Naive_bayes algorithms

Naive_Bayes

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+# Make Predictions with Naive Bayes On The Iris Dataset
+from csv import reader
+from math import sqrt
+from math import exp
+from math import pi
+
+# Load a CSV file
+def load_csv(filename):
+	dataset = list()
+	with open(filename, 'r') as file:
+		csv_reader = reader(file)
+		for row in csv_reader:
+			if not row:
+				continue
+			dataset.append(row)
+	return dataset
+
+# Convert string column to float
+def str_column_to_float(dataset, column):
+	for row in dataset:
+		row[column] = float(row[column].strip())
+
+# Convert string column to integer
+def str_column_to_int(dataset, column):
+	class_values = [row[column] for row in dataset]
+	unique = set(class_values)
+	lookup = dict()
+	for i, value in enumerate(unique):
+		lookup[value] = i
+		print('[%s] => %d' % (value, i))
+	for row in dataset:
+		row[column] = lookup[row[column]]
+	return lookup
+
+# 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
+
+# Predict the class for a given row
+def predict(summaries, row):
+	probabilities = calculate_class_probabilities(summaries, row)
+	best_label, best_prob = None, -1
+	for class_value, probability in probabilities.items():
+		if best_label is None or probability > best_prob:
+			best_prob = probability
+			best_label = class_value
+	return best_label
+
+# Make a prediction with Naive Bayes on Iris Dataset
+filename = 'iris.csv'
+dataset = load_csv(filename)
+for i in range(len(dataset[0])-1):
+	str_column_to_float(dataset, i)
+# convert class column to integers
+str_column_to_int(dataset, len(dataset[0])-1)
+# fit model
+model = summarize_by_class(dataset)
+# define a new record
+row = [5.7,2.9,4.2,1.3]
+# predict the label
+label = predict(model, row)
+print('Data=%s, Predicted: %s' % (row, label))