def test_kpoints_write_explicit(kpoints_parser_explicit, tmpdir):
"""Test read, write and read KPOINTS in explicit mode.
"""
kpoints = kpoints_parser_explicit.get_dict()
temp_file = str(tmpdir.join('KPOINTSEXP'))
kpoints_parser_explicit.write(file_path=temp_file)
kpoints_parser_explicit_temp = Kpoints(file_path=temp_file)
kpoints_temp = kpoints_parser_explicit_temp.get_dict()
assert kpoints_temp['mode'] == 'explicit'
assert kpoints_temp['comment'] == 'Example file'
assert kpoints_temp['divisions'] == None
assert kpoints_temp['shifts'] == None
assert kpoints_temp['centering'] == None
points = kpoints_temp['points']
assert len(points) == 4
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.0, 0.0, 0.5]))
np.testing.assert_allclose(points[2][0], np.array([0.0, 0.5, 0.5]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.5, 0.5]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 2.0)
assert utils.isclose(points[3][1], 4.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
assert kpoints_temp['tetra'] == [[6, 1, 2, 3, 4]]
assert utils.isclose(kpoints_temp['tetra_volume'], 0.183333333333333)
def test_kpoints_params_explicit(kpoints_parser_explicit):
"""Check parameters in KPOINTS for explicit generation.
"""
kpoints = kpoints_parser_explicit.get_dict()
assert kpoints['mode'] == 'explicit'
assert kpoints['comment'] == 'Example file'
assert kpoints['divisions'] == None
assert kpoints['shifts'] == None
assert kpoints['centering'] == None
points = kpoints['points']
assert len(points) == 4
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.0, 0.0, 0.5]))
np.testing.assert_allclose(points[2][0], np.array([0.0, 0.5, 0.5]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.5, 0.5]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 2.0)
assert utils.isclose(points[3][1], 4.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
assert kpoints['tetra'] == [[6, 1, 2, 3, 4]]
assert utils.isclose(kpoints['tetra_volume'], 0.183333333333333)
def obtener_resultado(self, min, max=None, interval=None, velocidad=None):
if max == None:
max = min
min = 0
tiempo_aproximado = None
tiempo_real = None
for tiempo in utils.frange(min, max, interval):
self.velocidad_anterior.append(
self.obtener_velocidad_aproximada(tiempo))
self.velocidad_anterior_real.append(self.obtener_velocidad(tiempo))
self.tiempo_anterior.append(tiempo)
if utils.isclose(self.obtener_velocidad_aproximada(tiempo),
velocidad,
rel_tol=0.001) and tiempo_aproximado is None:
tiempo_aproximado = tiempo
if utils.isclose(self.obtener_velocidad(tiempo),
velocidad,
rel_tol=0.001) and tiempo_real is None:
tiempo_real = tiempo
if tiempo_real is not None and tiempo_aproximado is not None:
return [tiempo_aproximado, tiempo_real]
def ta_form(self, timestamp, price):
row = self.get_row_data_timestamp(timestamp)
slowk,slowd = row['slowk'], row['slowd']
if slowd > 80:
self.form = 'overbuy'
elif slowd < 20:
self.form = 'oversell'
else:
self.form = ''
df = self.get_data_timestamp(timestamp)
if df.index.size < 3:
return
dif = df['slowk'] - df['slowd']
if isclose(dif.iloc[-1], 0):
self.form = 'crossing' ### need forecast next form?
if dif.iloc[-1] > 0: #check if crossed up
if dif.iloc[-2] < 0 or isclose(dif.iloc[-2], 0):
self.form = 'crossup'
else:
self.form = 'up' ##continus up
elif dif.iloc[-1] < 0: #check if crossed down
if dif.iloc[-2] > 0 or isclose(dif.iloc[-2], 0):
self.form = 'crossdown'
else:
self.form = 'down' ##continus down
return self.form
def test_xml_energies(xml_parser):
"""Check energies.
