import pandas as pd
from data_prepared import read_data, prepare_data_div
from testing import test_with_id
from pegasos import pegasos_
f = open(
"/Users/noch/Documents/workspace/data_challenge/result/Yte_pegasos_13.csv",
"a+")
nm_char = [6, 6, 5]
lmda = [10**(-5), 0.0001, 10**(-5)]
epoch = [400000, 300000, 300000]
for i in range(3):
isTr = 1
Xtr = read_data("Xtr" + str(i), isTr)
Ytr = read_data("Ytr" + str(i), isTr)
Ytr['Bound'][Ytr['Bound'] == 0] = -1
isTr = 0
Xte = read_data("Xte" + str(i), isTr)
Xte['Id'] = pd.DataFrame({'Id': range(i * 1000, (i + 1) * 1000)})
print("preparing data:" + str(i))
Xtr_p = prepare_data_div(Xtr, nm_char[i])
Xtr_p['Bound'] = Ytr['Bound']
Xte_p = prepare_data_div(pd.DataFrame(Xte['DNA']), nm_char[i])
Xte_p['Id'] = Xte['Id']
Xtr_p = Xtr_p.sample(frac=1)
Y_predicted = []
for i, x_i in enumerate(X_te):
# print("sum: " + str(np.sum((md.Alpha * md.Y) * kernel(md.X, x_i))))
result = np.sum((md.Alpha * md.Y) * kernel(md.X, x_i)) - md.b
#print("result: " + str(result))
if result <= 0:
Y_predicted.append(-1)
elif result > 0:
Y_predicted.append(1)
return Y_predicted
isTr = 1
for i in range(3):
X = read_data("Xtr" + str(i), isTr)
Y = read_data("Ytr" + str(i), isTr)
max_info = ""
max_predic = 0
Y['Bound'][Y['Bound'] == 0] = -1
f = open(
"/Users/noch/Documents/workspace/data_challenge/result/console_svm_SMO_ker_linear.txt",
"a+")
#f= open("/home/jibril/Desktop/data_challenge/result/console_svm_SMO_ker_linear.txt","a+")
print("\n testing on Xtr" + str(i) + ", Ytr" + str(i))
for k in range(2, 3):
