def test_load(self):
self.assertEqual(
'File 123 loaded',
solution.load('1', self.list_of_names_file1)
)
self.assertEqual(
'There is no list with that index',
solution.load('4', self.list_of_names_file1)
)
def test_load(self):
list_of_orders = []
dict_orders = {'dayana': 10}
save(dict_orders, list_of_orders)
save(dict_orders, list_of_orders)
save(dict_orders, list_of_orders)
self.assertEqual(2, load('1', list_of_orders, True))
self.assertEqual(
'You have unsaved order.' + '\n' + 'If you wish to discard the current order, type load again',
load(10, list_of_orders, False)
)
diff = points - numpy.array([x, y])
dists = (diff[:, 0]**2 + diff[:, 1]**2)**0.5 #euclidean
ind = numpy.argsort(dists)
prob[yi, xi] = classifier(X[ind[0]])[1]
pylab.imshow(prob, extent=(minx, maxx, maxy, miny))
pylab.xlim(minx, maxx)
pylab.ylim(miny, maxy)
pylab.xlabel(at1)
pylab.ylabel(at2)
pylab.show()
X, y = solution.load('reg.data')
learner = solution.LogRegLearner(lambda_=0.)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.01)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.3)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
def test_load(self):
self.assertEqual('File 123 loaded',
solution.load('1', self.list_of_names_file1))
self.assertEqual('There is no list with that index',
solution.load('4', self.list_of_names_file1))
ind = np.argsort(dists)
prob[yi, xi] = classifier(X[ind[0]])[1]
pyplot.imshow(prob, extent=(minx, maxx, maxy, miny), cmap="seismic")
pyplot.xlim(minx, maxx)
pyplot.ylim(miny, maxy)
pyplot.xlabel(at1)
pyplot.ylabel(at2)
pyplot.savefig(file_name + ".png")
pyplot.show()
if __name__ == "__main__":
X, y = load('./data/reg.data')
# lambda_ = 0
# learner = LogRegLearner(lambda_=0.)
# classifier = learner(X, y)
# draw_decision(X, y, classifier, 0, 1, file_name="lambda0")
# learner = LogRegLearner(lambda_=10)
# classifier = learner(X, y)
# draw_decision(X, y, classifier, 0, 1, file_name="lambda1")
#
# learner = LogRegLearner(lambda_=0.01)
# classifier = learner(X, y)
# draw_decision(X, y, classifier, 0, 1, file_name="lambda3")
#
# learner = LogRegLearner(lambda_=0.0001)
for c, (x, y) in zip(y, points):
pylab.text(x, y, str(c), ha="center", va="center")
pylab.scatter([x], [y], c=["b", "r"][c != 0], s=200)
num = grid
prob = numpy.zeros([num, num])
for xi, x in enumerate(numpy.linspace(minx, maxx, num=num)):
for yi, y in enumerate(numpy.linspace(miny, maxy, num=num)):
#probability of the closest example
diff = points - numpy.array([x, y])
dists = (diff[:, 0]**2 + diff[:, 1]**2)**0.5 #euclidean
ind = numpy.argsort(dists)
prob[yi, xi] = classifier(X[ind[0]])[1]
pylab.imshow(prob, extent=(minx, maxx, maxy, miny))
pylab.xlim(minx, maxx)
pylab.ylim(miny, maxy)
pylab.xlabel(at1)
pylab.ylabel(at2)
pylab.show()
X, y = load('reg.data')
learner = LogRegLearner(lambda_=0.1)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
def test_load(self):
file_ = open("orders_0000_00_00_00_00_00", "w")
file_.write("tester - 42.00")
file_.close()
result = solution.load(1, self.order)
self.assertEqual({"tester": 42.00}, result)
def test_save_load(self):
orders = {'Tyrion': '3.45', 'Kvothe': '4.62'}
number_saves = 1
solution.save(orders, number_saves)
self.assertEqual({'Tyrion': '3.45', 'Kvothe': '4.62'}, solution.load("load 1"))
#
# X, y = s.load('reg.data')
#
# lambdas = [10, 0.03, 0.]
#
# for i in lambdas:
# learner = s.LogRegLearner(lambda_=i)
# classifier = learner(X, y)
#
#
# s.draw_decision(i, X, y, classifier, 0, 1)
# Part 3 test_cv:
X, y = s.load('reg.data')
lambdas = [10, 1, 0.5, 0.1, 0.075, 0.05, 0.03, 0.01, 0.001, 0.0001, 0.]
lambdas_ca = {l: 0 for l in lambdas}
for i in range(1,21):
for l in lambdas:
learner = s.LogRegLearner(lambda_=l)
res = s.test_cv(learner, X, y, seed=i)
ca = s.CA(y, res)
lambdas_ca[l] += ca
for l in lambdas_ca.keys():
test_cases = {
'a=ab;d=c8\nln=21\n222=ool': [{
'a': 'ab',
'd': 'c8'
}, {
'ln': '21'
}, {
'222': 'ool'
}],
'': {},
'lns=123\nsad-ew=213;nm=20312': [{
'lns': '123'
}, {
'sad-ew': '213',
'nm': '20312'
}],
'\n\nsds=12321\n\nsadsa=2132;b=c': [{}, {}, {
'sds': '12321'
}, {}, {
'sadsa': '2132',
'b': 'c'
}]
}
for case, ans in test_cases.items():
load_result = load(case)
store_result = store(ans)
assert load_result == ans, print(load_result, ans)
assert load(store_result) == ans, print(store_result, case)