"""
import numpy as np
# Only one ionic step
xml_data = xml_parser()
energy = xml_data.get_energies('initial')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_data.get_energies('final')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_data.get_energies('all')[0]
assert utils.isclose(energy, -43.312106219999997)
# Ionic steps
xml_data = xml_parser(filename='basicrelax.xml')
energy = xml_data.get_energies('initial')[0]
assert utils.isclose(energy, -42.91113348)
energy = xml_data.get_energies('final')[0]
assert utils.isclose(energy, -43.39087657)
energy = xml_data.get_energies('all')
test_array = np.array([
-42.91113348, -43.27757545, -43.36648855, -43.37734069, -43.38062479,
-43.38334165, -43.38753003, -43.38708193, -43.38641449, -43.38701639,
-43.38699488, -43.38773717, -43.38988315, -43.3898822, -43.39011239,
-43.39020751, -43.39034244, -43.39044584, -43.39087657
])
assert np.allclose(np.array(energy), test_array)
def ta_form(self, timestamp, price):
row = self.get_row_data_timestamp(timestamp)
slowk, slowd = row['slowk'], row['slowd']
if slowd > 80:
self.form = 'overbuy'
elif slowd < 20:
self.form = 'oversell'
else:
self.form = ''
df = self.get_data_timestamp(timestamp)
if df.index.size < 3:
return
dif = df['slowk'] - df['slowd']
if isclose(dif.iloc[-1], 0):
self.form = 'crossing' ### need forecast next form?
if dif.iloc[-1] > 0: #check if crossed up
if dif.iloc[-2] < 0 or isclose(dif.iloc[-2], 0):
self.form = 'crossup'
else:
self.form = 'up' ##continus up
elif dif.iloc[-1] < 0: #check if crossed down
if dif.iloc[-2] > 0 or isclose(dif.iloc[-2], 0):
self.form = 'crossdown'
else:
self.form = 'down' ##continus down
return self.form
def test_trig_invariance(angle: float, n: int):
"""Test that cos(θ), sin(θ) ≃ cos(θ + n*360°), sin(θ + n*360°)"""
r_cos, r_sin = Vector._trig(angle)
n_cos, n_sin = Vector._trig(angle + 360 * n)
note(f"δcos: {r_cos - n_cos}")
assert isclose(r_cos, n_cos, rel_to=[n / 1e9])
note(f"δsin: {r_sin - n_sin}")
assert isclose(r_sin, n_sin, rel_to=[n / 1e9])
def test_remarkable_angles(angle, trig):
"""Test that our table of remarkable angles agrees with Vector._trig.
This is useful both as a consistency test of the table,
and as a test of Vector._trig (which Vector.rotate uses).
"""
cos_t, sin_t = trig
cos_m, sin_m = Vector._trig(angle)
assert isclose(sin_t, sin_m, abs_tol=0, rel_tol=1e-14)
assert isclose(cos_t, cos_m, abs_tol=0, rel_tol=1e-14)
def test_xml_energies(xml_parser):
"""Check energies.
"""
energy = xml_parser.get_energies('initial')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_parser.get_energies('final')[0]
assert utils.isclose(energy, -43.312106219999997)
energies = xml_parser.get_energies('all')
assert utils.isclose(energies[0], -43.312106219999997)
assert utils.isclose(energies[1], -43.312106219999997)
def test_xml_energies_file_object(xml_parser):
"""Check energies using file object.
"""
xml_data = xml_parser()
energy = xml_data.get_energies('initial')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_data.get_energies('final')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_data.get_energies('all')[0]
assert utils.isclose(energy, -43.312106219999997)
def test_xml_energies(xml_parser):
"""Check energies for runs with no ionic steps.
"""
import numpy as np
energy = xml_parser.get_energies('initial')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_parser.get_energies('final')[0]
assert utils.isclose(energy, -43.312106219999997)
energy = xml_parser.get_energies('all')[0]
assert utils.isclose(energy, -43.312106219999997)
def test_xml_energies_ionic(xml_parser):
"""Check energies for runs with ionic steps.
