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02_PYTHON/01_Naive_Bayes_Classifier/README.md

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+### Task
+    In this assignment you will implement naive Bayes classifiers based on histograms
+
+### Command-line Arguments
+
+    You must implement a program that learns a naive Bayes classifier for a classification problem.
+    Given some training data and some additional options. In particular, your program will be invoked as follows:
+    naive_bayes <training_file> <test_file> histograms <number>
+
+### Training: Histograms
+
+    If the third commandline argument is histograms, then you should model P(x | class) as a histogram separately for each dimension of the data. 
+    The number of bins for each histogram is specified by the fourth command line argument.
+    Suppose that you are building a histogram of N bins for the j-th dimension of the data and for the c-th class. 
+    Let S be the smallest and L be the largest value in the j-th dimension among all training data belonging to the c-th class. 
+    Let G = (L-S)/(N-3). G will be the width of all bins, except for bin 0 and bin N-1, whose width is infinite. 
+    If you get a value of G that is less than 0.0001, then set G to 0.0001. Your bins should have the following ranges:
+
+      Bin 0, covering interval (-infinity, S-G/2).
+      Bin 1, covering interval [S-G/2, S+G/2).
+      Bin 2, covering interval [S+G/2, S+G+G/2).
+      Bin 3, covering interval [S+G+G/2, S+2G+G/2).
+      ...
+      Bin N-2, covering interval [S+(N-4)G+G/2, S+(N-3)G+G/2). This interval is the same as [L-G/2, L+G/2).
+      Bin N-1, covering interval [S+(N-3)G+G/2, +infinity). This interval is the same as [L+G/2, +infinity).
+
+    The output of the training phase should be a sequence of lines like this:
+    Class %d, attribute %d, bin %d, P(bin | class) = %.2f
+    The output lines should be sorted by class number. Within the same class, lines should be sorted by attribute number. 
+    Within the same attribute, lines should be sorted by bin number. Attributes and bins should be numbered starting from 0, not from 1.
+    In computing the value that you store at each bin of each histogram, you must use Equation 2.241 on page 120 of the textbook. 
+    Notice that the width of the bin appears in the denominator of that equation. As mentioned above, the minimum width should be 0.0001. If your value of G is less than 0.0001, you should set G to 0.0001.
+
+    In your answers.pdf document, provide the output produced by the training stage of your program when given yeast_training.txt as the input file, using seven bins for each histogram.
+
+### Classification
+
+    For each test object you should print a line containing the following info:
+    object ID. This is the line number where that object occurs in the test file. Start with 0 in numbering the objects, not with 1.
+    predicted class (the result of the classification). If your classification result is a tie among two or more classes, choose one of them randomly.
+    probability of the predicted class given the data.
+    true class (from the last column of the test file).
+    accuracy. This is defined as follows:
+    If there were no ties in your classification result, and the predicted class is correct, the accuracy is 1.
+    If there were no ties in your classification result, and the predicted class is incorrect, the accuracy is 0.
+    If there were ties in your classification result, and the correct class was one of the classes that tied for best, the accuracy is 1 divided by the number of classes that tied for best.
+    If there were ties in your classification result, and the correct class was NOT one of the classes that tied for best, the accuracy is 0.
+    To produce this output in a uniform manner, use these printing statements:
+    For C or C++, use:
+    printf("ID=%5d, predicted=%3d, probability = %.4lf, true=%3d, accuracy=%4.2lf\n", 
+           object_id, probability, predicted_class, true_class, accuracy);
+    For Java, use:
+    System.out.printf("ID=%5d, predicted=%3d, probability = %.4f, true=%3d, accuracy=%4.2f\n", 
+                      object_id, predicted_class, probability, true_class, accuracy);

02_PYTHON/02_Gaussian_Naive_Bayes/README.md

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+### Task
+
+    You must implement a program that learns a naive Bayes classifier for a classification problem, given some training data and some additional options. 
+    In particular, your program will be invoked as follows:
+      - naive_bayes <training_file> <test_file> gaussians
+
+### Training: Gaussians
+
+    If the third commandline argument is gaussians, then you should model P(x | class) as a Gaussian separately for each dimension of the data. 
+    The output of the training phase should be a sequence of lines like this:
+      Class %d, attribute %d, mean = %.2f, std = %.2f
+    The output lines should be sorted by class number.
+    Within the same class, lines should be sorted by attribute number. 
+    Attributes should be numbered starting from 0, not from 1.
+    In certain cases, it is possible that value computed for the standard deviation is equal to zero. 
+    Your code should make sure that the variance of the Gaussian is NEVER smaller than 0.0001. 
+    Since the variance is the square of the standard deviation, this means that the standard deviation should never be smaller than sqrt(0.0001) = 0.01. 
+    Any time the value for the standard deviation is computed to be smaller than 0.01, your code should replace that value with 0.01.
+
+    In your answers.pdf document, provide the output produced by the training stage of your program when given yeast_training.txt as the input file.