initial commit

iris.txt

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+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica

task2.py

@@ -0,0 +1,166 @@
+from csv import reader
+from random import seed
+from random import randrange
+from math import sqrt
+from math import exp
+from math import pi
+
+def read_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_ids = [row[column] for row in dataset]
+	unique = set(class_ids)
+	lookup = dict()
+	print("Class IDs:")
+	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 a dataset into n folds
+def cross_validation_split(dataset, n_folds):
+	dataset_split = list()
+	dataset_copy = list(dataset)
+	fold_size = int(len(dataset) / n_folds)
+	for _ in range(n_folds):
+		fold = list()
+		while len(fold) < fold_size:
+			index = randrange(len(dataset_copy))
+			fold.append(dataset_copy.pop(index))
+		dataset_split.append(fold)
+	# print(dataset_split)
+	return dataset_split
+
+# calculate accuracy (in per cent)
+def accuracy_metric(actual, predicted):
+	correct = 0
+	for i in range(len(actual)):
+		if actual[i] == predicted[i]:
+			correct += 1
+	return correct / float(len(actual)) * 100.0
+
+# evaluate using a cross validation split
+def evaluate_algorithm(dataset, n_folds, algorithm, *args):
+	folds = cross_validation_split(dataset, n_folds)
+	scores = list()
+	for fold in folds:
+		train_set = list(folds)
+		train_set.remove(fold)
+		train_set = sum(train_set, [])
+		test_set = list()
+		for row in fold:
+			row_copy = list(row)
+			# get a row from each fold
+			test_set.append(row_copy)
+			# we don't know the class yet
+			row_copy[-1] = None
+		predicted = algorithm(train_set, test_set, *args)
+		actual = [row[-1] for row in fold]
+		# calculate accuracy
+		accuracy = accuracy_metric(actual, predicted)
+		scores.append(accuracy)
+	return scores
+
+# split the dataset by class values, return a dictionary
+def separate_by_class(dataset):
+	separated = dict()
+	for i in range(len(dataset)):
+		vector = dataset[i]
+		class_id = vector[-1]
+		if (class_id not in separated):
+			separated[class_id] = list()
+		separated[class_id].append(vector)
+	return separated
+
+# mean of a list of numbers
+def mean(numbers):
+	return sum(numbers)/float(len(numbers))
+
+# calculate the standard deviation (sigma) 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)
+
+# summarize - the mean, standard deviation (sigma) 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
+
+# calculate statistics for each row of the dataset split by class
+def summarize_by_class(dataset):
+	separated = separate_by_class(dataset)
+	summaries = dict()
+	for class_id, rows in separated.items():
+		summaries[class_id] = summarize_dataset(rows)
+	return summaries
+
+# Gaussian probability distribution function for x
+def gaussian_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 class_probability_predictions(summaries, row):
+	total_rows = sum([summaries[label][0][2] for label in summaries])
+	probabilities = dict()
+	for class_id, class_summaries in summaries.items():
+		# for iris, we only care about 3 classes (0-2)
+		probabilities[class_id] = summaries[class_id][0][2]/float(total_rows)
+		for i in range(len(class_summaries)):
+			mean, stdev, _ = class_summaries[i]
+			probabilities[class_id] *= gaussian_probability(row[i], mean, stdev)
+	return probabilities
+
+# predict the class for a given row
+def predict(summaries, row):
+	probabilities = class_probability_predictions(summaries, row)
+	best_label, best_probability = None, -1
+	for class_id, probability in probabilities.items():
+		if best_label is None or probability > best_probability:
+			best_probability= probability
+			best_label = class_id
+	return best_label
+
+# da thing, the magic
+def naive_bayes(train, test):
+	summarize = summarize_by_class(train)
+	predictions = list()
+	for row in test:
+		output = predict(summarize, row)
+		predictions.append(output)
+	return(predictions)
+
+# run naive bayes on the example dataset
+seed(1)
+filename = 'iris.txt'
+dataset = read_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)
+
+# evaluate algorithm
+n_folds = 10
+scores = evaluate_algorithm(dataset, n_folds, naive_bayes)
+print('Scores: %s' % scores)
+print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))