"""
import numpy as np
energy = xml_parser.get_energies('initial')[0]
assert utils.isclose(energy, -42.91113348)
energy = xml_parser.get_energies('final')[0]
assert utils.isclose(energy, -43.39087657)
energy = xml_parser.get_energies('all')
test_array = np.array([-42.91113348, -43.27757545, -43.36648855, -43.37734069, -43.38062479, -43.38334165, -43.38753003, -43.38708193, -43.38641449, -43.38701639, -43.38699488, -43.38773717, -43.38988315, -43.3898822, -43.39011239, -43.39020751, -43.39034244, -43.39044584, -43.39087657])
assert np.allclose(np.array(energy),
test_array)
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""[Fig 18.8]
Return the optimal value of size having minimum error
on validation set.
err_train: A training error array, indexed by size
err_val: A validation error array, indexed by size
"""
err_val = []
err_train = []
size = 1
while True:
errT, errV = cross_validation(learner, size, dataset, k)
# Check for convergence provided err_val is not empty
if (err_train and isclose(err_train[-1], errT, rel_tol=1e-6)):
best_size = 0
min_val = math.inf
i = 0
while i < size:
if err_val[i] < min_val:
min_val = err_val[i]
best_size = i
i += 1
err_val.append(errV)
err_train.append(errT)
print(err_val)
size += 1
def update_upload_speeds(self, speeds, additive):
if len(self.uploads) != len(speeds):
raise Exception("Invalid speeds length, must match self.uploads")
factor = 1 if additive else 0
s = 0
for upload, speed in zip(self.uploads, speeds):
s += 1
skip = additive and utils.isclose(0, speed)
skip |= not additive and utils.isclose(speed, upload.speed)
if skip:
continue
new_speed = (upload.speed * factor) + speed
upload.interrupt((Connection.SPEED_MODIFIED, new_speed))
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""[Fig 18.8]
Return the optimal value of size having minimum error
on validation set.
err_train: A training error array, indexed by size
err_val: A validation error array, indexed by size
"""
err_val = []
err_train = []
size = 1
while True:
errT, errV = cross_validation(learner, size, dataset, k)
# Check for convergence provided err_val is not empty
if (err_train and isclose(err_train[-1], errT, rel_tol=1e-6)):
best_size = 0
min_val = math.inf
i = 0
while i<size:
if err_val[i] < min_val:
min_val = err_val[i]
best_size = i
i += 1
err_val.append(errV)
err_train.append(errT)
print(err_val)
size += 1
def test_text_models():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
# Test top
assert P1.top(5) == [(2081, 'the'), (1479, 'of'),
(1021, 'and'), (1008, 'to'),
(850, 'a')]
assert P2.top(5) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is'))]
assert P3.top(5) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as'))]
# Test isclose
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
# Test cond_prob.get
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
# Test dictionary
test_string = 'unigram'
wordseq = words(test_string)
P1 = UnigramWordModel(wordseq)
assert P1.dictionary == {('unigram'): 1}
test_string = 'bigram text'
wordseq = words(test_string)
P2 = NgramWordModel(2, wordseq)
assert P2.dictionary == {('bigram', 'text'): 1}
test_string = 'test trigram text here'
wordseq = words(test_string)
P3 = NgramWordModel(3, wordseq)
assert ('test', 'trigram', 'text') in P3.dictionary
assert ('trigram', 'text', 'here') in P3.dictionary
def test_text_models():
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wordseq)
P3 = NgramWordModel(3, wordseq)
# Test top
assert P1.top(5) == [(2081, 'the'), (1479, 'of'),
(1021, 'and'), (1008, 'to'),
(850, 'a')]
assert P2.top(5) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is'))]
assert P3.top(5) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as'))]
# Test isclose
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
# Test cond_prob.get
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
# Test dictionary
test_string = 'unigram'
wordseq = words(test_string)
P1 = UnigramWordModel(wordseq)
assert P1.dictionary == {('unigram'): 1}
test_string = 'bigram text'
wordseq = words(test_string)
P2 = NgramWordModel(2, wordseq)
assert P2.dictionary == {('bigram', 'text'): 1}
test_string = 'test trigram text here'
wordseq = words(test_string)
P3 = NgramWordModel(3, wordseq)
assert ('test', 'trigram', 'text') in P3.dictionary
assert ('trigram', 'text', 'here') in P3.dictionary
def validate_result(self, result):
# edge case
if str(self.res) == "-0":
self.res = 0
result = result.encode('ascii', 'replace')
result = result.replace('?', '-')
result = result.replace(',', '')
result = Decimal(result)
return utils.isclose(float(result), float(self.res))
def normalize(self):
"""Verifique se as probabilidades de todos os valores somam 1.
