'enron', 'com', 'billy', 'braddock', 'enron', 'com', 'garrett', 'tripp', 'enron', 'com', 'john', 'massey', 'enron', 'com', 'v', 'charles', 'weldon', 'enron', 'com', 'phayes', 'enron', 'com', 'ross', 'mesquita', 'enron', 'com', 'david', 'mitchell', 'enron', 'com', 'christie', 'patrick', 'enron', 'com', 'michael', 'b', 'rosen', 'enron', 'com', 'cindy', 'derecskey', 'enron', 'com', 'cc', 'elyse', 'kalmans', 'enron', 'com', 'richard', 'causey', 'enron', 'com', 'sally', 'beck', 'enron', 'com', 'vince', 'j', 'kaminski', 'enron', 'com', 'jeffrey', 'a', 'shankman', 'enron', 'com', 'angela', 'dorsey', 'subject', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'the', '500', 'registration', 'fee', 'is', 'waived', 'for', 'any', 'enron', 'employee', 'who', 'wishes', 'to', 'attend', 'this', 'conference', 'because', 'of', 'our', 'relationship', 'with', 'the', 'school', 'please', 'forward', 'this', 'information', 'to', 'your', 'managers', 'and', 'staff', 'members', 'who', 'would', 'benefit', 'from', 'participating', 'in', 'this', 'important', 'conference', 'note', 'vince', 'kaminski', 'is', 'a', 'panellist', 'for', 'the', 'risk', 'management', 'session', '3', 'please', 'note', 'the', 'deadline', 'for', 'rsvp', 'hotel', 'reservations', 'is', 'monday', 'january', '22', 'nd', 'don', 't', 'miss', 'this', 'opportunity', 'should', 'you', 'have', 'any', 'questions', 'please', 'feel', 'free', 'to', 'contact', 'me', 'at', 'ext', '37632', 'karen', 'forwarded', 'by', 'karen', 'marshall', 'hou', 'ect', 'on', '01', '11', '2001', '07', '38', 'pm', 'angela', 'dorsey', 'on', '01', '10', '2001', '03', '06', '18', 'pm', 'to', 'angela', 'dorsey', 'cc', 'ehud', 'ronn', 'sheridan', 'titman', 'e', 'mail', 'subject', 'invitation', 'to', '2001', 'energy', 'finance', 'conference', 'the', 'university', 'of', 'texas', 'at', 'austin', 'colleagues', 'and', 'friends', 'of', 'the', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'cefer', 'happy', 'new', 'year', 'hope', 'you', 'all', 'had', 'a', 'wonderful', 'holiday', 'season', 'on', 'behalf', 'of', 'the', 'university', 'of', 'texas', 'finance', 'department', 'and', 'cefer', 'we', 'would', 'like', 'to', 'cordially', 'invite', 'you', 'to', 'attend', 'our', '2001', 'energy', 'finance', 'conference', 'austin', 'texas', 'february', '22', '23', '2001', 'hosted', 'by', 'the', 'university', 'of', 'texas', 'finance', 'department', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'dr', 'ehud', 'i', 'ronn', 'and', 'dr', 'sheridan', 'titman', 'are', 'currently', 'in', 'the', 'process', 'of', 'finalizing', 'the', 'details', 'of', 'the', 'conference', 'agenda', 'we', 'have', 'listed', 'the', 'agenda', 'outline', 'below', 'to', 'assist', 'you', 'in', 'your', 'travel', 'planning', 'each', 'conference', 'session', 'will', 'be', 'composed', 'of', 'a', 'panel', 'discussion', 'between', '3', '4', 'guest', 'speakers', 'on', 'the', 'designated', 'topic', 'as', 'supporters', 'of', 'the', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'representatives', 'of', 'our', 'trustee', 'corporations', 'enron', 'el', 'paso', 'reliant', 'conoco', 'and', 'southern', 'will', 'have', 'the', '500', 'conference', 'fee', 'waived', 'the', 'conference', 'package', 'includes', 'thursday', 'evening', 's', 'cocktails', 'dinner', 'and', 'hotel', 'ut', 'shuttle', 'service', 'as', 'well', 'as', 'friday', 's', 'conference', 'meals', 'session', 'materials', 'and', 'shuttle', 'service', 'travel', 'to', 'austin', 'and', 'hotel', 'reservations', 'are', 'each', 'participant', 's', 'responsibility', 'a', 'limited', 'number', 'of', 'hotel', 'rooms', 'are', 'being', 'tentatively', 'held', 'at', 'the', 'radisson', 'hotel', 'on', 'town', 'lake', 'under', 'the', 'group', 'name', 'university', 'of', 'texas', 'finance', 'department', 'for', 'the', 'nights', 'of', 'thursday', '2', '22', '01', 'and', 'friday', '2', '23', '01', 'the', 'latter', 'evening', 'for', 'those', 'who', 'choose', 'to', 'stay', 'in', 'austin', 'after', 'the', 'conference', 's', 'conclusion', 'to', 'guarantee', 'room', 'reservations', 'you', 'will', 'need', 'to', 'contact', 'the', 'radisson', 'hotel', 'at', '512', '478', '9611', 'no', 'later', 'than', 'monday', 'january', '22', 'nd', 'and', 'make', 'your', 'reservations', 'with', 'a', 'credit', 'card', 'please', 'let', 'me', 'know', 'when', 'you', 'have', 'made', 'those', 'arrangements', 'so', 'that', 'i', 'can', 'make', 'sure', 'the', 'radisson', 'gives', 'you', 'the', 'special', 'room', 'rate', 'of', '129', 'night', 'please', 'rsvp', 'your', 'interest', 'in', 'attending', 'this', 'conference', 'no', 'later', 'than', 'january', '22', 'nd', 'to', 'angela', 'dorsey', 'bus', 'utexas', 'edu', 'or', '512', '232', '7386', 'as', 'seating', 'availability', 'is', 'limited', 'please', 'feel', 'free', 'to', 'extend', 'this', 'invitation', 'to', 'your', 'colleagues', 'who', 'might', 'be', 'interested', 'in', 'attending', 'this', 'conference', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'program', 'of', 'the', '2001', 'energy', 'finance', 'conference', 'february', '22', '23', '2001', 'thursday', 'feb', '22', '3', '00', 'p', 'm', 'reserved', 'rooms', 'at', 'the', 'radisson', 'hotel', 'available', 'for', 'check', 'in', '5', '30', 'p', 'm', 'bus', 'will', 'pick', 'up', 'guests', 'at', 'the', 'radisson', 'for', 'transport', 'to', 'ut', 'club', '6', '00', 'p', 'm', 'cocktails', 'ut', 'club', '9', 'th', 'floor', '7', '00', 'p', 'm', 'dinner', 'ut', 'club', '8', '00', 'p', 'm', 'keynote', 'speaker', '9', '00', 'p', 'm', 'bus', 'will', 'transport', 'guests', 'back', 'to', 'hotel', 'friday', 'feb', '23', '7', '45', 'a', 'm', 'bus', 'will', 'pick', 'up', 'at', 'the', 'radisson', 'for', 'transport', 'to', 'ut', '8', '30', 'a', 'm', 'session', '1', 'real', 'options', 'panelists', 'jim', 'dyer', 'ut', 'chair', 'sheridan', 'titman', 'ut', 'john', 'mccormack', 'stern', 'stewart', 'co', '10', '00', 'a', 'm', 'coffee', 'break', '10', '15', 'a', 'm', 'session', '2', 'deregulation', 'panelists', 'david', 'eaton', 'ut', 'chair', 'david', 'spence', 'ut', 'jeff', 'sandefer', 'sandefer', 'capital', 'partners', 'ut', 'peter', 'nance', 'teknecon', 'energy', 'risk', 'advisors', '11', '45', 'a', 'm', 'catered', 'lunch', 'keynote', 'speaker', '1', '30', 'p', 'm', 'guest', 'tour', 'eds', 'financial', 'trading', 'technology', 'center', '2', '00', 'p', 'm', 'session', '3', 'risk', 'management', 'panelists', 'keith', 'brown', 'ut', 'chair', 'vince', 'kaminski', 'enron', 'alexander', 'eydeland', 'southern', 'co', 'ehud', 'i', 'ronn', 'ut', '3', '30', 'p', 'm', 'snack', 'break', '3', '45', 'p', 'm', 'session', '4', 'globalization', 'of', 'the', 'energy', 'business', 'panelists', 'laura', 'starks', 'ut', 'chair', 'bob', 'goldman', 'conoco', 'ray', 'hill', 'southern', 'co', '5', '15', 'p', 'm', 'wrap', 'up', '5', '30', 'p', 'm', 'bus', 'picks', 'up', 'for', 'transport', 'to', 'airport', 'dinner', '6', '30', 'p', 'm', 'working', 'dinner', 'for', 'senior', 'officers', 'of', 'energy', 'finance', 'center', 'trustees', 'we', 'have', 'made', 'arrangements', 'to', 'provide', 'shuttle', 'service', 'between', 'the', 'radisson', 'hotel', 'and', 'ut', 'during', 'the', 'conference', 'however', 'if', 'you', 'choose', 'to', 'stay', 'at', 'an', 'alternative', 'hotel', 'then', 'transportation', 'to', 'conference', 'events', 'will', 'become', 'your', 'responsibility', 'angela', 'dorsey', 'assistant', 'director', 'center', 'for', 'energy', 'finance', 'education', 'research', 'the', 'university', 'of', 'texas', 'at', 'austin', 'department', 'of', 'finance', 'cba', '6', '222', 'austin', 'tx', '78712', 'angela', 'dorsey', 'bus', 'utexas', 'edu', '']
+    Label: 0
