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vanet (1).txt
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vanet (1).txt
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import pandas
import numpy as np
import csv
from random import seed
from random import randrange
from csv import reader
from math import sqrt
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
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
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
for row in dataset:
row[column] = lookup[row[column]]
return lookup
def cross_validation_split(dataset, n_folds):
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_folds)
for i 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)
return dataset_split
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
def evaluate_algorithm(dataset, algorithm, n_folds, *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)
test_set.append(row_copy)
row_copy[-1] = None
predicted = algorithm(train_set, test_set, *args)
actual = [row[-1] for row in fold]
accuracy = accuracy_metric(actual, predicted)
scores.append(accuracy)
return scores
def test_split(index, value, dataset):
left, right = list(), list()
for row in dataset:
if row[index] < value:
left.append(row)
else:
right.append(row)
return left, right
# Calculate the Gini index
def gini_index(groups, classes):
n_instances = float(sum([len(group) for group in groups]))
gini = 0.0
for group in groups:
size = float(len(group))
if size == 0:
continue
score = 0.0
for class_val in classes:
p = [row[-1] for row in group].count(class_val) / size
score += p * p
gini += (1.0 - score) * (size / n_instances)
return gini
def get_split(dataset, n_features):
class_values = list(set(row[-1] for row in dataset))
b_index, b_value, b_score, b_groups = 999, 999, 999, None
features = list()
while len(features) < n_features:
index = randrange(len(dataset[0])-1)
if index not in features:
features.append(index)
for index in features:
for row in dataset:
groups = test_split(index, row[index], dataset)
gini = gini_index(groups, class_values)
if gini < b_score:
b_index, b_value, b_score, b_groups = index, row[index], gini, groups
return {'index':b_index, 'value':b_value, 'groups':b_groups}
def to_terminal(group):
outcomes = [row[-1] for row in group]
return max(set(outcomes), key=outcomes.count)
def split(node, max_depth, min_size, n_features, depth):
left, right = node['groups']
del(node['groups'])
if not left or not right:
node['left'] = node['right'] = to_terminal(left + right)
return
if depth >= max_depth:
node['left'], node['right'] = to_terminal(left), to_terminal(right)
return
if len(left) <= min_size:
node['left'] = to_terminal(left)
else:
node['left'] = get_split(left, n_features)
split(node['left'], max_depth, min_size, n_features, depth+1)
if len(right) <= min_size:
node['right'] = to_terminal(right)
else:
node['right'] = get_split(right, n_features)
split(node['right'], max_depth, min_size, n_features, depth+1)
def build_tree(train, max_depth, min_size, n_features):
root = get_split(train, n_features)
split(root, max_depth, min_size, n_features, 1)
return root
def predict(node, row):
if row[node['index']] < node['value']:
if isinstance(node['left'], dict):
return predict(node['left'], row)
else:
return node['left']
else:
if isinstance(node['right'], dict):
return predict(node['right'], row)
else:
return node['right']
def subsample(dataset, ratio):
sample = list()
n_sample = round(len(dataset) * ratio)
while len(sample) < n_sample:
index = randrange(len(dataset))
sample.append(dataset[index])
return sample
def bagging_predict(trees, row):
predictions = [predict(tree, row) for tree in trees]
return max(set(predictions), key=predictions.count)
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
trees = list()
for i in range(n_trees):
sample = subsample(train, sample_size)
tree = build_tree(sample, max_depth, min_size, n_features)
trees.append(tree)
predictions = [bagging_predict(trees, row) for row in test]
return(predictions)
seed(2)
filename = 'vanet1.csv'
dataset = load_csv(filename)
for i in range(0, len(dataset[0])-1):
str_column_to_float(dataset, i)
str_column_to_int(dataset, len(dataset[0])-1)
n_folds = 5
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = int(sqrt(len(dataset[0])-1))
for n_trees in [1, 5, 10]:
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
print('Trees: %d' % n_trees)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
a=pandas.read_csv("vanet1.csv")
datapath="vanet1.csv"
def get_data(path):
f=open(path,"r")
reader=csv.reader(f)
header=next(reader)
data= []
for line in reader:
data.append([float(x) for x in line])
return header, np.transpose(data)
header, data=get_data(datapath)
x=data[2]
y=data[3]
z=data[4]
%pylab inline
scatter(x, y, c=z)