Retorna a distribuição normalizada.
Aumenta um ZeroDivisionError se a soma dos valores for 0."""
total = sum(self.prob.values())
if not isclose(total, 1.0):
for val in self.prob:
self.prob[val] /= total
return self
def normalize(self):
"""Make sure the probabilities of all values sum to 1.
Returns the normalized distribution.
Raises a ZeroDivisionError if the sum of the values is 0."""
total = sum(self.prob.values())
if not isclose(total, 1.0):
for val in self.prob:
self.prob[val] /= total
return self
def normalize(self):
"""Make sure the probabilities of all values sum to 1.
Returns the normalized distribution.
Raises a ZeroDivisionError if the sum of the values is 0."""
total = sum(self.prob.values())
if not isclose(total, 1.0):
for val in self.prob:
self.prob[val] /= total
return self
def ta_form(self, timestamp, price):
row = self.get_row_data_timestamp(timestamp)
up, mid, low = row['up'], row['mid'], row['low']
if isclose(up, low):
self.form = 'up_low_close'
elif isclose(up, price):
self.form = 'up'
elif isclose(low, price):
self.form = 'low'
elif isclose(mid, price):
self.form = 'mid'
elif price > up:
self.form = 'upper'
elif price < low:
self.form = 'lower'
else:
if price > mid:
self.form = 'midup'
elif price < mid:
self.form = 'midlow'
return self.form
def ta_form(self, timestamp, price):
row = self.get_row_data_timestamp(timestamp)
up,mid,low = row['up'],row['mid'],row['low']
if isclose(up, low):
self.form = 'up_low_close'
elif isclose(up, price):
self.form = 'up'
elif isclose(low, price):
self.form = 'low'
elif isclose(mid, price):
self.form = 'mid'
elif price > up:
self.form = 'upper'
elif price < low:
self.form = 'lower'
else:
if price > mid:
self.form = 'midup'
elif price < mid:
self.form = 'midlow'
return self.form
def test_kpoints_write_line(kpoints_parser_line, tmpdir):
"""Test read, write and read KPOINTS in line mode.
"""
kpoints = kpoints_parser_line.get_dict()
temp_file = str(tmpdir.join('KPOINTSLINE'))
kpoints_parser_line.write(file_path=temp_file)
kpoints_parser_line_temp = Kpoints(file_path=temp_file)
kpoints_temp = kpoints_parser_line_temp.get_dict()
assert kpoints_temp['mode'] == 'line'
assert kpoints_temp['comment'] == 'k-points along high symmetry lines'
assert kpoints_temp['divisions'] == None
assert kpoints_temp['shifts'] == None
assert kpoints_temp['centering'] == None
assert kpoints_temp['num_kpoints'] == 40
points = kpoints_temp['points']
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[2][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.75, 0.25]))
np.testing.assert_allclose(points[4][0], np.array([0.5, 0.75, 0.25]))
np.testing.assert_allclose(points[5][0], np.array([0.0, 0.0, 0.0]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 1.0)
assert utils.isclose(points[3][1], 1.0)
assert utils.isclose(points[4][1], 1.0)
assert utils.isclose(points[5][1], 1.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
assert points[4][2]
assert points[5][2]
def test_kpoints_params_line(kpoints_parser_line):
"""Check parameters in KPOINTS for line generation.
