def testMultiplication(self):
standard_normal = Gaussian(0, 1)
shifted_gaussian = Gaussian(2, 3)
product = standard_normal * shifted_gaussian
self.assertAlmostEqual(
0.2, product.mean, None,
"testMultiplication mean expected %.15f, got %.15f" %
(0.2, product.mean), GaussianDistributionTest.ERROR_TOLERANCE)
self.assertAlmostEqual(
3.0 / sqrt(10), product.stdev, None,
"testMultiplication stdev expected %.15f, got %.15f" %
(0.2, product.mean), GaussianDistributionTest.ERROR_TOLERANCE)
m4s5 = Gaussian(4, 5)
m6s7 = Gaussian(6, 7)
product2 = m4s5 * m6s7
expectedMean = (4.0 * 7.0**2 + 6.0 * 5.0**2) / (5.0**2 + 7.0**2)
self.assertAlmostEqual(
expectedMean, product2.mean, None,
"testMultiplication mean2 expected %.15f, got %.15f" %
(expectedMean, product2.mean),
GaussianDistributionTest.ERROR_TOLERANCE)
expectedSigma = sqrt((5.0**2 * 7.0**2) / (5.0**2 + 7.0**2))
self.assertAlmostEqual(
expectedSigma, product2.stdev, None,
"testMultiplication stdev2 expected %.15f, got %.15f" %
(expectedSigma, product2.stdev),
GaussianDistributionTest.ERROR_TOLERANCE)
def __init__(self, mean, variance, variable):
GaussianFactor.__init__(self, "Prior value going to %s" % variable)
self.new_message = Gaussian(mean, sqrt(variance))
new_message = Message(Gaussian.from_precision_mean(0, 0),
"message from %s to %s" % (self, variable))
self.create_variable_to_message_binding_with_message(
variable, new_message)
def testDivision(self):
product = Gaussian(0.2, 3.0 / sqrt(10))
standard_normal = Gaussian(0.0, 1.0)
product_divided_by_standard_normal = product / standard_normal
self.assertAlmostEqual(
2.0, product_divided_by_standard_normal.mean, None,
"testDivision mean expected %.15f, got %.15f" %
(2.0, product_divided_by_standard_normal.mean),
GaussianDistributionTest.ERROR_TOLERANCE)
self.assertAlmostEqual(
3.0, product_divided_by_standard_normal.stdev, None,
"testDivision stdev expected %.15f, got %.15f" %
(3.0, product_divided_by_standard_normal.stdev),
GaussianDistributionTest.ERROR_TOLERANCE)
product2 = Gaussian((4.0 * 7.0**2 + 6.0 * 5.0**2) / (5.0**2 + 7.0**2),
sqrt((5.0**2 * 7.0**2) / (5.0**2 + 7.0**2)))
m4s5 = Gaussian(4.0, 5.0)
product2divided_by_m4s5 = product2 / m4s5
self.assertAlmostEqual(
6.0, product2divided_by_m4s5.mean, None,
"testDivision mean2 expected %.15f, got %.15f" %
(6.0, product2divided_by_m4s5.mean),
GaussianDistributionTest.ERROR_TOLERANCE)
self.assertAlmostEqual(
7.0, product2divided_by_m4s5.stdev, None,
"testDivision stdev2 expected %.15f, got %.15f" %
(7.0, product2divided_by_m4s5.stdev),
GaussianDistributionTest.ERROR_TOLERANCE)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
return (-Gaussian.log_product_normalization(message_from_variable, message) +
log(Gaussian.cumulative_to((message_from_variable.mean - self.epsilon) / message_from_variable.stdev)))
def testLogRatioNormalization(self):
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lrn = Gaussian.log_ratio_normalization(m1s2, m3s4)
answer = 2.6157405972171204
self.assertAlmostEqual(
answer, lrn, None,
"testLogRatioNormalization lrn expected %.15f, got %.15f" %
(answer, lrn), GaussianDistributionTest.ERROR_TOLERANCE)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
mean = message_from_variable.mean
std = message_from_variable.stdev
z = (Gaussian.cumulative_to((self.epsilon - mean) / std) -
Gaussian.cumulative_to((-self.epsilon - mean) / std))
return -Gaussian.log_product_normalization(message_from_variable, message) + log(z)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
return (