+    Tokenized text: ['subject', 'interview', 'jaesoo', 'lew', '10', '25', '00', 'attached', 'please', 'find', 'the', 'resume', 'interview', 'schedule', 'and', 'evaluation', 'form', 'for', 'jaesoo', 'lew', 'jaesoo', 'will', 'be', 'interviewing', 'with', 'vince', 'kaminski', 's', 'group', 'on', 'an', 'exploratory', 'basis', 'on', 'october', '25', '2000', 'please', 'contact', 'me', 'with', 'any', 'comments', 'or', 'concerns', 'thank', 'you', 'cheryl', 'arguijo', 'ena', 'recruiting', '713', '345', '4016']
+    Label: 0
+    Tokenized text: ['subject', 'custom', 'marketing', 'to', 'webmaster', 'ezmlm', 'org', 'email', 'is', 'the', 'best', 'promote', 'tool', 'we', 'offer', 'online', 'marketing', 'with', 'quality', 'service', '1', 'target', 'email', 'list', 'we', 'can', 'provide', 'target', 'email', 'list', 'you', 'need', 'which', 'are', 'compiled', 'only', 'on', 'your', 'order', 'we', 'will', 'customize', 'your', 'client', 'email', 'list', 'we', 'have', 'millions', 'of', 'lists', 'in', 'a', 'wide', 'variety', 'of', 'categories', '2', 'send', 'out', 'target', 'list', 'for', 'you', 'we', 'can', 'send', 'your', 'email', 'message', 'to', 'your', 'target', 'clients', 'we', 'will', 'customize', 'your', 'email', 'list', 'and', 'send', 'your', 'message', 'for', 'you', 'our', 'site', 'www', 'marketingforus', 'com', 'we', 'also', 'offer', 'web', 'hosting', 'mail', 'server', 'regards', 'jason', 'marketing', 'support', 'sales', 'marketingforus', 'com', 'no', 'thanks', 'byebye', 'msn', 'com', 'subject', 'webmaster', 'ezmlm', 'org']
+    Label: 1
+    Tokenized text: ['subject', 'popular', 'software', 'at', 'low', 'low', 'prices', 'alaina', 'windows', 'xp', 'professional', '2002', '50', 'adobe', 'photoshop', '7', '0', '50', 'microsoft', 'office', 'xp', 'professional', '2002', '50', 'corel', 'draw', 'graphics', 'suite', '11', '50', '']
+    Label: 1
+
+%% Cell type:markdown id: tags:
+
+## TASK 1: Implement `fit`
+Implement the "fit" and "predict" methods for Naive Bayes. Use $m$-estimate to address missing attribute values (also called **Laplace smoothing** when $m$ = 1). In general, $m$ values should be small. We'll use $m$ = 1. We'll use log probabilities to avoid overflow.
+
+%% Cell type:code id: tags:
+
+``` python
+class Naive_Bayes(Model):
+    def __init__(self, m):
+        '''
+        Args:
+            m: int
+                Specifies the smoothing parameter
+        '''
+        self.m = m
+
+    def fit(self, X, Y):
+        '''
+        Args:
+            X: list
+                list of lists where each entry is a tokenized email text
+            Y: ndarray
+                1D array of true labels. 1: spam, 0: ham
+        '''
+
+        #TODO
+
+        # Replace Nones, empty lists and dictionaries below
+
+        # List containing all distinct words in all emails
+        # A list might not be the best data structure for obtaining
+        # the vocabulary.
+        # Use a temporary more efficient data structure
+        # then populate self.vocabulary.
+
+        self.vocabulary = []
+
+        # find *log* class prior probabilities
+        self.prior = {'spam': None, 'ham': None}
+
+        # find the number of words(counting repeats) summed across all emails in a class
+        self.total_count = {'spam': None, 'ham': None}
+
+        # find the number of each word summed across all emails in a class
+        # populate self.word_counts
+        # self.word_counts['spam'] is a dict with words as keys.
+        self.word_counts = {'spam': {}, 'ham': {}}
+
+
+
+    def predict(self, X):
+        '''
+        Args:
+            X: list
+                list of lists where each entry is a tokenized email text
+        Returns:
+            probs: ndarray
+                mx2 array containing unnormalized log posteriors for spam and ham (for grading purposes)
+            preds: ndarray
+                1D binary array containing predicted labels
+        '''
+        preds = []
+        probs = []
+
+        #TODO
+
+        # use the attributes calculated in fit to compute unnormalized class posterior probabilities
+        # and predicted labels
+
+        raise NotImplementedError
+
+        return np.array(probs), np.array(preds)
+
+```
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* correct vocabulary length +5,+5
+* correct log class priors +10, +10
+* correct word counts for the 5 most frequent words in each class +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `fit`
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize the model
+my_model = Naive_Bayes(...)
+
+# pass the training emails and labels
+my_model.fit(X=..., Y=...)
+
+# display the most frequent 5 words in both classes
+for cl in ['ham', 'spam']:
+    srt = sorted(my_model.word_counts[cl].items() ,  key=lambda x: x[1], reverse=True)
+    print('\n{} log prior: {}'.format(cl, my_model.prior[cl]))
+    print('5 most frequent words:')
+    print(srt[:5])
+print('\nVocabulary has {} distinct words'.format(len(my_model.vocabulary)))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 2: Implement `predict`
+Print the unnormalized log posteriors for the first five examples. We can use the "conf_matrix" function to see how error is distributed.
+
+### Rubric:
+* Correct unnormalized log posteriors +20, +20
+* Correct confusion matrix +5, +5
+
+### Test `predict`
+
+%% Cell type:code id: tags:
+
+``` python
+probs, preds = my_model.predict(X=...)
+print ('\nUnnormalized log posteriors of first 5 test examples:')
+print (probs [:5])
+tp,tn, fp, fn = conf_matrix(true = ..., pred = preds)
+print('tp: {}, tn: {}, fp: {}, fn:{}'.format(tp, tn, fp, fn))
+```
+%% Cell type:markdown id: tags:
+
+# Naive Bayes Spam Classifier
+
+In this part of the assignment, we will
+
+* implement a Naive Bayes spam classifier
+    * address sparse data problem with **pseudocounts** (**$m$-estimate**)
+
+A skeleton of a general supervised learning model is provided in "model.ipynb". We won't make any implementations for this part of homework 3. A confusion matrix implementation is provided for you in "model.ipynb".
+
+### Note:
+
+You are not required to follow this exact template. You can change what parameters your functions take or partition the tasks across functions differently. However, make sure there are outputs and implementation for items listed in the rubric for each task. Also, indicate in code with comments which task you are attempting.
+
+%% Cell type:markdown id: tags:
+
+# GRADING
+
+You will be graded on parts that are marked with **\#TODO** comments. Read the comments in the code to make sure you don't miss any.
+
+### Mandatory for 478 & 878:
+
+|   | Tasks                      | 478 | 878 |
+|---|----------------------------|-----|-----|
+| 1 | Implement `fit` method     | 25  |  25 |
+| 2 | Implement `predict` method |  25 |  25 |
+
+Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There are a total of 50 points in this part of assignment 2 for both 478 and 878 students.
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import json
+%run 'model.ipynb'
+```
+
+%% Cell type:markdown id: tags:
+
+We will use the Enron spam/ham dataset for spam filtering. The emails are already tokenized. Below code reads in the processed data. There are 33,702 emails: 17157 spam and 16545 ham.
+
+**Please do not change the order of the test indices as you'll be graded on results for the first 5 test examples.**
+
+%% Cell type:code id: tags:
+
+``` python
+# load tokenized email texts
+with open('../data/enron_text.json') as f:
+    X = json.load(f)
+# load email labels
+with open('../data/enron_label.json') as f:
+    Y = json.load(f)
+with open('../data/enron_split.json') as f:
+    indices = json.load(f)
+
+indices.keys()
+```
+
+%% Output
+
+    dict_keys(['training', 'test'])
+
+%% Cell type:markdown id: tags:
+
+X is a list of lists. Each of its 33,702 entries correspond to a tokenized email. Y is a list. Each entry is a label for the tokenized email at the corresponding position in X. 1 denotes spam, 0 denotes ham. Below are 5 random emails and their corresponding labels.