"""
kpoints = kpoints_parser_line.get_dict()
assert kpoints['mode'] == 'line'
assert kpoints['comment'] == 'k-points along high symmetry lines'
assert kpoints['divisions'] == None
assert kpoints['shifts'] == None
assert kpoints['centering'] == None
assert kpoints['num_kpoints'] == 40
points = kpoints['points']
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[2][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.75, 0.25]))
np.testing.assert_allclose(points[4][0], np.array([0.5, 0.75, 0.25]))
np.testing.assert_allclose(points[5][0], np.array([0.0, 0.0, 0.0]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 1.0)
assert utils.isclose(points[3][1], 1.0)
assert utils.isclose(points[4][1], 1.0)
assert utils.isclose(points[5][1], 1.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
assert points[4][2]
assert points[5][2]
def test_kpoints_modify_line(kpoints_parser_line, tmpdir):
"""Test read, modify, write and read KPOINTS in line mode.
"""
kpoints = kpoints_parser_line.get_dict()
assert kpoints['comment'] == 'k-points along high symmetry lines'
kpoints_parser_line.modify('comment', 'No comment')
point = Kpoint(np.array([0.5, 0.5, 0.25]), 1.0)
kpoints_parser_line.modify('points', point, point_number=3)
kpoints_parser_line.modify('points', point, point_number=4)
temp_file = str(tmpdir.join('KPOINTSLINE'))
kpoints_parser_line.write(file_path=temp_file)
kpoints_parser_line_temp = Kpoints(file_path=temp_file)
kpoints_temp = kpoints_parser_line_temp.get_dict()
assert kpoints_temp['comment'] == 'No comment'
points = kpoints_temp['points']
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[2][0], np.array([0.5, 0.5, 0.0]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.5, 0.25]))
np.testing.assert_allclose(points[4][0], np.array([0.5, 0.5, 0.25]))
np.testing.assert_allclose(points[5][0], np.array([0.0, 0.0, 0.0]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 1.0)
assert utils.isclose(points[3][1], 1.0)
assert utils.isclose(points[4][1], 1.0)
assert utils.isclose(points[5][1], 1.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
assert points[4][2]
assert points[5][2]
def test_text_models():
flatland = DataFile("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramTextModel(wordseq)
P2 = NgramTextModel(2, wordseq)
P3 = NgramTextModel(3, wordseq)
# The most frequent entries in each model
assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'),
(1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'),
(478, 'that'), (399, 'is'), (348, 'you')]
assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is')),
(71, ('by', 'the')), (68, ('for', 'the')),
(68, ('and', 'the')), (62, ('on', 'the')),
(60, ('to', 'be'))]
assert P3.top(10) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as')),
(12, ('of', 'the', 'circles')),
(12, ('of', 'sight', 'recognition')),
(11, ('the', 'number', 'of')),
(11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))]
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
def test_ngram_models():
flatland = DataFile("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramTextModel(wordseq)
P2 = NgramTextModel(2, wordseq)
P3 = NgramTextModel(3, wordseq)
# The most frequent entries in each model
assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'),
(1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'),
(478, 'that'), (399, 'is'), (348, 'you')]
assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')),
(152, ('in', 'the')), (86, ('of', 'a')),
(80, ('it', 'is')),
(71, ('by', 'the')), (68, ('for', 'the')),
(68, ('and', 'the')), (62, ('on', 'the')),
(60, ('to', 'be'))]
assert P3.top(10) == [(30, ('a', 'straight', 'line')),
(19, ('of', 'three', 'dimensions')),
(16, ('the', 'sense', 'of')),
(13, ('by', 'the', 'sense')),
(13, ('as', 'well', 'as')),
(12, ('of', 'the', 'circles')),
(12, ('of', 'sight', 'recognition')),
(11, ('the', 'number', 'of')),
(11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))]
assert isclose(P1['the'], 0.0611, rel_tol=0.001)
assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001)
assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001)
assert P2.cond_prob.get(('went',)) is None
assert P3.cond_prob['in', 'order'].dictionary == {'to': 6}
def test_dot_rotational_invariance(x: Vector, y: Vector, angle: float):
"""Test that rotating vectors doesn't change their dot product."""
