from dataset.store import loadCSV
import numpy as np
from sknn.mlp import Classifier, Layer
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from libs.roc import getScore
from store import saveXLSX
from sklearn import metrics
from datetime import datetime
folder = 'dataset/2 classes/breast-cancer-wisconsin'
file = 'breast-cancer-wisconsin.csv'
# ====================================================
data = np.asarray(loadCSV(folder, file))
y, X = data[:, 1], data[:, 2:].astype(float)
y[y == 'M'] = 0
y[y == 'B'] = 1
y = y.astype(int)
# ====================================================
learning_rate = [0.01] # [0.001, 0.005, 0.01, 0.05]
learning_rule = ['sgd'] # ['sgd', 'adagrad']
hidden_units = [8] # [8, 16, 32, 64, 128, 256, 512]
n_iters = [16] # [16, 32, 64, 128, 256, 512, 1024]
output = {}
output['score'] = [[
from dataset.store import loadCSV
import numpy as np
from sknn.mlp import Classifier, Layer
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from libs.roc import getScore
from store import saveXLSX
import datetime
folder = 'dataset/3 classes/wine'
file = 'wine.data.csv'
# ====================================================
data = np.asarray(loadCSV(folder, file))
y, X = data[:, 0].astype(int), data[:, 1:].astype(float)
y[y == 1] = 0
y[y == 2] = 1
y[y == 3] = 2
# ====================================================
learning_rate = [0.001, 0.005, 0.01, 0.05]
learning_rule = ['sgd', 'adagrad']
hidden_units = [8, 16, 32, 64, 128, 256, 512]
n_iters = [16, 32, 64, 128, 256, 512, 1024]
output = {}
output['score'] = [['learning_rate', 'learning_rule', 'hidden_units', 'n_iters', '#', 'acc', 'u', 'VUS_1', 'VUS_2', 'TP1', 'F12', 'F13', 'F21', 'TP2', 'F23', 'F31', 'F32', 'TP3' 'countingtime']]
import numpy as np
from dataset.store import loadCSV, saveXLSX
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
import tensorflow as tf
import math
from datetime import datetime
from libs.roc import getScore
from sklearn import metrics
folder = 'dataset/2 classes/german_credit'
file = 'german_credit.csv'
# ====================================================
data = np.asarray(loadCSV(folder + '/' + file))
y, X = data[:, 0], data[:, 1:].astype(float)
y = np.asarray([[1, 0] if (_ == '0') else [0, 1] for _ in y])
# ====================================================
learning_rate = [0.001] # [0.001, 0.005, 0.01, 0.05]
learning_rule = ['adagrad'] # ['adagrad', 'sgd']
hidden_units = [8] # [8, 16, 32, 64, 128, 256, 512]
hidden_layers = [2] # [2, 3, 4]
n_iters = [16] # [16, 32, 64, 128, 256, 512, 1024]
# ====================================================
def initWeight(size):
return tf.Variable(tf.zeros(size))
def initWeight_U(size):
import numpy as np
from dataset.store import loadCSV, saveXLSX
from sklearn.cross_validation import train_test_split
import tensorflow as tf
import math
from datetime import datetime
from libs.roc import getScore
from sklearn import metrics
folder = 'dataset/2 classes/breast-cancer-wisconsin'
file = 'breast-cancer-wisconsin.csv'
data = np.asarray(loadCSV(folder + '/' + file))
y, X = data[:, 1], data[:, 2:].astype(float)
y = np.asarray([[1, 0] if (_ == 'M') else [0, 1] for _ in y])
# ====================================================
learning_rate = [0.001, 0.005, 0.01, 0.05]
learning_rule = ['sgd', 'adagrad']
hidden_units = [8, 16, 32, 64, 128, 256, 512]
n_iters = [16, 32, 64, 128, 256, 512, 1024]
# ====================================================
def initWeight(size):
return tf.Variable(tf.zeros(size))
def initWeight_U(size):
minR, maxR = 0, 0
if (len(size) == 1):
minR, maxR = -1/size[0], 1/size[0]
elif (len(size) == 2):