+
+%% Cell type:code id: tags:
+
+``` python
+# number of emails
+size = len(Y)
+# randomly select and print some emails
+ind_list = np.random.choice(size, 5)
+for ind in ind_list:
+    print('Tokenized text: {}'.format(X[ind]))
+    print('Label: {}'.format(Y[ind]))
+```
+
+%% Output
+
+    Tokenized text: ['subject', 're', 'power', 'crisis', 'in', 'the', 'west', 'tim', 'belden', 's', 'office', 'referred', 'me', 'to', 'you', 'has', 'grant', 'masson', 'been', 'replaced', 'original', 'message', 'from', 'vince', 'j', 'kaminski', 'enron', 'com', 'mailto', 'vince', 'j', 'kaminski', 'enron', 'com', 'sent', 'monday', 'november', '06', '2000', '11', '00', 'am', 'to', 'niam', 'infocastinc', 'com', 'subject', 're', 'power', 'crisis', 'in', 'the', 'west', 'nia', 'please', 'contact', 'tim', 'belden', 'in', 'our', 'portland', 'office', 'his', 'phone', 'number', 'is', '503', '464', '3820', 'vince', 'nia', 'mansell', 'on', '11', '03', '2000', '12', '46', '52', 'pm', 'to', 'vkamins', 'enron', 'com', 'cc', 'subject', 'power', 'crisis', 'in', 'the', 'west', 'dear', 'vince', 'i', 'spoke', 'with', 'you', 'briefly', 'yesterday', 'regarding', 'grant', 'masson', 'you', 'informed', 'me', 'that', 'he', 'is', 'no', 'longer', 'an', 'enron', 'employee', 'i', 'have', 'also', 'been', 'informed', 'that', 'grant', 'has', 'not', 'yet', 'been', 'replaced', 'i', 'am', 'inquiring', 'because', 'infocast', 'would', 'like', 'to', 'have', 'an', 'enron', 'representative', 'speak', 'at', 'an', 'upcoming', 'conference', 'entitled', 'power', 'crisis', 'in', 'the', 'west', 'status', 'it', 'is', 'certainly', 'going', 'to', 'be', 'an', 'exciting', 'conference', 'due', 'to', 'all', 'of', 'the', 'controversy', 'surrounding', 'the', 'situation', 'in', 'san', 'diego', 'kind', 'regards', 'nia', 'mansell', 'infocast', 'conference', 'manager', '818', '888', '4445', 'ext', '45', '818', '888', '4440', 'fax', 'niam', 'com', 'see', 'attached', 'file', 'power', 'crisis', 'in', 'the', 'west', 'invite', 'doc', '']
+    Label: 0
+    Tokenized text: ['subject', 're', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'fyi', 'forwarded', 'by', 'karen', 'marshall', 'hou', 'ect', 'on', '01', '18', '2001', '03', '07', 'pm', 'angela', 'dorsey', 'on', '01', '18', '2001', '02', '53', '59', 'pm', 'to', 'cc', 'subject', 're', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'karen', 'thanks', 'for', 'the', 'extra', 'support', 'in', 'getting', 'the', 'word', 'out', 'i', 've', 'had', 'a', 'couple', 'rsvp', 's', 'from', 'enron', 'sincerely', 'angela', 'original', 'message', 'from', 'karen', 'marshall', 'enron', 'com', 'mailto', 'karen', 'marshall', 'enron', 'com', 'sent', 'wednesday', 'january', '17', '2001', '7', '59', 'pm', 'to', 'david', 'haug', 'enron', 'com', 'gary', 'hickerson', 'enron', 'com', 'cchilde', 'enron', 'com', 'thomas', 'suffield', 'enron', 'com', 'ben', 'f', 'glisan', 'enron', 'com', 'ermes', 'melinchon', 'enron', 'com', 'hal', 'elrod', 'enron', 'com', 'clay', 'spears', 'enron', 'com', 'kelly', 'mahmoud', 'enron', 'com', 'ellen', 'fowler', 'enron', 'com', 'kevin', 'kuykendall', 'enron', 'com', 'fred', 'mitro', 'enron', 'com', 'kyle', 'kettler', 'enron', 'com', 'jeff', 'bartlett', 'enron', 'com', 'paul', 'j', 'broderick', 'enron', 'com', 'john', 'house', 'enron', 'com', 'george', 'mccormick', 'enron', 'com', 'guido', 'caranti', 'enron', 'com', 'ken', 'sissingh', 'enron', 'com', 'gwynn', 'gorsuch', 'enron', 'com', 'mark', 'gandy', 'enron', 'com', 'shawn', 'cumberland', 'enron', 'com', 'jennifer', 'martinez', 'enron', 'com', 'sean', 'keenan', 'enron', 'com', 'webb', 'jennings', 'enron', 'com', 'brian', 'hendon', 'enron', 'com', 'billy', 'braddock', 'enron', 'com', 'paul', 'burkhart', 'enron', 'com', 'garrett', 'tripp', 'enron', 'com', 'john', 'massey', 'enron', 'com', 'v', 'charles', 'weldon', 'enron', 'com', 'phayes', 'enron', 'com', 'ross', 'mesquita', 'enron', 'com', 'david', 'mitchell', 'enron', 'com', 'brian', 'kerrigan', 'enron', 'com', 'mark', 'gandy', 'enron', 'com', 'jennifer', 'martinez', 'enron', 'com', 'sean', 'keenan', 'enron', 'com', 'webb', 'jennings', 'enron', 'com', 'brian', 'hendon', 'enron', 'com', 'billy', 'braddock', 'enron', 'com', 'garrett', 'tripp', 'enron', 'com', 'john', 'massey', 'enron', 'com', 'v', 'charles', 'weldon', 'enron', 'com', 'phayes', 'enron', 'com', 'ross', 'mesquita', 'enron', 'com', 'david', 'mitchell', 'enron', 'com', 'christie', 'patrick', 'enron', 'com', 'michael', 'b', 'rosen', 'enron', 'com', 'cindy', 'derecskey', 'enron', 'com', 'cc', 'elyse', 'kalmans', 'enron', 'com', 'richard', 'causey', 'enron', 'com', 'sally', 'beck', 'enron', 'com', 'vince', 'j', 'kaminski', 'enron', 'com', 'jeffrey', 'a', 'shankman', 'enron', 'com', 'angela', 'dorsey', 'subject', 'urgent', 'deadline', 'rsvp', 'by', 'jan', '22', 'nd', 'invitation', 'to', '2001', 'energy', 'financeconference', 'feb', '22', '23', '2001', 'the', 'university', 'of', 'texas', 'at', 'austin', 'the', '500', 'registration', 'fee', 'is', 'waived', 'for', 'any', 'enron', 'employee', 'who', 'wishes', 'to', 'attend', 'this', 'conference', 'because', 'of', 'our', 'relationship', 'with', 'the', 'school', 'please', 'forward', 'this', 'information', 'to', 'your', 'managers', 'and', 'staff', 'members', 'who', 'would', 'benefit', 'from', 'participating', 'in', 'this', 'important', 'conference', 'note', 'vince', 'kaminski', 'is', 'a', 'panellist', 'for', 'the', 'risk', 'management', 'session', '3', 'please', 'note', 'the', 'deadline', 'for', 'rsvp', 'hotel', 'reservations', 'is', 'monday', 'january', '22', 'nd', 'don', 't', 'miss', 'this', 'opportunity', 'should', 'you', 'have', 'any', 'questions', 'please', 'feel', 'free', 'to', 'contact', 'me', 'at', 'ext', '37632', 'karen', 'forwarded', 'by', 'karen', 'marshall', 'hou', 'ect', 'on', '01', '11', '2001', '07', '38', 'pm', 'angela', 'dorsey', 'on', '01', '10', '2001', '03', '06', '18', 'pm', 'to', 'angela', 'dorsey', 'cc', 'ehud', 'ronn', 'sheridan', 'titman', 'e', 'mail', 'subject', 'invitation', 'to', '2001', 'energy', 'finance', 'conference', 'the', 'university', 'of', 'texas', 'at', 'austin', 'colleagues', 'and', 'friends', 'of', 'the', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'cefer', 'happy', 'new', 'year', 'hope', 'you', 'all', 'had', 'a', 'wonderful', 'holiday', 'season', 'on', 'behalf', 'of', 'the', 'university', 'of', 'texas', 'finance', 'department', 'and', 'cefer', 'we', 'would', 'like', 'to', 'cordially', 'invite', 'you', 'to', 'attend', 'our', '2001', 'energy', 'finance', 'conference', 'austin', 'texas', 'february', '22', '23', '2001', 'hosted', 'by', 'the', 'university', 'of', 'texas', 'finance', 'department', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'dr', 'ehud', 'i', 'ronn', 'and', 'dr', 'sheridan', 'titman', 'are', 'currently', 'in', 'the', 'process', 'of', 'finalizing', 'the', 'details', 'of', 'the', 'conference', 'agenda', 'we', 'have', 'listed', 'the', 'agenda', 'outline', 'below', 'to', 'assist', 'you', 'in', 'your', 'travel', 'planning', 'each', 'conference', 'session', 'will', 'be', 'composed', 'of', 'a', 'panel', 'discussion', 'between', '3', '4', 'guest', 'speakers', 'on', 'the', 'designated', 'topic', 'as', 'supporters', 'of', 'the', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'representatives', 'of', 'our', 'trustee', 'corporations', 'enron', 'el', 'paso', 'reliant', 'conoco', 'and', 'southern', 'will', 'have', 'the', '500', 'conference', 