t = x.angle(y)
cos_t, _ = Vector._trig(t)
note(f"θ: {t}")
note(f"cos θ: {cos_t}")
# Exclude near-orthogonal test inputs
assume(abs(cos_t) > 1e-6)
assert isclose(x * y,
x.rotate(angle) * y.rotate(angle),
rel_to=(x, y),
rel_exp=2)
def test_reflect_angle(initial: Vector, normal: Vector):
"""Test angle-related properties of Vector.reflect:
* initial.reflect(normal) * normal == - initial * normal
* normal.angle(initial) == 180 - normal.angle(reflected)
"""
# Exclude initial vectors that are very small or very close to the surface.
assume(not angle_isclose(initial.angle(normal) % 180, 90, epsilon=10))
assume(initial.length > 1e-10)
reflected = initial.reflect(normal)
assert isclose((initial * normal), -(reflected * normal))
assert angle_isclose(normal.angle(initial), 180 - normal.angle(reflected))
def test_dot_from_angle(x: Vector, y: Vector):
"""Test x · y == |x| · |y| · cos(θ)"""
t = x.angle(y)
cos_t, _ = Vector._trig(t)
# Dismiss near-othogonal test inputs
assume(abs(cos_t) > 1e-6)
min_len, max_len = sorted((x.length, y.length))
geometric = min_len * (max_len * cos_t)
note(f"θ: {t}")
note(f"cos θ: {cos_t}")
note(f"algebraic: {x * y}")
note(f"geometric: {geometric}")
assert isclose(x * y, geometric, rel_to=(x, y), rel_exp=2)
def test_kpoints_modify_explicit(kpoints_parser_explicit, tmpdir):
"""Test read, modify, write and read KPOINTS in explicit mode.
"""
kpoints = kpoints_parser_explicit.get_dict()
assert kpoints['comment'] == 'Example file'
points = kpoints['points']
assert len(points) == 4
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.0, 0.0, 0.5]))
np.testing.assert_allclose(points[2][0], np.array([0.0, 0.5, 0.5]))
np.testing.assert_allclose(points[3][0], np.array([0.5, 0.5, 0.5]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 2.0)
assert utils.isclose(points[3][1], 4.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
kpoints_parser_explicit.modify('comment', 'Nada comment')
point = Kpoint(np.array([0.0, 0.0, 0.0]), 1.0)
kpoints_parser_explicit.modify('points', point, point_number=3)
temp_file = str(tmpdir.join('KPOINTSEXP'))
kpoints_parser_explicit.write(file_path=temp_file)
kpoints_parser_explicit_temp = Kpoints(file_path=temp_file)
kpoints_temp = kpoints_parser_explicit_temp.get_dict()
assert kpoints_temp['comment'] == 'Nada comment'
points = kpoints_temp['points']
assert len(points) == 4
np.testing.assert_allclose(points[0][0], np.array([0.0, 0.0, 0.0]))
np.testing.assert_allclose(points[1][0], np.array([0.0, 0.0, 0.5]))
np.testing.assert_allclose(points[2][0], np.array([0.0, 0.5, 0.5]))
np.testing.assert_allclose(points[3][0], np.array([0.0, 0.0, 0.0]))
assert utils.isclose(points[0][1], 1.0)
assert utils.isclose(points[1][1], 1.0)
assert utils.isclose(points[2][1], 2.0)
assert utils.isclose(points[3][1], 1.0)
assert points[0][2]
assert points[1][2]
assert points[2][2]
assert points[3][2]
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""[Fig 18.8]
Return the optimal value of size having minimum error
on validation set.
err_train: A training error array, indexed by size
err_val: A validation error array, indexed by size
Size : [extracted from the book] In this section we explain how to select among models that are parameterized by size.