-Gaussian.log_product_normalization(message_from_variable, message)
+ log(
Gaussian.cumulative_to(
(message_from_variable.mean - self.epsilon) /
message_from_variable.stdev)))
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
mean = message_from_variable.mean
std = message_from_variable.stdev
z = (Gaussian.cumulative_to(
(self.epsilon - mean) / std) - Gaussian.cumulative_to(
(-self.epsilon - mean) / std))
return -Gaussian.log_product_normalization(message_from_variable,
message) + log(z)
def partial_update(self, prior, full_posterior, update_percentage):
prior_gaussian = Gaussian(prior.mean, prior.stdev)
posterior_gaussian = Gaussian(full_posterior.mean, full_posterior.stdev)
partial_precision_diff = update_percentage * (posterior_gaussian.precision - prior_gaussian.precision)
partial_precision_mean_diff = update_percentage * (posterior_gaussian.precision_mean - prior_gaussian.precision_mean)
partial_posterior_gaussian = Gaussian.from_precision_mean(
prior_gaussian.precision_mean + partial_precision_mean_diff,
prior_gaussian.precision + partial_precision_diff)
return Rating(partial_posterior_gaussian.mean,
partial_posterior_gaussian.stdev)
def testLogProductNormalization(self):
standard_normal = Gaussian(0, 1)
lpn = Gaussian.log_product_normalization(standard_normal, standard_normal)
answer = -1.2655121234846454
self.assertAlmostEqual(answer, lpn, None,
"testLogProductNormalization lpn expected %.15f, got %.15f" % (answer, lpn),
GaussianDistributionTest.ERROR_TOLERANCE)
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lpn2 = Gaussian.log_product_normalization(m1s2, m3s4)
answer = -2.5168046699816684
self.assertAlmostEqual(answer, lpn2, None,
"testLogProductNormalization lpn2 expected %.15f, got %.15f" % (answer, lpn2),
GaussianDistributionTest.ERROR_TOLERANCE)
def testCumulativeTo(self):
expected = 0.691462461274013
answer = Gaussian.cumulative_to(0.5)
self.assertAlmostEqual(
expected, answer, None,
"testCumulativeTo expected %.15f, got %.15f" % (expected, answer),
GaussianDistributionTest.ERROR_TOLERANCE)
def update_message_variable(self, message, variable):
old_marginal = copy(variable.value)
old_message = copy(message.value)
message_from_var = old_marginal / old_message
c = message_from_var.precision
d = message_from_var.precision_mean
sqrt_c = sqrt(c)
d_on_sqrt_c = d / sqrt_c
epsilon_times_sqrt_c = self.epsilon * sqrt_c
d = message_from_var.precision_mean
denom = 1.0 - w_exceeds_margin(d_on_sqrt_c, epsilon_times_sqrt_c)
new_precision = c / denom
new_precision_mean = (d + sqrt_c * v_exceeds_margin(
d_on_sqrt_c, epsilon_times_sqrt_c)) / denom
new_marginal = Gaussian.from_precision_mean(new_precision_mean,
new_precision)
new_message = (old_message * new_marginal) / old_marginal
message.value = new_message
variable.value = new_marginal
return new_marginal - old_marginal
def create_variable_to_message_binding(self, variable):
new_distribution = Gaussian.from_precision_mean(0.0, 0.0)
binding = Factor.create_variable_to_message_binding_with_message(
self, variable,
Message(new_distribution,
"message from %s to %s" % (self, variable)))
return binding
def log_normalization(self):
result = 0.0
for i in range(1, len(self.variables)):
result += Gaussian.log_ratio_normalization(self.variables[i].value,
self.messages[i].value)
return result
def testAt(self):
expected = 0.352065326764300
answer = Gaussian.at(0.5)
self.assertAlmostEqual(
expected, answer, None,
"testAt expected %.15f, got %.15f" % (expected, answer),
GaussianDistributionTest.ERROR_TOLERANCE)