'fee', 'waived', 'the', 'conference', 'package', 'includes', 'thursday', 'evening', 's', 'cocktails', 'dinner', 'and', 'hotel', 'ut', 'shuttle', 'service', 'as', 'well', 'as', 'friday', 's', 'conference', 'meals', 'session', 'materials', 'and', 'shuttle', 'service', 'travel', 'to', 'austin', 'and', 'hotel', 'reservations', 'are', 'each', 'participant', 's', 'responsibility', 'a', 'limited', 'number', 'of', 'hotel', 'rooms', 'are', 'being', 'tentatively', 'held', 'at', 'the', 'radisson', 'hotel', 'on', 'town', 'lake', 'under', 'the', 'group', 'name', 'university', 'of', 'texas', 'finance', 'department', 'for', 'the', 'nights', 'of', 'thursday', '2', '22', '01', 'and', 'friday', '2', '23', '01', 'the', 'latter', 'evening', 'for', 'those', 'who', 'choose', 'to', 'stay', 'in', 'austin', 'after', 'the', 'conference', 's', 'conclusion', 'to', 'guarantee', 'room', 'reservations', 'you', 'will', 'need', 'to', 'contact', 'the', 'radisson', 'hotel', 'at', '512', '478', '9611', 'no', 'later', 'than', 'monday', 'january', '22', 'nd', 'and', 'make', 'your', 'reservations', 'with', 'a', 'credit', 'card', 'please', 'let', 'me', 'know', 'when', 'you', 'have', 'made', 'those', 'arrangements', 'so', 'that', 'i', 'can', 'make', 'sure', 'the', 'radisson', 'gives', 'you', 'the', 'special', 'room', 'rate', 'of', '129', 'night', 'please', 'rsvp', 'your', 'interest', 'in', 'attending', 'this', 'conference', 'no', 'later', 'than', 'january', '22', 'nd', 'to', 'angela', 'dorsey', 'bus', 'utexas', 'edu', 'or', '512', '232', '7386', 'as', 'seating', 'availability', 'is', 'limited', 'please', 'feel', 'free', 'to', 'extend', 'this', 'invitation', 'to', 'your', 'colleagues', 'who', 'might', 'be', 'interested', 'in', 'attending', 'this', 'conference', 'center', 'for', 'energy', 'finance', 'education', 'and', 'research', 'program', 'of', 'the', '2001', 'energy', 'finance', 'conference', 'february', '22', '23', '2001', 'thursday', 'feb', '22', '3', '00', 'p', 'm', 'reserved', 'rooms', 'at', 'the', 'radisson', 'hotel', 'available', 'for', 'check', 'in', '5', '30', 'p', 'm', 'bus', 'will', 'pick', 'up', 'guests', 'at', 'the', 'radisson', 'for', 'transport', 'to', 'ut', 'club', '6', '00', 'p', 'm', 'cocktails', 'ut', 'club', '9', 'th', 'floor', '7', '00', 'p', 'm', 'dinner', 'ut', 'club', '8', '00', 'p', 'm', 'keynote', 'speaker', '9', '00', 'p', 'm', 'bus', 'will', 'transport', 'guests', 'back', 'to', 'hotel', 'friday', 'feb', '23', '7', '45', 'a', 'm', 'bus', 'will', 'pick', 'up', 'at', 'the', 'radisson', 'for', 'transport', 'to', 'ut', '8', '30', 'a', 'm', 'session', '1', 'real', 'options', 'panelists', 'jim', 'dyer', 'ut', 'chair', 'sheridan', 'titman', 'ut', 'john', 'mccormack', 'stern', 'stewart', 'co', '10', '00', 'a', 'm', 'coffee', 'break', '10', '15', 'a', 'm', 'session', '2', 'deregulation', 'panelists', 'david', 'eaton', 'ut', 'chair', 'david', 'spence', 'ut', 'jeff', 'sandefer', 'sandefer', 'capital', 'partners', 'ut', 'peter', 'nance', 'teknecon', 'energy', 'risk', 'advisors', '11', '45', 'a', 'm', 'catered', 'lunch', 'keynote', 'speaker', '1', '30', 'p', 'm', 'guest', 'tour', 'eds', 'financial', 'trading', 'technology', 'center', '2', '00', 'p', 'm', 'session', '3', 'risk', 'management', 'panelists', 'keith', 'brown', 'ut', 'chair', 'vince', 'kaminski', 'enron', 'alexander', 'eydeland', 'southern', 'co', 'ehud', 'i', 'ronn', 'ut', '3', '30', 'p', 'm', 'snack', 'break', '3', '45', 'p', 'm', 'session', '4', 'globalization', 'of', 'the', 'energy', 'business', 'panelists', 'laura', 'starks', 'ut', 'chair', 'bob', 'goldman', 'conoco', 'ray', 'hill', 'southern', 'co', '5', '15', 'p', 'm', 'wrap', 'up', '5', '30', 'p', 'm', 'bus', 'picks', 'up', 'for', 'transport', 'to', 'airport', 'dinner', '6', '30', 'p', 'm', 'working', 'dinner', 'for', 'senior', 'officers', 'of', 'energy', 'finance', 'center', 'trustees', 'we', 'have', 'made', 'arrangements', 'to', 'provide', 'shuttle', 'service', 'between', 'the', 'radisson', 'hotel', 'and', 'ut', 'during', 'the', 'conference', 'however', 'if', 'you', 'choose', 'to', 'stay', 'at', 'an', 'alternative', 'hotel', 'then', 'transportation', 'to', 'conference', 'events', 'will', 'become', 'your', 'responsibility', 'angela', 'dorsey', 'assistant', 'director', 'center', 'for', 'energy', 'finance', 'education', 'research', 'the', 'university', 'of', 'texas', 'at', 'austin', 'department', 'of', 'finance', 'cba', '6', '222', 'austin', 'tx', '78712', 'angela', 'dorsey', 'bus', 'utexas', 'edu', '']
+    Label: 0
+    Tokenized text: ['subject', 'interview', 'jaesoo', 'lew', '10', '25', '00', 'attached', 'please', 'find', 'the', 'resume', 'interview', 'schedule', 'and', 'evaluation', 'form', 'for', 'jaesoo', 'lew', 'jaesoo', 'will', 'be', 'interviewing', 'with', 'vince', 'kaminski', 's', 'group', 'on', 'an', 'exploratory', 'basis', 'on', 'october', '25', '2000', 'please', 'contact', 'me', 'with', 'any', 'comments', 'or', 'concerns', 'thank', 'you', 'cheryl', 'arguijo', 'ena', 'recruiting', '713', '345', '4016']
+    Label: 0
+    Tokenized text: ['subject', 'custom', 'marketing', 'to', 'webmaster', 'ezmlm', 'org', 'email', 'is', 'the', 'best', 'promote', 'tool', 'we', 'offer', 'online', 'marketing', 'with', 'quality', 'service', '1', 'target', 'email', 'list', 'we', 'can', 'provide', 'target', 'email', 'list', 'you', 'need', 'which', 'are', 'compiled', 'only', 'on', 'your', 'order', 'we', 'will', 'customize', 'your', 'client', 'email', 'list', 'we', 'have', 'millions', 'of', 'lists', 'in', 'a', 'wide', 'variety', 'of', 'categories', '2', 'send', 'out', 'target', 'list', 'for', 'you', 'we', 'can', 'send', 'your', 'email', 'message', 'to', 'your', 'target', 'clients', 'we', 'will', 'customize', 'your', 'email', 'list', 'and', 'send', 'your', 'message', 'for', 'you', 'our', 'site', 'www', 'marketingforus', 'com', 'we', 'also', 'offer', 'web', 'hosting', 'mail', 'server', 'regards', 'jason', 'marketing', 'support', 'sales', 'marketingforus', 'com', 'no', 'thanks', 'byebye', 'msn', 'com', 'subject', 'webmaster', 'ezmlm', 'org']
+    Label: 1
+    Tokenized text: ['subject', 'popular', 'software', 'at', 'low', 'low', 'prices', 'alaina', 'windows', 'xp', 'professional', '2002', '50', 'adobe', 'photoshop', '7', '0', '50', 'microsoft', 'office', 'xp', 'professional', '2002', '50', 'corel', 'draw', 'graphics', 'suite', '11', '50', '']
+    Label: 1
+
+%% Cell type:markdown id: tags:
+
+## TASK 1: Implement `fit`
+Implement the "fit" and "predict" methods for Naive Bayes. Use $m$-estimate to address missing attribute values (also called **Laplace smoothing** when $m$ = 1). In general, $m$ values should be small. We'll use $m$ = 1. We'll use log probabilities to avoid overflow.
+
+%% Cell type:code id: tags:
+
+``` python
+class Naive_Bayes(Model):
+    def __init__(self, m):
+        '''
+        Args:
+            m: int
+                Specifies the smoothing parameter
+        '''
+        self.m = m
+
+    def fit(self, X, Y):
+        '''
+        Args:
+            X: list
+                list of lists where each entry is a tokenized email text
+            Y: ndarray
+                1D array of true labels. 1: spam, 0: ham
+        '''
+
+        #TODO
+
+        # Replace Nones, empty lists and dictionaries below
+
+        # List containing all distinct words in all emails
+        # A list might not be the best data structure for obtaining
+        # the vocabulary.
