def get_data():
raw_data = []
# loop through the csv and get each row
with open('data/forestFireData.csv', 'rb') as csvfile:
# reader reads the csv
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
# append to the list the row
for row in reader:
raw_data.append(row)
# get the header from the raw_data
header = raw_data[0]
# get the rest of the data
data = raw_data[1:len(raw_data)]
# make the nodes
nodes = []
# loop through the dimensions and make a node for each one
for i in range(0, len(header)):
node = BNNode(['0', '1'], util.col(data, i))
node.col_index = i
node.name = header[i]
nodes.append(node)
# set the classes
classes = nodes[-1]
del nodes[-1]
# return everything
return (nodes, data, classes.row_values)
def test_remove_parental_diff_from_pred(self):
a = BNNode([], [])
b = BNNode([], [])
c = BNNode([], [])
pred = [a,b,c]
node = BNNode([], [])
node.parents = [c]
new_pred = BN.remove_parental_diff_from_pred(node, pred)
self.assertEquals(2, len(new_pred))
self.assertEquals(True, new_pred[0] == a)
self.assertEquals(True, new_pred[1] == b)
def test_get_list_of_cols(self):
n1 = BNNode([], [])
n1.col_index = 0
n2 = BNNode([], [])
n2.col_index = 1
n3 = BNNode([], [])
n3.col_index = 2
n1.parents.append(n2)
n1.parents.append(n3)
col_list = BNClassifier.get_list_of_cols(n1)
self.assertEquals(1, col_list[0])
self.assertEquals(2, col_list[1])
def test_get_count(self):
n1 = BNNode([], [])
n1.col_index = 0
n2 = BNNode([], [])
n2.col_index = 1
n3 = BNNode([], [])
n3.col_index = 2
n1.parents.append(n2)
n1.parents.append(n3)
cols = [1,2]
row = [0,1,0]
data = [[0,1,0], [0,1,0], [0,0,0], [0,1,0]]
classes = ['a', 'b', 'a', 'b']
poss = ['a', 'b']
counts = BNClassifier.get_count(row, n1, data, classes, poss)