def update_message_variable(self, message, variable):
old_marginal = copy(variable.value)
old_message = copy(message.value)
message_from_var = old_marginal / old_message
c = message_from_var.precision
d = message_from_var.precision_mean
sqrt_c = sqrt(c)
d_on_sqrt_c = d / sqrt_c
epsilon_times_sqrt_c = self.epsilon * sqrt_c
d = message_from_var.precision_mean
denom = 1.0 - w_exceeds_margin(d_on_sqrt_c, epsilon_times_sqrt_c)
new_precision = c / denom
new_precision_mean = (d + sqrt_c * v_exceeds_margin(d_on_sqrt_c, epsilon_times_sqrt_c)) / denom
new_marginal = Gaussian.from_precision_mean(new_precision_mean, new_precision)
new_message = (old_message * new_marginal) / old_marginal
message.value = new_message
variable.value = new_marginal
return new_marginal - old_marginal
def testSubtraction(self):
standard_normal = Gaussian(0.0, 1.0)
abs_diff = standard_normal - standard_normal
self.assertAlmostEqual(
0.0, abs_diff, None,
"testAbsoluteDifference abs_diff expected %.15f, got %.15f" %
(0.0, abs_diff), GaussianDistributionTest.ERROR_TOLERANCE)
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
abs_diff2 = m1s2 - m3s4
answer = 0.4330127018922193
self.assertAlmostEqual(
0.0, abs_diff, None,
"testAbsoluteDifference abs_diff2 expected %.15f, got %.15f" %
(answer, abs_diff2), GaussianDistributionTest.ERROR_TOLERANCE)
def w_within_margin(team_performance_difference, draw_margin):
team_performance_difference_abs = abs(team_performance_difference)
denominator = (Gaussian.cumulative_to(draw_margin - team_performance_difference_abs) -
Gaussian.cumulative_to(-draw_margin - team_performance_difference_abs))
if denominator < 2.222758749e-162:
return 1.0
vt = v_within_margin(team_performance_difference_abs, draw_margin)
return (vt ** 2 +
(
(draw_margin - team_performance_difference_abs) *
Gaussian.at(draw_margin - team_performance_difference_abs) -
(-draw_margin - team_performance_difference_abs) *
Gaussian.at(-draw_margin - team_performance_difference_abs)) / denominator)
def create_variable_to_message_binding(self, variable):
new_distribution = Gaussian.from_precision_mean(0.0, 0.0)
binding = Factor.create_variable_to_message_binding_with_message(
self,
variable,
Message(new_distribution, "message from {} to {}".format(self, variable))
)
return binding
def v_within_margin(team_performance_difference, draw_margin):
team_performance_difference_abs = abs(team_performance_difference)
denominator = (
Gaussian.cumulative_to(draw_margin - team_performance_difference_abs) -
Gaussian.cumulative_to(-draw_margin - team_performance_difference_abs))
if denominator < 2.222758749e-162:
if team_performance_difference < 0.0:
return -team_performance_difference - draw_margin
return -team_performance_difference + draw_margin
numerator = (Gaussian.at(-draw_margin - team_performance_difference_abs) -
Gaussian.at(draw_margin - team_performance_difference_abs))
if team_performance_difference < 0.0:
return -numerator / denominator
return numerator / denominator
def w_within_margin(team_performance_difference, draw_margin):
team_performance_difference_abs = abs(team_performance_difference)
denominator = (
Gaussian.cumulative_to(draw_margin - team_performance_difference_abs) -
Gaussian.cumulative_to(-draw_margin - team_performance_difference_abs))
if denominator < 2.222758749e-162:
return 1.0
vt = v_within_margin(team_performance_difference_abs, draw_margin)
return (vt**2 +
((draw_margin - team_performance_difference_abs) *
Gaussian.at(draw_margin - team_performance_difference_abs) -