+        # Use a temporary more efficient data structure
+        # then populate self.vocabulary.
+
+        self.vocabulary = []
+
+        # find *log* class prior probabilities
+        self.prior = {'spam': None, 'ham': None}
+
+        # find the number of words(counting repeats) summed across all emails in a class
+        self.total_count = {'spam': None, 'ham': None}
+
+        # find the number of each word summed across all emails in a class
+        # populate self.word_counts
+        # self.word_counts['spam'] is a dict with words as keys.
+        self.word_counts = {'spam': {}, 'ham': {}}
+
+
+
+    def predict(self, X):
+        '''
+        Args:
+            X: list
+                list of lists where each entry is a tokenized email text
+        Returns:
+            probs: ndarray
+                mx2 array containing unnormalized log posteriors for spam and ham (for grading purposes)
+            preds: ndarray
+                1D binary array containing predicted labels
+        '''
+        preds = []
+        probs = []
+
+        #TODO
+
+        # use the attributes calculated in fit to compute unnormalized class posterior probabilities
+        # and predicted labels
+
+        raise NotImplementedError
+
+        return np.array(probs), np.array(preds)
+
+```
+
+%% Cell type:markdown id: tags:
+
+### Rubric:
+* correct vocabulary length +5,+5
+* correct log class priors +10, +10
+* correct word counts for the 5 most frequent words in each class +10, +10
+
+%% Cell type:markdown id: tags:
+
+### Test `fit`
+
+%% Cell type:code id: tags:
+
+``` python
+# initialize the model
+my_model = Naive_Bayes(...)
+
+# pass the training emails and labels
+my_model.fit(X=..., Y=...)
+
+# display the most frequent 5 words in both classes
+for cl in ['ham', 'spam']:
+    srt = sorted(my_model.word_counts[cl].items() ,  key=lambda x: x[1], reverse=True)
+    print('\n{} log prior: {}'.format(cl, my_model.prior[cl]))
+    print('5 most frequent words:')
+    print(srt[:5])
+print('\nVocabulary has {} distinct words'.format(len(my_model.vocabulary)))
+```
+
+%% Cell type:markdown id: tags:
+
+## TASK 2: Implement `predict`
+Print the unnormalized log posteriors for the first five examples. We can use the "conf_matrix" function to see how error is distributed.
+
+### Rubric:
+* Correct unnormalized log posteriors +20, +20
+* Correct confusion matrix +5, +5
+
+### Test `predict`
+
+%% Cell type:code id: tags:
+
+``` python
+probs, preds = my_model.predict(X=...)
+print ('\nUnnormalized log posteriors of first 5 test examples:')
+print (probs [:5])
+tp,tn, fp, fn = conf_matrix(true = ..., pred = preds)
+print('tp: {}, tn: {}, fp: {}, fn:{}'.format(tp, tn, fp, fn))
+```
--- a/ProgrammingAssignment_3/model.ipynb
+++ b/ProgrammingAssignment_3/model.ipynb
+%% Cell type:markdown id: tags:
+
+# JUPYTER NOTEBOOK TIPS
+
+Each rectangular box is called a cell.
+* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
+* alt+ENTER evaluates the current cell and adds a new cell below it.
+* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
+
+%% Cell type:markdown id: tags:
+
+# Supervised Learning Model Skeleton
+
+We'll use this skeleton for implementing different supervised learning algorithms.
+
+%% Cell type:code id: tags:
+
+``` python
+class Model:
+
+    def fit(self):
+
+        raise NotImplementedError
+
+    def predict(self, test_points):
+        raise NotImplementedError
+```
+
+%% Cell type:markdown id: tags:
+
+## General supervised learning performance related functions
+
+%% Cell type:markdown id: tags:
+
+"conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*).
+
+%% Cell type:code id: tags:
+
+``` python
+def conf_matrix(true, pred):
+    '''
+    Args:
+        true:  ndarray
+            nx1 array of true labels for test set
+        pred: ndarray
+            nx1 array of predicted labels for test set
+    Returns:
+        ndarray
+    '''
+
+    tp = tn = fp = fn = 0
+    # calculate true positives (tp), true negatives(tn)
+    # false positives (fp) and false negatives (fn)
+
+    size = len(true)
+    for i in range(size):
+        if true[i]==1:
+            if pred[i] == 1:
+                tp += 1
+            else:
+                fn += 1
+        else:
+            if pred[i] == 0:
+                tn += 1
+            else:
+                fp += 1
+
+    # returns the confusion matrix as numpy.ndarray
+    return np.array([[tp,fp], [fn, tn]])
+```
+%% Cell type:markdown id: tags:
+
+# JUPYTER NOTEBOOK TIPS
+
+Each rectangular box is called a cell.
+* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
+* alt+ENTER evaluates the current cell and adds a new cell below it.
+* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
+
+%% Cell type:markdown id: tags:
+
+# Supervised Learning Model Skeleton
+
+We'll use this skeleton for implementing different supervised learning algorithms.
+
+%% Cell type:code id: tags:
+
+``` python
+class Model:
+
+    def fit(self):
+
+        raise NotImplementedError
+
+    def predict(self, test_points):
+        raise NotImplementedError
+```
+
+%% Cell type:markdown id: tags:
+
+## General supervised learning performance related functions
+
+%% Cell type:markdown id: tags:
+
+"conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*).
+
+%% Cell type:code id: tags:
+
+``` python
+def conf_matrix(true, pred):
+    '''
+    Args:
+        true:  ndarray
+            nx1 array of true labels for test set
+        pred: ndarray
+            nx1 array of predicted labels for test set
+    Returns:
+        ndarray
+    '''
+
+    tp = tn = fp = fn = 0
+    # calculate true positives (tp), true negatives(tn)
+    # false positives (fp) and false negatives (fn)
+
+    size = len(true)
+    for i in range(size):
+        if true[i]==1:
+            if pred[i] == 1:
+                tp += 1
+            else:
+                fn += 1
+        else:
+            if pred[i] == 0:
+                tn += 1
+            else:
+                fp += 1
+
+    # returns the confusion matrix as numpy.ndarray
+    return np.array([[tp,fp], [fn, tn]])
+```
--- a/data/housing.data
+++ b/data/housing.data
+ 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00
+ 0.02731   0.00   7.070  0  0.4690  6.4210  78.90  4.9671   2  242.0  17.80 396.90   9.14  21.60
+ 0.02729   0.00   7.070  0  0.4690  7.1850  61.10  4.9671   2  242.0  17.80 392.83   4.03  34.70
+ 0.03237   0.00   2.180  0  0.4580  6.9980  45.80  6.0622   3  222.0  18.70 394.63   2.94  33.40
+ 0.06905   0.00   2.180  0  0.4580  7.1470  54.20  6.0622   3  222.0  18.70 396.90   5.33  36.20
