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data_loader.py
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data_loader.py
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import math
import numpy
def replace_missing_values(line):
avg_value = 0
num_values = 0
missing_values = []
for i, val in enumerate(line):
if val != "?":
avg_value += float(val)
num_values += 1
else:
missing_values.append(i)
avg_value = avg_value / num_values
for index in missing_values:
line[index] = str(avg_value)
return line
def get_iris_data():
file = open("./Data/iris.data", "r")
data = []
num_input_nodes = 4
num_hidden_nodes = 4
num_hidden_layers = 1
num_output_nodes = 3
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split(",")
input_layer = numpy.array([float(x) for x in line[0:4]])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [0, 0, 0]
out[int(line[4])] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("iris data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_heart_disease():
file = open("./Data/processed.cleveland.data", "r")
data = []
num_input_nodes = 13
num_hidden_nodes = 6
num_hidden_layers = 1
num_output_nodes = 5
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split(",")
try:
input_layer = numpy.array([float(x) for x in line[:13]])
except:
input_layer = numpy.array([float(x) for x in replace_missing_values(line[:13])])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [0, 0, 0, 0, 0]
out[int(line[13])] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("Heart disease data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_wine_data():
file = open("./Data/wine.data", "r")
data = []
num_input_nodes = 13
num_hidden_nodes = 10
num_hidden_layers = 1
num_output_nodes = 3
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split(",")
input_layer = numpy.array([float(x) for x in line[1:]])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [0, 0, 0]
out[int(line[0]) - 1] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("wine data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_soybean_large():
file = open("./Data/soybean-large.data", "r")
data = []
num_input_nodes = 35
num_hidden_nodes = 12
num_hidden_layers = 1
num_output_nodes = 19
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split(",")
try:
input_layer = numpy.array([float(x) for x in line[1:]])
except:
input_layer = numpy.array([float(x) for x in replace_missing_values(line[1:])])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [0 for _ in range(19)]
out[int(line[0])] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("Soyabean data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_breast_cancer_data():
file = open("./Data/wdbc.data", "r")
data = []
num_input_nodes = 30
num_hidden_nodes = 25
num_hidden_layers = 1
num_output_nodes = 2
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split(",")
input_layer = numpy.array([float(x) for x in line[2:]])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [0, 0]
if line[1] == "M":
out[0] = 1
else:
out[1] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("wdbc data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_coil_2000():
file = open("./Data/ticdata2000.data", "r")
data = []
num_input_nodes = 85
num_hidden_nodes = 85
num_hidden_layers = 1
num_output_nodes = 1
min_value = math.inf
max_value = -math.inf
for line in file:
line = line.strip().split("\t")
try:
input_layer = numpy.array([float(x) for x in line[:85]])
except:
input_layer = numpy.array([float(x) for x in replace_missing_values(line[:85])])
min_value = min(numpy.amin(input_layer), min_value)
max_value = max(numpy.amax(input_layer), max_value)
out = [float(line[85])]
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
for i in range(len(data)):
for v in range(len(data[i][0])):
data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("Coil data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_crime():
file = open("./Data/communities.data", "r")
data = []
num_input_nodes = 122
num_hidden_nodes = 122
num_hidden_layers = 1
num_output_nodes = 1
# min_value = math.inf
# max_value = -math.inf
for line in file:
line = line.strip().split(",")[5:]
try:
input_layer = numpy.array([float(x) for x in line[:122]])
except:
input_layer = numpy.array([float(x) for x in replace_missing_values(line[:122])])
# min_value = min(numpy.amin(input_layer), min_value)
# max_value = max(numpy.amax(input_layer), max_value)
out = [float(line[122])]
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
# for i in range(len(data)):
# for v in range(len(data[i][0])):
# data[i][0][v] = (data[i][0][v] - min_value) / (max_value - min_value)
print("Crime data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data
def get_cnae9_data():
file = open("./Data/CNAE-9.data")
data = []
num_input_nodes = 856
num_hidden_nodes = 30
num_hidden_layers = 1
num_output_nodes = 9
for line in file:
line = line.strip().split(",")
input_layer = numpy.array([float(x) for x in line[1:]])
out = [0 for _ in range(9)]
out[int(line[0]) - 1] = 1
output_layer = numpy.array(out)
data.append((input_layer, output_layer))
print("CNAE9 data loaded")
return (num_input_nodes, num_hidden_nodes, num_hidden_layers, num_output_nodes), data