(-draw_margin - team_performance_difference_abs) *
Gaussian.at(-draw_margin - team_performance_difference_abs)) /
denominator)
def v_within_margin(team_performance_difference, draw_margin):
team_performance_difference_abs = abs(team_performance_difference)
denominator = (
Gaussian.cumulative_to(draw_margin - team_performance_difference_abs) -
Gaussian.cumulative_to(-draw_margin - team_performance_difference_abs))
if denominator < 2.222758749e-162:
if team_performance_difference < 0.0:
return -team_performance_difference - draw_margin
return -team_performance_difference + draw_margin
numerator = (Gaussian.at(-draw_margin - team_performance_difference_abs) -
Gaussian.at(draw_margin - team_performance_difference_abs))
if team_performance_difference < 0.0:
return -numerator / denominator
return numerator / denominator
def testLogRatioNormalization(self):
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lrn = Gaussian.log_ratio_normalization(m1s2, m3s4)
answer = 2.6157405972171204
self.assertAlmostEqual(answer, lrn, None,
"testLogRatioNormalization lrn expected %.15f, got %.15f" % (answer, lrn),
GaussianDistributionTest.ERROR_TOLERANCE)
def w_exceeds_margin(team_performance_difference, draw_margin):
denominator = Gaussian.cumulative_to(team_performance_difference - draw_margin)
if denominator < 2.222758749e-162:
if team_performance_difference < 0.0:
return 1.0
return 0.0
v_win = v_exceeds_margin(team_performance_difference, draw_margin)
return v_win * (v_win + team_performance_difference - draw_margin)
def testLogProductNormalization(self):
standard_normal = Gaussian(0, 1)
lpn = Gaussian.log_product_normalization(standard_normal,
standard_normal)
answer = -1.2655121234846454
self.assertAlmostEqual(
answer, lpn, None,
"testLogProductNormalization lpn expected %.15f, got %.15f" %
(answer, lpn), GaussianDistributionTest.ERROR_TOLERANCE)
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lpn2 = Gaussian.log_product_normalization(m1s2, m3s4)
answer = -2.5168046699816684
self.assertAlmostEqual(
answer, lpn2, None,
"testLogProductNormalization lpn2 expected %.15f, got %.15f" %
(answer, lpn2), GaussianDistributionTest.ERROR_TOLERANCE)
def w_exceeds_margin(team_performance_difference, draw_margin):
denominator = Gaussian.cumulative_to(team_performance_difference -
draw_margin)
if denominator < 2.222758749e-162:
if team_performance_difference < 0.0:
return 1.0
return 0.0
v_win = v_exceeds_margin(team_performance_difference, draw_margin)
return v_win * (v_win + team_performance_difference - draw_margin)
def update_message_variable(self, message, variable):
old_marginal = copy(variable.value)
old_message = message
new_marginal = Gaussian.from_precision_mean(
old_marginal.precision_mean + self.new_message.precision_mean - old_message.value.precision_mean,
old_marginal.precision + self.new_message.precision - old_message.value.precision)
variable.value = new_marginal
new_message = self.new_message
message.value = new_message
return old_marginal - new_marginal
def update_message_variable(self, message, variable):
old_marginal = copy(variable.value)
old_message = message
new_marginal = Gaussian.from_precision_mean(
old_marginal.precision_mean + self.new_message.precision_mean -
old_message.value.precision_mean, old_marginal.precision +
self.new_message.precision - old_message.value.precision)
variable.value = new_marginal
new_message = self.new_message
message.value = new_message
return old_marginal - new_marginal
def partial_update(self, prior, full_posterior, update_percentage):
