




import numpy

# List of input, output pairs
UpperCAmelCase_						=(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase_						=(((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase_						=[2, 4, 1, 5]
UpperCAmelCase_						=len(train_data)
UpperCAmelCase_						=0.0_09



def       UpperCAmelCase       (     _snake_case						, _snake_case="train"							):
							return calculate_hypothesis_value(_snake_case						, _snake_case							) - output(
							    _snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		0
							for i in range(len(_snake_case							) - 1							):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val



def       UpperCAmelCase       (     _snake_case						, _snake_case							):







							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None



def       UpperCAmelCase       (     _snake_case						, _snake_case							):







							if data_set == "train":
														return _hypothesis_value(train_data[example_no][0]							)
							elif data_set == "test":
														return _hypothesis_value(test_data[example_no][0]							)
							return None



def       UpperCAmelCase       (     _snake_case						, _snake_case=m							):
							lowerCAmelCase   =		0
							for i in range(_snake_case							):
														if index == -1:
																					summation_value += _error(_snake_case							)
														else:
																					summation_value += _error(_snake_case							) * train_data[i][0][index]
							return summation_value



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		summation_of_cost_derivative(_snake_case						, _snake_case							) / m
							return cost_derivative_value



def       UpperCAmelCase       (     ):
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							lowerCAmelCase   =		0.000002
							lowerCAmelCase   =		0
							lowerCAmelCase   =		0
							while True:
														j += 1
														lowerCAmelCase   =		[0, 0, 0, 0]
														for i in range(0						, len(_snake_case							)							):
																					lowerCAmelCase   =		get_cost_derivative(i - 1							)
																					lowerCAmelCase   =		(
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    _snake_case						, _snake_case						, atol=_snake_case						, rtol=_snake_case						, ):
																					break
														lowerCAmelCase   =		temp_parameter_vector
							print(('''Number of iterations:''', j)							)



def       UpperCAmelCase       (     ):







							for i in range(len(_snake_case							)							):
														print(('''Actual output value:''', output(_snake_case						, '''test'''							))							)
														print(('''Hypothesis output:''', calculate_hypothesis_value(_snake_case						, '''test'''							))							)


if __name__ == "__main__":
			run_gradient_descent()
			print("""\nTesting gradient descent for a linear hypothesis function.\n""")
			test_gradient_descent()









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



