def cross_validation_scan():
# v1_train = scan("res/x_validation/v1.train")
# v1_test = scan("res/x_validation/v1.test")
# v2_train = scan("res/x_validation/v2.train")
# v2_test = scan("res/x_validation/v2.test")
# v3_train = scan("res/x_validation/v3.train")
# v3_test = scan("res/x_validation/v3.test")
# v4_train = scan("res/x_validation/v4.train")
# v4_test = scan("res/x_validation/v4.test")
# v5_train = scan("res/x_validation/v5.train")
# v5_test = scan("res/x_validation/v5.test")
# v6_train = scan("res/x_validation/v6.train")
# v6_test = scan("res/x_validation/v6.test")
v1_train = scan("res/xv/f1.train")
v1_test = scan("res/xv/f1.xv")
v2_train = scan("res/xv/f2.train")
v2_test = scan("res/xv/f2.xv")
v3_train = scan("res/xv/f3.train")
v3_test = scan("res/xv/f3.xv")
v4_train = scan("res/xv/f4.train")
v4_test = scan("res/xv/f4.xv")
v5_train = scan("res/xv/f5.train")
v5_test = scan("res/xv/f5.xv")
v6_train = scan("res/xv/f6.train")
v6_test = scan("res/xv/f6.xv")
return {"v1": [v1_train, v1_test], "v2": [v2_train, v2_test], "v3": [v3_train, v3_test],
"v4": [v4_train, v4_test], "v5": [v5_train, v5_test], "v6": [v6_train, v6_test]}
import warnings
warnings.simplefilter("ignore", RuntimeWarning)
from Scanner import scan
from Perceptron import perceptron, test_perceptron
from LogRegClass import gradient_descent_logistic_reg, test_log_reg_class
from SVM import svm, test_svm
from Helper import limit_features, feature_scaling, standardiztion
mu = 0.25
epoch = 10
train = scan('res/training1.data')
train_data = train['d']
train_y = train['l']
test1 = scan('res/test.data/AHU 13.csv')
test1_data = test1['d']
test1_y = test1['l']
test2 = scan('res/test.data/AHU38 1.csv')
test2_data = test2['d']
test2_y = test2['l']
test3 = scan('res/test.data/AHU19B 1.csv')
test3_data = test3['d']
test3_y = test3['l']
train_data = standardiztion(train_data)
test1_data = standardiztion(test1_data)
test2_data = standardiztion(test2_data)
test3_data = standardiztion(test3_data)
train_data = limit_features(train_data,
[36, 24, 22, 42, 402, 52, 32, 29, 20, 51])
def cross_validation_scan():
v1_train = scan("res/x_validation/v1.train")
v1_test = scan("res/x_validation/v1.test")
v2_train = scan("res/x_validation/v2.train")
v2_test = scan("res/x_validation/v2.test")
v3_train = scan("res/x_validation/v3.train")
v3_test = scan("res/x_validation/v3.test")
v4_train = scan("res/x_validation/v4.train")
v4_test = scan("res/x_validation/v4.test")
v5_train = scan("res/x_validation/v5.train")
v5_test = scan("res/x_validation/v5.test")
v6_train = scan("res/x_validation/v6.train")
v6_test = scan("res/x_validation/v6.test")
return {
"v1": [v1_train, v1_test],
"v2": [v2_train, v2_test],
"v3": [v3_train, v3_test],
"v4": [v4_train, v4_test],
"v5": [v5_train, v5_test],
"v6": [v6_train, v6_test]
}
import warnings
warnings.simplefilter("ignore", RuntimeWarning)
from Scanner import scan
from LogRegClass import gradient_descent_logistic_reg, test_weight
epochs = 30
sigma = 32
#{'d': training_data, 'l': y_vals}
train = scan("res/a5a.train")
test = scan("res/a5a.test")
# get the training info
train_data = train['d']
train_labels = train['l']
# get the test info
test_data = test['d']
test_labels = test['l']
w = gradient_descent_logistic_reg(train_data, train_labels, epochs, sigma)
# parse w
weights = w['w']
log_likelihood = w['o']
# run the test
c = test_weight(test_data, test_labels, weights)
# get the accuracy
accuracy = c["correct"] / (c["correct"] + c["wrong"])
import warnings
warnings.simplefilter("ignore", RuntimeWarning)
from Scanner import scan
from Winnow import winnow, test_winnow
from Helper import limit_features, feature_scaling, standardiztion
r = 2
epoch = 1
data = scan('res/training1.data')
test_data = data['d']
test_y = data['l']
test_data = standardiztion(test_data)
test_data = limit_features(test_data,
[36, 24, 22, 42, 402, 52, 32, 29, 20, 51])
print("WINNOW")
weight_perceptron = winnow(test_data, test_y, epoch, mu)
results_perceptron = test_winnow(test_data, test_y, weight_perceptron)
print("POSITIVE: " + str(test_y.count(1)))
print("NEGATIVE: " + str(test_y.count(-1)))
print("")
print("WEIGHT: " + str(weight_perceptron))
# get the accuracy
accuracy = results_perceptron["correct"] / (results_perceptron["correct"] +
def scanNow(event):
scan(setlist)
strVar.set("Scan Complete")