from math import sqrt
from math import pi
from math import exp

# Split the dataset by class values, returns a dictionary


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in separated):
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated

# Calculate the mean of a list of numbers


def mean(numbers):
    return sum(numbers)/float(len(numbers))

# Calculate the standard deviation of a list of numbers


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)
    return sqrt(variance)

# Calculate the mean, stdev and count for each column in a dataset


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column))
                 for column in zip(*dataset)]
    del(summaries[-1])
    return summaries

# Split dataset by class then calculate statistics for each row


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries

# Calculate the Gaussian probability distribution function for x


def calculate_probability(x, mean, stdev):
    exponent = exp(-((x-mean)**2 / (2 * stdev**2)))
    return (1 / (sqrt(2 * pi) * stdev)) * exponent

# Calculate the probabilities of predicting each class for a given row


def calculate_class_probabilities(summaries, row):
    total_rows = sum([summaries[label][0][2] for label in summaries])
    probabilities = dict()
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = summaries[class_value][0][2] / \
            float(total_rows)
        for i in range(len(class_summaries)):
            mean, stdev, _ = class_summaries[i]
            probabilities[class_value] *= calculate_probability(
                row[i], mean, stdev)
    return probabilities


# Test calculating class probabilities
dataset = [[3.393533211, 2.331273381, 0],
           [3.110073483, 1.781539638, 0],
           [1.343808831, 3.368360954, 0],
           [3.582294042, 4.67917911, 0],
           [2.280362439, 2.866990263, 0],
           [7.423436942, 4.696522875, 1],
           [5.745051997, 3.533989803, 1],
           [9.172168622, 2.511101045, 1],
           [7.792783481, 3.424088941, 1],
           [7.939820817, 0.791637231, 1]]
summaries = summarize_by_class(dataset)
probabilities = calculate_class_probabilities(summaries, dataset[0])
print(probabilities)

caca0d9dd64051a90c97e6cc1cfbcbab6dae8606
<mask token>


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if class_value not in separated:
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated


def mean(numbers):
    return sum(numbers) / float(len(numbers))


<mask token>


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column)) for column in
        zip(*dataset)]
    del summaries[-1]
    return summaries


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries


<mask token>

<mask token>


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if class_value not in separated:
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated


def mean(numbers):
    return sum(numbers) / float(len(numbers))


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([((x - avg) ** 2) for x in numbers]) / float(len(numbers
        ) - 1)
    return sqrt(variance)


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column)) for column in
        zip(*dataset)]
    del summaries[-1]
    return summaries


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries


def calculate_probability(x, mean, stdev):
    exponent = exp(-((x - mean) ** 2 / (2 * stdev ** 2)))
    return 1 / (sqrt(2 * pi) * stdev) * exponent


<mask token>

<mask token>


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if class_value not in separated:
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated


def mean(numbers):
    return sum(numbers) / float(len(numbers))


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([((x - avg) ** 2) for x in numbers]) / float(len(numbers
        ) - 1)
    return sqrt(variance)


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column)) for column in
        zip(*dataset)]
    del summaries[-1]
    return summaries


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries


def calculate_probability(x, mean, stdev):
    exponent = exp(-((x - mean) ** 2 / (2 * stdev ** 2)))
    return 1 / (sqrt(2 * pi) * stdev) * exponent


def calculate_class_probabilities(summaries, row):
    total_rows = sum([summaries[label][0][2] for label in summaries])
    probabilities = dict()
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = summaries[class_value][0][2] / float(
            total_rows)
        for i in range(len(class_summaries)):
            mean, stdev, _ = class_summaries[i]
            probabilities[class_value] *= calculate_probability(row[i],
                mean, stdev)
    return probabilities


<mask token>

<mask token>


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if class_value not in separated:
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated


def mean(numbers):
    return sum(numbers) / float(len(numbers))


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([((x - avg) ** 2) for x in numbers]) / float(len(numbers
        ) - 1)
    return sqrt(variance)


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column)) for column in
        zip(*dataset)]
    del summaries[-1]
    return summaries


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries


def calculate_probability(x, mean, stdev):
    exponent = exp(-((x - mean) ** 2 / (2 * stdev ** 2)))
    return 1 / (sqrt(2 * pi) * stdev) * exponent


def calculate_class_probabilities(summaries, row):
    total_rows = sum([summaries[label][0][2] for label in summaries])
    probabilities = dict()
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = summaries[class_value][0][2] / float(
            total_rows)
        for i in range(len(class_summaries)):
            mean, stdev, _ = class_summaries[i]
            probabilities[class_value] *= calculate_probability(row[i],
                mean, stdev)
    return probabilities


dataset = [[3.393533211, 2.331273381, 0], [3.110073483, 1.781539638, 0], [
    1.343808831, 3.368360954, 0], [3.582294042, 4.67917911, 0], [
    2.280362439, 2.866990263, 0], [7.423436942, 4.696522875, 1], [
    5.745051997, 3.533989803, 1], [9.172168622, 2.511101045, 1], [
    7.792783481, 3.424088941, 1], [7.939820817, 0.791637231, 1]]
summaries = summarize_by_class(dataset)
probabilities = calculate_class_probabilities(summaries, dataset[0])
print(probabilities)

from math import sqrt
from math import pi
from math import exp

# Split the dataset by class values, returns a dictionary


def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in separated):
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated

# Calculate the mean of a list of numbers


def mean(numbers):
    return sum(numbers)/float(len(numbers))

# Calculate the standard deviation of a list of numbers


def stdev(numbers):
    avg = mean(numbers)
    variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)
    return sqrt(variance)

# Calculate the mean, stdev and count for each column in a dataset


def summarize_dataset(dataset):
    summaries = [(mean(column), stdev(column), len(column))
                 for column in zip(*dataset)]
    del(summaries[-1])
    return summaries

# Split dataset by class then calculate statistics for each row


def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value, rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries

# Calculate the Gaussian probability distribution function for x


def calculate_probability(x, mean, stdev):
    exponent = exp(-((x-mean)**2 / (2 * stdev**2)))
    return (1 / (sqrt(2 * pi) * stdev)) * exponent

# Calculate the probabilities of predicting each class for a given row


def calculate_class_probabilities(summaries, row):
    total_rows = sum([summaries[label][0][2] for label in summaries])
    probabilities = dict()
    for class_value, class_summaries in summaries.items():
        probabilities[class_value] = summaries[class_value][0][2] / \
            float(total_rows)
        for i in range(len(class_summaries)):
            mean, stdev, _ = class_summaries[i]
            probabilities[class_value] *= calculate_probability(
                row[i], mean, stdev)
    return probabilities


# Test calculating class probabilities
dataset = [[3.393533211, 2.331273381, 0],
           [3.110073483, 1.781539638, 0],
           [1.343808831, 3.368360954, 0],
           [3.582294042, 4.67917911, 0],
           [2.280362439, 2.866990263, 0],
           [7.423436942, 4.696522875, 1],
           [5.745051997, 3.533989803, 1],
           [9.172168622, 2.511101045, 1],
           [7.792783481, 3.424088941, 1],
           [7.939820817, 0.791637231, 1]]
summaries = summarize_by_class(dataset)
probabilities = calculate_class_probabilities(summaries, dataset[0])
print(probabilities)
