-Added Gradient Descent Algorithm

Author: =

machine_learning/gradient_descent.py

@@ -0,0 +1,121 @@
+"""
+Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis function.
+"""
+import numpy
+
+# List of input, output pairs
+train_data = (((5, 2, 3), 15), ((6, 5, 9), 25),
+              ((11, 12, 13), 41), ((1, 1, 1), 8), ((11, 12, 13), 41))
+test_data = (((515, 22, 13), 555), ((61, 35, 49), 150))
+parameter_vector = [2, 4, 1, 5]
+m = len(train_data)
+LEARNING_RATE = 0.009
+
+
+def _error(example_no, data_set='train'):
+    """
+    :param data_set: train data or test data
+    :param example_no: example number whose error has to be checked
+    :return: error in example pointed by example number.
+    """
+    return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set)
+
+
+def _hypothesis_value(data_input_tuple):
+    """
+    Calculates hypothesis function value for a given input
+    :param data_input_tuple: Input tuple of a particular example
+    :return: Value of hypothesis function at that point.
+    Note that there is an 'biased input' whose value is fixed as 1.
+    It is not explicitly mentioned in input data.. But, ML hypothesis functions use it.
+    So, we have to take care of it separately. Line 36 takes care of it.
+    """
+    hyp_val = 0
+    for i in range(len(parameter_vector) - 1):
+        hyp_val += data_input_tuple[i]*parameter_vector[i+1]
+    hyp_val += parameter_vector[0]
+    return hyp_val
+
+
+def output(example_no, data_set):
+    """
+    :param data_set: test data or train data
+    :param example_no: example whose output is to be fetched
+    :return: output for that example
+    """
+    if data_set == 'train':
+        return train_data[example_no][1]
+    elif data_set == 'test':
+        return test_data[example_no][1]
+
+
+def calculate_hypothesis_value(example_no, data_set):
+    """
+    Calculates hypothesis value for a given example
+    :param data_set: test data or train_data
+    :param example_no: example whose hypothesis value is to be calculated
+    :return: hypothesis value for that example
+    """
+    if data_set == "train":
+        return _hypothesis_value(train_data[example_no][0])
+    elif data_set == "test":
+        return _hypothesis_value(test_data[example_no][0])
+
+
+def summation_of_cost_derivative(index, end=m):
+    """
+    Calculates the sum of cost function derivative
+    :param index: index wrt derivative is being calculated
+    :param end: value where summation ends, default is m, number of examples
+    :return: Returns the summation of cost derivative
+    Note: If index is -1, this means we are calculating summation wrt to biased parameter.
+    """
+    summation_value = 0
+    for i in range(end):
+        if index == -1:
+            summation_value += _error(i)
+        else:
+            summation_value += _error(i)*train_data[i][0][index]
+    return summation_value
+
+
+def get_cost_derivative(index):
+    """
+    :param index: index of the parameter vector wrt to derivative is to be calculated
+    :return: derivative wrt to that index
+    Note: If index is -1, this means we are calculating summation wrt to biased parameter.
+    """
+    cost_derivative_value = summation_of_cost_derivative(index, m)/m
+    return cost_derivative_value
+
+
+def run_gradient_descent():
+    global parameter_vector
+    # Tune these values to set a tolerance value for predicted output
+    absolute_error_limit = 0.000002
+    relative_error_limit = 0
+    j = 0
+    while True:
+        j += 1
+        temp_parameter_vector = [0, 0, 0, 0]
+        for i in range(0, len(parameter_vector)):
+            cost_derivative = get_cost_derivative(i-1)
+            temp_parameter_vector[i] = parameter_vector[i] - \
+                LEARNING_RATE*cost_derivative
+        if numpy.allclose(parameter_vector, temp_parameter_vector,
+                          atol=absolute_error_limit, rtol=relative_error_limit):
+            break
+        parameter_vector = temp_parameter_vector
+    print("Number of iterations:", j)
+
+
+def test_gradient_descent():
+    for i in range(len(test_data)):
+        print("Actual output value:", output(i, 'test'))
+        print("Hypothesis output:", calculate_hypothesis_value(i, 'test'))
+
+
+if __name__ == '__main__':
+    run_gradient_descent()
+    print("\nTesting gradient descent for a linear hypothesis function.\n")
+    test_gradient_descent()