prior_gaussian = Gaussian(prior.mean, prior.stdev)
posterior_gaussian = Gaussian(full_posterior.mean, full_posterior.stdev)
partial_precision_diff = update_percentage * (posterior_gaussian.precision - prior_gaussian.precision)
partial_precision_mean_diff = update_percentage * (posterior_gaussian.precision_mean - prior_gaussian.precision_mean)
partial_posterior_gaussian = Gaussian.from_precision_mean(
prior_gaussian.precision_mean + partial_precision_mean_diff,
prior_gaussian.precision + partial_precision_diff)
return Rating(partial_posterior_gaussian.mean,
partial_posterior_gaussian.stdev)
def __init__(self, teams, team_ranks, game_info):
game_info = TrueSkillGameInfo.ensure_game_info(game_info)
FactorGraph.__init__(self)
self.prior_layer = PlayerPriorValuesToSkillsLayer(self, teams)
self.game_info = game_info
new_factory = VariableFactory(lambda: Gaussian.from_precision_mean(0.0, 0.0))
self.variable_factory = new_factory
self.layers = [
self.prior_layer,
PlayerSkillsToPerformancesLayer(self),
PlayerPerformancesToTeamPerformancesLayer(self),
IteratedTeamDifferencesInnerLayer(self,
TeamPerformancesToTeamPerformanceDifferencesLayer(self),
TeamDifferencesComparisonLayer(self, team_ranks))
]
def __init__(self, teams, team_ranks, game_info):
game_info = TrueSkillGameInfo.ensure_game_info(game_info)
FactorGraph.__init__(self)
self.prior_layer = PlayerPriorValuesToSkillsLayer(self, teams)
self.game_info = game_info
new_factory = VariableFactory(
lambda: Gaussian.from_precision_mean(0.0, 0.0))
self.variable_factory = new_factory
self.layers = [
self.prior_layer,
PlayerSkillsToPerformancesLayer(self),
PlayerPerformancesToTeamPerformancesLayer(self),
IteratedTeamDifferencesInnerLayer(
self, TeamPerformancesToTeamPerformanceDifferencesLayer(self),
TeamDifferencesComparisonLayer(self, team_ranks))
]
def __init__(self, initial_mean=DEFAULT_INITIAL_MEAN,
stdev=DEFAULT_INITIAL_STANDARD_DEVIATION,
beta=DEFAULT_BETA,
dynamics_factor=DEFAULT_DYNAMICS_FACTOR,
draw_probability=DEFAULT_DRAW_PROBABILITY,
conservative_stdev_multiplier=DEFAULT_CONSERVATIVE_STANDARD_DEVIATION_MULTIPLIER):
try:
self.initial_mean = float(initial_mean)
self.initial_stdev = float(stdev)
self.beta = float(beta)
self.dynamics_factor = float(dynamics_factor)
self.draw_probability = float(draw_probability)
self.conservative_stdev_multiplier = float(conservative_stdev_multiplier)
self.draw_margin = float(Gaussian.inverse_cumulative_to(0.5 * (self.draw_probability + 1), 0, 1) *
sqrt(1 + 1) * self.beta)
except ValueError:
raise ValueError("TrueSkillGameInfo arguments must be numeric")
def update_helper(self, weights, weights_squared, messages, variables):
message0 = copy(messages[0].value)
marginal0 = copy(variables[0].value)
inverse_of_new_precision_sum = 0.0
another_inverse_of_new_precision_sum = 0.0
weighted_mean_sum = 0.0
another_weighted_mean_sum = 0.0
for i in range(len(weights_squared)):
inverse_of_new_precision_sum += weights_squared[i] / (
variables[i + 1].value.precision -
messages[i + 1].value.precision)
diff = variables[i + 1].value / messages[i + 1].value
another_inverse_of_new_precision_sum += weights_squared[
i] / diff.precision
weighted_mean_sum += (weights[i] *
(variables[i + 1].value.precision_mean -
messages[i + 1].value.precision_mean) /
(variables[i + 1].value.precision -
messages[i + 1].value.precision))
another_weighted_mean_sum += weights[
i] * diff.precision_mean / diff.precision
new_precision = 1.0 / inverse_of_new_precision_sum
#another_new_precision = 1.0 / another_inverse_of_new_precision_sum