+ 0.02985   0.00   2.180  0  0.4580  6.4300  58.70  6.0622   3  222.0  18.70 394.12   5.21  28.70
+ 0.08829  12.50   7.870  0  0.5240  6.0120  66.60  5.5605   5  311.0  15.20 395.60  12.43  22.90
+ 0.14455  12.50   7.870  0  0.5240  6.1720  96.10  5.9505   5  311.0  15.20 396.90  19.15  27.10
+ 0.21124  12.50   7.870  0  0.5240  5.6310 100.00  6.0821   5  311.0  15.20 386.63  29.93  16.50
+ 0.17004  12.50   7.870  0  0.5240  6.0040  85.90  6.5921   5  311.0  15.20 386.71  17.10  18.90
+ 0.22489  12.50   7.870  0  0.5240  6.3770  94.30  6.3467   5  311.0  15.20 392.52  20.45  15.00
+ 0.11747  12.50   7.870  0  0.5240  6.0090  82.90  6.2267   5  311.0  15.20 396.90  13.27  18.90
+ 0.09378  12.50   7.870  0  0.5240  5.8890  39.00  5.4509   5  311.0  15.20 390.50  15.71  21.70
+ 0.62976   0.00   8.140  0  0.5380  5.9490  61.80  4.7075   4  307.0  21.00 396.90   8.26  20.40
+ 0.63796   0.00   8.140  0  0.5380  6.0960  84.50  4.4619   4  307.0  21.00 380.02  10.26  18.20
+ 0.62739   0.00   8.140  0  0.5380  5.8340  56.50  4.4986   4  307.0  21.00 395.62   8.47  19.90
+ 1.05393   0.00   8.140  0  0.5380  5.9350  29.30  4.4986   4  307.0  21.00 386.85   6.58  23.10
+ 0.78420   0.00   8.140  0  0.5380  5.9900  81.70  4.2579   4  307.0  21.00 386.75  14.67  17.50
+ 0.80271   0.00   8.140  0  0.5380  5.4560  36.60  3.7965   4  307.0  21.00 288.99  11.69  20.20
+ 0.72580   0.00   8.140  0  0.5380  5.7270  69.50  3.7965   4  307.0  21.00 390.95  11.28  18.20
+ 1.25179   0.00   8.140  0  0.5380  5.5700  98.10  3.7979   4  307.0  21.00 376.57  21.02  13.60
+ 0.85204   0.00   8.140  0  0.5380  5.9650  89.20  4.0123   4  307.0  21.00 392.53  13.83  19.60
+ 1.23247   0.00   8.140  0  0.5380  6.1420  91.70  3.9769   4  307.0  21.00 396.90  18.72  15.20
+ 0.98843   0.00   8.140  0  0.5380  5.8130 100.00  4.0952   4  307.0  21.00 394.54  19.88  14.50
+ 0.75026   0.00   8.140  0  0.5380  5.9240  94.10  4.3996   4  307.0  21.00 394.33  16.30  15.60
+ 0.84054   0.00   8.140  0  0.5380  5.5990  85.70  4.4546   4  307.0  21.00 303.42  16.51  13.90
+ 0.67191   0.00   8.140  0  0.5380  5.8130  90.30  4.6820   4  307.0  21.00 376.88  14.81  16.60
+ 0.95577   0.00   8.140  0  0.5380  6.0470  88.80  4.4534   4  307.0  21.00 306.38  17.28  14.80
+ 0.77299   0.00   8.140  0  0.5380  6.4950  94.40  4.4547   4  307.0  21.00 387.94  12.80  18.40
+ 1.00245   0.00   8.140  0  0.5380  6.6740  87.30  4.2390   4  307.0  21.00 380.23  11.98  21.00
+ 1.13081   0.00   8.140  0  0.5380  5.7130  94.10  4.2330   4  307.0  21.00 360.17  22.60  12.70
+ 1.35472   0.00   8.140  0  0.5380  6.0720 100.00  4.1750   4  307.0  21.00 376.73  13.04  14.50
+ 1.38799   0.00   8.140  0  0.5380  5.9500  82.00  3.9900   4  307.0  21.00 232.60  27.71  13.20
+ 1.15172   0.00   8.140  0  0.5380  5.7010  95.00  3.7872   4  307.0  21.00 358.77  18.35  13.10
+ 1.61282   0.00   8.140  0  0.5380  6.0960  96.90  3.7598   4  307.0  21.00 248.31  20.34  13.50
+ 0.06417   0.00   5.960  0  0.4990  5.9330  68.20  3.3603   5  279.0  19.20 396.90   9.68  18.90
+ 0.09744   0.00   5.960  0  0.4990  5.8410  61.40  3.3779   5  279.0  19.20 377.56  11.41  20.00
+ 0.08014   0.00   5.960  0  0.4990  5.8500  41.50  3.9342   5  279.0  19.20 396.90   8.77  21.00
+ 0.17505   0.00   5.960  0  0.4990  5.9660  30.20  3.8473   5  279.0  19.20 393.43  10.13  24.70
+ 0.02763  75.00   2.950  0  0.4280  6.5950  21.80  5.4011   3  252.0  18.30 395.63   4.32  30.80
+ 0.03359  75.00   2.950  0  0.4280  7.0240  15.80  5.4011   3  252.0  18.30 395.62   1.98  34.90
+ 0.12744   0.00   6.910  0  0.4480  6.7700   2.90  5.7209   3  233.0  17.90 385.41   4.84  26.60
+ 0.14150   0.00   6.910  0  0.4480  6.1690   6.60  5.7209   3  233.0  17.90 383.37   5.81  25.30
+ 0.15936   0.00   6.910  0  0.4480  6.2110   6.50  5.7209   3  233.0  17.90 394.46   7.44  24.70
+ 0.12269   0.00   6.910  0  0.4480  6.0690  40.00  5.7209   3  233.0  17.90 389.39   9.55  21.20
+ 0.17142   0.00   6.910  0  0.4480  5.6820  33.80  5.1004   3  233.0  17.90 396.90  10.21  19.30
+ 0.18836   0.00   6.910  0  0.4480  5.7860  33.30  5.1004   3  233.0  17.90 396.90  14.15  20.00
+ 0.22927   0.00   6.910  0  0.4480  6.0300  85.50  5.6894   3  233.0  17.90 392.74  18.80  16.60
+ 0.25387   0.00   6.910  0  0.4480  5.3990  95.30  5.8700   3  233.0  17.90 396.90  30.81  14.40
+ 0.21977   0.00   6.910  0  0.4480  5.6020  62.00  6.0877   3  233.0  17.90 396.90  16.20  19.40
+ 0.08873  21.00   5.640  0  0.4390  5.9630  45.70  6.8147   4  243.0  16.80 395.56  13.45  19.70
+ 0.04337  21.00   5.640  0  0.4390  6.1150  63.00  6.8147   4  243.0  16.80 393.97   9.43  20.50
+ 0.05360  21.00   5.640  0  0.4390  6.5110  21.10  6.8147   4  243.0  16.80 396.90   5.28  25.00
+ 0.04981  21.00   5.640  0  0.4390  5.9980  21.40  6.8147   4  243.0  16.80 396.90   8.43  23.40
+ 0.01360  75.00   4.000  0  0.4100  5.8880  47.60  7.3197   3  469.0  21.10 396.90  14.80  18.90
+ 0.01311  90.00   1.220  0  0.4030  7.2490  21.90  8.6966   5  226.0  17.90 395.93   4.81  35.40
+ 0.02055  85.00   0.740  0  0.4100  6.3830  35.70  9.1876   2  313.0  17.30 396.90   5.77  24.70
+ 0.01432 100.00   1.320  0  0.4110  6.8160  40.50  8.3248   5  256.0  15.10 392.90   3.95  31.60
+ 0.15445  25.00   5.130  0  0.4530  6.1450  29.20  7.8148   8  284.0  19.70 390.68   6.86  23.30
+ 0.10328  25.00   5.130  0  0.4530  5.9270  47.20  6.9320   8  284.0  19.70 396.90   9.22  19.60
+ 0.14932  25.00   5.130  0  0.4530  5.7410  66.20  7.2254   8  284.0  19.70 395.11  13.15  18.70
+ 0.17171  25.00   5.130  0  0.4530  5.9660  93.40  6.8185   8  284.0  19.70 378.08  14.44  16.00
+ 0.11027  25.00   5.130  0  0.4530  6.4560  67.80  7.2255   8  284.0  19.70 396.90   6.73  22.20
+ 0.12650  25.00   5.130  0  0.4530  6.7620  43.40  7.9809   8  284.0  19.70 395.58   9.50  25.00
+ 0.01951  17.50   1.380  0  0.4161  7.1040  59.50  9.2229   3  216.0  18.60 393.24   8.05  33.00
+ 0.03584  80.00   3.370  0  0.3980  6.2900  17.80  6.6115   4  337.0  16.10 396.90   4.67  23.50
+ 0.04379  80.00   3.370  0  0.3980  5.7870  31.10  6.6115   4  337.0  16.10 396.90  10.24  19.40
+ 0.05789  12.50   6.070  0  0.4090  5.8780  21.40  6.4980   4  345.0  18.90 396.21   8.10  22.00
+ 0.13554  12.50   6.070  0  0.4090  5.5940  36.80  6.4980   4  345.0  18.90 396.90  13.09  17.40
+ 0.12816  12.50   6.070  0  0.4090  5.8850  33.00  6.4980   4  345.0  18.90 396.90   8.79  20.90
+ 0.08826   0.00  10.810  0  0.4130  6.4170   6.60  5.2873   4  305.0  19.20 383.73   6.72  24.20
+ 0.15876   0.00  10.810  0  0.4130  5.9610  17.50  5.2873   4  305.0  19.20 376.94   9.88  21.70
+ 0.09164   0.00  10.810  0  0.4130  6.0650   7.80  5.2873   4  305.0  19.20 390.91   5.52  22.80