For example, with polynomials we have size = 1 for linear functions, size = 2 for quadratics,
and so on. For decision trees, the size could be the number of nodes in the tree. In all cases
we want to find the value of the size parameter that best balances underfitting and overfitting
to give the best test set accuracy.
"""
err_val = []
err_train = []
size = 1
"""
[deviates from pseudocode]
Intuition: The size, i.e complexity of a function would never be physically greater than the
number of total training data points. For example, if we have 3 data points, a polynomial of degree
greater than 3 is more than just overfitting. So, instead of looping till infinity, can just add the
length of the data set as a rough upper limit to it.
"""
while size < len(dataset):
errT, errV = cross_validation(learner, size, dataset, k)
# Check for convergence provided err_val is not empty
if (err_train and isclose(err_train[-1], errT, rel_tol=1e-6)):
best_size = 0
min_val = math.inf
i = 0
while i < size:
if err_val[i] < min_val:
min_val = err_val[i]
best_size = i
i += 1
err_val.append(errV)
err_train.append(errT)
print(err_val)
size += 1
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""
Fig 18.8
Return the optimal value of size having minimum error
on validataion set
err_train: a training error array, indexed by size
err_val: a validataion error array, indexed by size
"""
err_val = []
err_train = []
size = 1
while True:
errT, errV = cross_validation(learner, size, dataset, k)
# Check for convergence provided err_val is not empty
if (err_val and isclose(err_val[-1], errV, rel_tol=1e-6)):
best_size = size
return learner(dataset, best_size)
err_val.append(errV)
err_train.append(errT)
print(err_val)
size += 1
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""
Fig 18.8
Return the optimal value of size having minimum error
on validataion set
err_train: a training error array, indexed by size
err_val: a validataion error array, indexed by size
"""
err_val = []
err_train = []
size = 1
while True:
errT, errV = cross_validation(learner, size, dataset, k)
# Check for convergence provided err_val is not empty
if (err_val and isclose(err_val[-1], errV, rel_tol=1e-6)):
best_size = size
return learner(dataset, best_size)
err_val.append(errV)
err_train.append(errT)
print(err_val)
size += 1
def cross_validation_wrapper(learner, dataset, k=10, trials=1):
"""
[Figure 18.8]
Return the optimal value of size having minimum error on validation set.
errT: a training error array, indexed by size
errV: a validation error array, indexed by size
"""
errs = []
size = 1
while True:
errT, errV = cross_validation(learner, dataset, size, k, trials)
# check for convergence provided err_val is not empty
if errT and not isclose(errT[-1], errT, rel_tol=1e-6):
best_size = 0
min_val = math.inf
i = 0
while i < size:
if errs[i] < min_val:
min_val = errs[i]
best_size = i
i += 1
return learner(dataset, best_size)
errs.append(errV)
size += 1
def bandwidth_check_up(self): used_upload = math.fsum(c.speed for c in self.uploads) cond = used_upload <= self.up_mbps or utils.isclose(used_upload, self.up_mbps) assert cond, "Upload BW exceeded (host ID: %d, used: %f)" % (self.id, used_upload)
def test_normalize_length(v):
"""v.normalize().length == 1 and v == v.length * v.normalize()"""
assume(v)
assert isclose(v.normalize().length, 1)
assert v.isclose(v.length * v.normalize())
def avail_download_space(self): used_download = math.fsum(c.speed for c in self.downloads) free = self.down_mbps - used_download return 0 if utils.isclose(0, free) else free
def bandwidth_check_down(self): used_download = math.fsum(c.speed for c in self.downloads) cond = used_download <= self.down_mbps or utils.isclose(used_download, self.down_mbps) assert cond, "Download BW exceeded (host ID: %d, used: %f, original_mbps: %f)" % (self.id, used_download, self.down_mbps)