+ 0.19539   0.00  10.810  0  0.4130  6.2450   6.20  5.2873   4  305.0  19.20 377.17   7.54  23.40
+ 0.07896   0.00  12.830  0  0.4370  6.2730   6.00  4.2515   5  398.0  18.70 394.92   6.78  24.10
+ 0.09512   0.00  12.830  0  0.4370  6.2860  45.00  4.5026   5  398.0  18.70 383.23   8.94  21.40
+ 0.10153   0.00  12.830  0  0.4370  6.2790  74.50  4.0522   5  398.0  18.70 373.66  11.97  20.00
+ 0.08707   0.00  12.830  0  0.4370  6.1400  45.80  4.0905   5  398.0  18.70 386.96  10.27  20.80
+ 0.05646   0.00  12.830  0  0.4370  6.2320  53.70  5.0141   5  398.0  18.70 386.40  12.34  21.20
+ 0.08387   0.00  12.830  0  0.4370  5.8740  36.60  4.5026   5  398.0  18.70 396.06   9.10  20.30
+ 0.04113  25.00   4.860  0  0.4260  6.7270  33.50  5.4007   4  281.0  19.00 396.90   5.29  28.00
+ 0.04462  25.00   4.860  0  0.4260  6.6190  70.40  5.4007   4  281.0  19.00 395.63   7.22  23.90
+ 0.03659  25.00   4.860  0  0.4260  6.3020  32.20  5.4007   4  281.0  19.00 396.90   6.72  24.80
+ 0.03551  25.00   4.860  0  0.4260  6.1670  46.70  5.4007   4  281.0  19.00 390.64   7.51  22.90
+ 0.05059   0.00   4.490  0  0.4490  6.3890  48.00  4.7794   3  247.0  18.50 396.90   9.62  23.90
+ 0.05735   0.00   4.490  0  0.4490  6.6300  56.10  4.4377   3  247.0  18.50 392.30   6.53  26.60
+ 0.05188   0.00   4.490  0  0.4490  6.0150  45.10  4.4272   3  247.0  18.50 395.99  12.86  22.50
+ 0.07151   0.00   4.490  0  0.4490  6.1210  56.80  3.7476   3  247.0  18.50 395.15   8.44  22.20
+ 0.05660   0.00   3.410  0  0.4890  7.0070  86.30  3.4217   2  270.0  17.80 396.90   5.50  23.60
+ 0.05302   0.00   3.410  0  0.4890  7.0790  63.10  3.4145   2  270.0  17.80 396.06   5.70  28.70
+ 0.04684   0.00   3.410  0  0.4890  6.4170  66.10  3.0923   2  270.0  17.80 392.18   8.81  22.60
+ 0.03932   0.00   3.410  0  0.4890  6.4050  73.90  3.0921   2  270.0  17.80 393.55   8.20  22.00
+ 0.04203  28.00  15.040  0  0.4640  6.4420  53.60  3.6659   4  270.0  18.20 395.01   8.16  22.90
+ 0.02875  28.00  15.040  0  0.4640  6.2110  28.90  3.6659   4  270.0  18.20 396.33   6.21  25.00
+ 0.04294  28.00  15.040  0  0.4640  6.2490  77.30  3.6150   4  270.0  18.20 396.90  10.59  20.60
+ 0.12204   0.00   2.890  0  0.4450  6.6250  57.80  3.4952   2  276.0  18.00 357.98   6.65  28.40
+ 0.11504   0.00   2.890  0  0.4450  6.1630  69.60  3.4952   2  276.0  18.00 391.83  11.34  21.40
+ 0.12083   0.00   2.890  0  0.4450  8.0690  76.00  3.4952   2  276.0  18.00 396.90   4.21  38.70
+ 0.08187   0.00   2.890  0  0.4450  7.8200  36.90  3.4952   2  276.0  18.00 393.53   3.57  43.80
+ 0.06860   0.00   2.890  0  0.4450  7.4160  62.50  3.4952   2  276.0  18.00 396.90   6.19  33.20
+ 0.14866   0.00   8.560  0  0.5200  6.7270  79.90  2.7778   5  384.0  20.90 394.76   9.42  27.50
+ 0.11432   0.00   8.560  0  0.5200  6.7810  71.30  2.8561   5  384.0  20.90 395.58   7.67  26.50
+ 0.22876   0.00   8.560  0  0.5200  6.4050  85.40  2.7147   5  384.0  20.90  70.80  10.63  18.60
+ 0.21161   0.00   8.560  0  0.5200  6.1370  87.40  2.7147   5  384.0  20.90 394.47  13.44  19.30
+ 0.13960   0.00   8.560  0  0.5200  6.1670  90.00  2.4210   5  384.0  20.90 392.69  12.33  20.10
+ 0.13262   0.00   8.560  0  0.5200  5.8510  96.70  2.1069   5  384.0  20.90 394.05  16.47  19.50
+ 0.17120   0.00   8.560  0  0.5200  5.8360  91.90  2.2110   5  384.0  20.90 395.67  18.66  19.50
+ 0.13117   0.00   8.560  0  0.5200  6.1270  85.20  2.1224   5  384.0  20.90 387.69  14.09  20.40
+ 0.12802   0.00   8.560  0  0.5200  6.4740  97.10  2.4329   5  384.0  20.90 395.24  12.27  19.80
+ 0.26363   0.00   8.560  0  0.5200  6.2290  91.20  2.5451   5  384.0  20.90 391.23  15.55  19.40
+ 0.10793   0.00   8.560  0  0.5200  6.1950  54.40  2.7778   5  384.0  20.90 393.49  13.00  21.70
+ 0.10084   0.00  10.010  0  0.5470  6.7150  81.60  2.6775   6  432.0  17.80 395.59  10.16  22.80
+ 0.12329   0.00  10.010  0  0.5470  5.9130  92.90  2.3534   6  432.0  17.80 394.95  16.21  18.80
+ 0.22212   0.00  10.010  0  0.5470  6.0920  95.40  2.5480   6  432.0  17.80 396.90  17.09  18.70
+ 0.14231   0.00  10.010  0  0.5470  6.2540  84.20  2.2565   6  432.0  17.80 388.74  10.45  18.50
+ 0.17134   0.00  10.010  0  0.5470  5.9280  88.20  2.4631   6  432.0  17.80 344.91  15.76  18.30
+ 0.13158   0.00  10.010  0  0.5470  6.1760  72.50  2.7301   6  432.0  17.80 393.30  12.04  21.20
+ 0.15098   0.00  10.010  0  0.5470  6.0210  82.60  2.7474   6  432.0  17.80 394.51  10.30  19.20
+ 0.13058   0.00  10.010  0  0.5470  5.8720  73.10  2.4775   6  432.0  17.80 338.63  15.37  20.40
+ 0.14476   0.00  10.010  0  0.5470  5.7310  65.20  2.7592   6  432.0  17.80 391.50  13.61  19.30
+ 0.06899   0.00  25.650  0  0.5810  5.8700  69.70  2.2577   2  188.0  19.10 389.15  14.37  22.00
+ 0.07165   0.00  25.650  0  0.5810  6.0040  84.10  2.1974   2  188.0  19.10 377.67  14.27  20.30
+ 0.09299   0.00  25.650  0  0.5810  5.9610  92.90  2.0869   2  188.0  19.10 378.09  17.93  20.50
+ 0.15038   0.00  25.650  0  0.5810  5.8560  97.00  1.9444   2  188.0  19.10 370.31  25.41  17.30
+ 0.09849   0.00  25.650  0  0.5810  5.8790  95.80  2.0063   2  188.0  19.10 379.38  17.58  18.80
+ 0.16902   0.00  25.650  0  0.5810  5.9860  88.40  1.9929   2  188.0  19.10 385.02  14.81  21.40
+ 0.38735   0.00  25.650  0  0.5810  5.6130  95.60  1.7572   2  188.0  19.10 359.29  27.26  15.70
+ 0.25915   0.00  21.890  0  0.6240  5.6930  96.00  1.7883   4  437.0  21.20 392.11  17.19  16.20
+ 0.32543   0.00  21.890  0  0.6240  6.4310  98.80  1.8125   4  437.0  21.20 396.90  15.39  18.00
+ 0.88125   0.00  21.890  0  0.6240  5.6370  94.70  1.9799   4  437.0  21.20 396.90  18.34  14.30
+ 0.34006   0.00  21.890  0  0.6240  6.4580  98.90  2.1185   4  437.0  21.20 395.04  12.60  19.20
+ 1.19294   0.00  21.890  0  0.6240  6.3260  97.70  2.2710   4  437.0  21.20 396.90  12.26  19.60
+ 0.59005   0.00  21.890  0  0.6240  6.3720  97.90  2.3274   4  437.0  21.20 385.76  11.12  23.00
+ 0.32982   0.00  21.890  0  0.6240  5.8220  95.40  2.4699   4  437.0  21.20 388.69  15.03  18.40
+ 0.97617   0.00  21.890  0  0.6240  5.7570  98.40  2.3460   4  437.0  21.20 262.76  17.31  15.60
+ 0.55778   0.00  21.890  0  0.6240  6.3350  98.20  2.1107   4  437.0  21.20 394.67  16.96  18.10
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+13.91340   0.00  18.100  0  0.7130  6.2080  95.00  2.2222  24  666.0  20.20 100.63  15.17  11.70
+11.16040   0.00  18.100  0  0.7400  6.6290  94.60  2.1247  24  666.0  20.20 109.85  23.27  13.40
+14.42080   0.00  18.100  0  0.7400  6.4610  93.30  2.0026  24  666.0  20.20  27.49  18.05   9.60
+15.17720   0.00  18.100  0  0.7400  6.1520 100.00  1.9142  24  666.0  20.20   9.32  26.45   8.70
+13.67810   0.00  18.100  0  0.7400  5.9350  87.90  1.8206  24  666.0  20.20  68.95  34.02   8.40
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+ 9.72418   0.00  18.100  0  0.7400  6.4060  97.20  2.0651  24  666.0  20.20 385.96  19.52  17.10
+ 5.66637   0.00  18.100  0  0.7400  6.2190 100.00  2.0048  24  666.0  20.20 395.69  16.59  18.40
+ 9.96654   0.00  18.100  0  0.7400  6.4850 100.00  1.9784  24  666.0  20.20 386.73  18.85  15.40
+12.80230   0.00  18.100  0  0.7400  5.8540  96.60  1.8956  24  666.0  20.20 240.52  23.79  10.80
+10.67180   0.00  18.100  0  0.7400  6.4590  94.80  1.9879  24  666.0  20.20  43.06  23.98  11.80
+ 6.28807   0.00  18.100  0  0.7400  6.3410  96.40  2.0720  24  666.0  20.20 318.01  17.79  14.90
+ 9.92485   0.00  18.100  0  0.7400  6.2510  96.60  2.1980  24  666.0  20.20 388.52  16.44  12.60
+ 9.32909   0.00  18.100  0  0.7130  6.1850  98.70  2.2616  24  666.0  20.20 396.90  18.13  14.10
+ 7.52601   0.00  18.100  0  0.7130  6.4170  98.30  2.1850  24  666.0  20.20 304.21  19.31  13.00
+ 6.71772   0.00  18.100  0  0.7130  6.7490  92.60  2.3236  24  666.0  20.20   0.32  17.44  13.40
+ 5.44114   0.00  18.100  0  0.7130  6.6550  98.20  2.3552  24  666.0  20.20 355.29  17.73  15.20
+ 5.09017   0.00  18.100  0  0.7130  6.2970  91.80  2.3682  24  666.0  20.20 385.09  17.27  16.10
+ 8.24809   0.00  18.100  0  0.7130  7.3930  99.30  2.4527  24  666.0  20.20 375.87  16.74  17.80
+ 9.51363   0.00  18.100  0  0.7130  6.7280  94.10  2.4961  24  666.0  20.20   6.68  18.71  14.90
+ 4.75237   0.00  18.100  0  0.7130  6.5250  86.50  2.4358  24  666.0  20.20  50.92  18.13  14.10
+ 4.66883   0.00  18.100  0  0.7130  5.9760  87.90  2.5806  24  666.0  20.20  10.48  19.01  12.70
+ 8.20058   0.00  18.100  0  0.7130  5.9360  80.30  2.7792  24  666.0  20.20   3.50  16.94  13.50
+ 7.75223   0.00  18.100  0  0.7130  6.3010  83.70  2.7831  24  666.0  20.20 272.21  16.23  14.90
+ 6.80117   0.00  18.100  0  0.7130  6.0810  84.40  2.7175  24  666.0  20.20 396.90  14.70  20.00
+ 4.81213   0.00  18.100  0  0.7130  6.7010  90.00  2.5975  24  666.0  20.20 255.23  16.42  16.40
+ 3.69311   0.00  18.100  0  0.7130  6.3760  88.40  2.5671  24  666.0  20.20 391.43  14.65  17.70
+ 6.65492   0.00  18.100  0  0.7130  6.3170  83.00  2.7344  24  666.0  20.20 396.90  13.99  19.50
+ 5.82115   0.00  18.100  0  0.7130  6.5130  89.90  2.8016  24  666.0  20.20 393.82  10.29  20.20
+ 7.83932   0.00  18.100  0  0.6550  6.2090  65.40  2.9634  24  666.0  20.20 396.90  13.22  21.40
+ 3.16360   0.00  18.100  0  0.6550  5.7590  48.20  3.0665  24  666.0  20.20 334.40  14.13  19.90
+ 3.77498   0.00  18.100  0  0.6550  5.9520  84.70  2.8715  24  666.0  20.20  22.01  17.15  19.00
+ 4.42228   0.00  18.100  0  0.5840  6.0030  94.50  2.5403  24  666.0  20.20 331.29  21.32  19.10
+15.57570   0.00  18.100  0  0.5800  5.9260  71.00  2.9084  24  666.0  20.20 368.74  18.13  19.10
+13.07510   0.00  18.100  0  0.5800  5.7130  56.70  2.8237  24  666.0  20.20 396.90  14.76  20.10
+ 4.34879   0.00  18.100  0  0.5800  6.1670  84.00  3.0334  24  666.0  20.20 396.90  16.29  19.90
+ 4.03841   0.00  18.100  0  0.5320  6.2290  90.70  3.0993  24  666.0  20.20 395.33  12.87  19.60
+ 3.56868   0.00  18.100  0  0.5800  6.4370  75.00  2.8965  24  666.0  20.20 393.37  14.36  23.20
+ 4.64689   0.00  18.100  0  0.6140  6.9800  67.60  2.5329  24  666.0  20.20 374.68  11.66  29.80
+ 8.05579   0.00  18.100  0  0.5840  5.4270  95.40  2.4298  24  666.0  20.20 352.58  18.14  13.80
+ 6.39312   0.00  18.100  0  0.5840  6.1620  97.40  2.2060  24  666.0  20.20 302.76  24.10  13.30
+ 4.87141   0.00  18.100  0  0.6140  6.4840  93.60  2.3053  24  666.0  20.20 396.21  18.68  16.70
+15.02340   0.00  18.100  0  0.6140  5.3040  97.30  2.1007  24  666.0  20.20 349.48  24.91  12.00
+10.23300   0.00  18.100  0  0.6140  6.1850  96.70  2.1705  24  666.0  20.20 379.70  18.03  14.60
+14.33370   0.00  18.100  0  0.6140  6.2290  88.00  1.9512  24  666.0  20.20 383.32  13.11  21.40
+ 5.82401   0.00  18.100  0  0.5320  6.2420  64.70  3.4242  24  666.0  20.20 396.90  10.74  23.00
+ 5.70818   0.00  18.100  0  0.5320  6.7500  74.90  3.3317  24  666.0  20.20 393.07   7.74  23.70
+ 5.73116   0.00  18.100  0  0.5320  7.0610  77.00  3.4106  24  666.0  20.20 395.28   7.01  25.00
+ 2.81838   0.00  18.100  0  0.5320  5.7620  40.30  4.0983  24  666.0  20.20 392.92  10.42  21.80
+ 2.37857   0.00  18.100  0  0.5830  5.8710  41.90  3.7240  24  666.0  20.20 370.73  13.34  20.60
+ 3.67367   0.00  18.100  0  0.5830  6.3120  51.90  3.9917  24  666.0  20.20 388.62  10.58  21.20
+ 5.69175   0.00  18.100  0  0.5830  6.1140  79.80  3.5459  24  666.0  20.20 392.68  14.98  19.10
+ 4.83567   0.00  18.100  0  0.5830  5.9050  53.20  3.1523  24  666.0  20.20 388.22  11.45  20.60
+ 0.15086   0.00  27.740  0  0.6090  5.4540  92.70  1.8209   4  711.0  20.10 395.09  18.06  15.20
+ 0.18337   0.00  27.740  0  0.6090  5.4140  98.30  1.7554   4  711.0  20.10 344.05  23.97   7.00
+ 0.20746   0.00  27.740  0  0.6090  5.0930  98.00  1.8226   4  711.0  20.10 318.43  29.68   8.10
+ 0.10574   0.00  27.740  0  0.6090  5.9830  98.80  1.8681   4  711.0  20.10 390.11  18.07  13.60
+ 0.11132   0.00  27.740  0  0.6090  5.9830  83.50  2.1099   4  711.0  20.10 396.90  13.35  20.10
+ 0.17331   0.00   9.690  0  0.5850  5.7070  54.00  2.3817   6  391.0  19.20 396.90  12.01  21.80
+ 0.27957   0.00   9.690  0  0.5850  5.9260  42.60  2.3817   6  391.0  19.20 396.90  13.59  24.50
+ 0.17899   0.00   9.690  0  0.5850  5.6700  28.80  2.7986   6  391.0  19.20 393.29  17.60  23.10
+ 0.28960   0.00   9.690  0  0.5850  5.3900  72.90  2.7986   6  391.0  19.20 396.90  21.14  19.70
+ 0.26838   0.00   9.690  0  0.5850  5.7940  70.60  2.8927   6  391.0  19.20 396.90  14.10  18.30
+ 0.23912   0.00   9.690  0  0.5850  6.0190  65.30  2.4091   6  391.0  19.20 396.90  12.92  21.20
+ 0.17783   0.00   9.690  0  0.5850  5.5690  73.50  2.3999   6  391.0  19.20 395.77  15.10  17.50
+ 0.22438   0.00   9.690  0  0.5850  6.0270  79.70  2.4982   6  391.0  19.20 396.90  14.33  16.80
+ 0.06263   0.00  11.930  0  0.5730  6.5930  69.10  2.4786   1  273.0  21.00 391.99   9.67  22.40
+ 0.04527   0.00  11.930  0  0.5730  6.1200  76.70  2.2875   1  273.0  21.00 396.90   9.08  20.60
+ 0.06076   0.00  11.930  0  0.5730  6.9760  91.00  2.1675   1  273.0  21.00 396.90   5.64  23.90
+ 0.10959   0.00  11.930  0  0.5730  6.7940  89.30  2.3889   1  273.0  21.00 393.45   6.48  22.00
+ 0.04741   0.00  11.930  0  0.5730  6.0300  80.80  2.5050   1  273.0  21.00 396.90   7.88  11.90
--- a/data/housing.names
+++ b/data/housing.names
+CRIM
+ZN
+INDUS
+CHAS
+NOX
+RM
+AGE
+DIS
+RAD
+TAX
+PTRATIO
+B
+LSTAT
No results found