new_precision_mean = new_precision * weighted_mean_sum
#another_new_precision_mean = another_new_precision * another_weighted_mean_sum
new_message = Gaussian.from_precision_mean(new_precision_mean,
new_precision)
old_marginal_without_message = marginal0 / message0
new_marginal = old_marginal_without_message * new_message
messages[0].value = new_message
variables[0].value = new_marginal
final_diff = new_marginal - marginal0
return final_diff
def update_helper(self, message1, message2, variable1, variable2):
message1_value = copy(message1.value)
message2_value = copy(message2.value)
marginal1 = copy(variable1.value)
marginal2 = copy(variable2.value)
a = self.precision / (self.precision + marginal2.precision - message2_value.precision)
new_message = Gaussian.from_precision_mean(
a * (marginal2.precision_mean - message2_value.precision_mean),
a * (marginal2.precision - message2_value.precision))
old_marginal_without_message = marginal1 / message1_value
new_marginal = old_marginal_without_message * new_message
message1.value = new_message
variable1.value = new_marginal
return new_marginal - marginal1
def update_helper(self, message1, message2, variable1, variable2):
message1_value = copy(message1.value)
message2_value = copy(message2.value)
marginal1 = copy(variable1.value)
marginal2 = copy(variable2.value)
a = self.precision / (self.precision + marginal2.precision -
message2_value.precision)
new_message = Gaussian.from_precision_mean(
a * (marginal2.precision_mean - message2_value.precision_mean),
a * (marginal2.precision - message2_value.precision))
old_marginal_without_message = marginal1 / message1_value
new_marginal = old_marginal_without_message * new_message
message1.value = new_message
variable1.value = new_marginal
return new_marginal - marginal1
def __init__(
self,
initial_mean=DEFAULT_INITIAL_MEAN,
stdev=DEFAULT_INITIAL_STANDARD_DEVIATION,
beta=DEFAULT_BETA,
dynamics_factor=DEFAULT_DYNAMICS_FACTOR,
draw_probability=DEFAULT_DRAW_PROBABILITY,
conservative_stdev_multiplier=DEFAULT_CONSERVATIVE_STANDARD_DEVIATION_MULTIPLIER
):
try:
self.initial_mean = float(initial_mean)
self.initial_stdev = float(stdev)
self.beta = float(beta)
self.dynamics_factor = float(dynamics_factor)
self.draw_probability = float(draw_probability)
self.conservative_stdev_multiplier = float(
conservative_stdev_multiplier)
self.draw_margin = float(
Gaussian.inverse_cumulative_to(
0.5 * (self.draw_probability + 1), 0, 1) * sqrt(1 + 1) *
self.beta)
except ValueError:
raise ValueError("TrueSkillGameInfo arguments must be numeric")
def update_helper(self, weights, weights_squared, messages, variables):
message0 = copy(messages[0].value)
marginal0 = copy(variables[0].value)
inverse_of_new_precision_sum = 0.0
another_inverse_of_new_precision_sum = 0.0
weighted_mean_sum = 0.0
another_weighted_mean_sum = 0.0
for i in range(len(weights_squared)):
inverse_of_new_precision_sum += weights_squared[i] / (variables[i + 1].value.precision - messages[i + 1].value.precision)
diff = variables[i + 1].value / messages[i + 1].value
another_inverse_of_new_precision_sum += weights_squared[i] / diff.precision
weighted_mean_sum += (weights[i] *
(variables[i + 1].value.precision_mean - messages[i + 1].value.precision_mean) /
(variables[i + 1].value.precision - messages[i + 1].value.precision))
another_weighted_mean_sum += weights[i] * diff.precision_mean / diff.precision
new_precision = 1.0 / inverse_of_new_precision_sum
# another_new_precision = 1.0 / another_inverse_of_new_precision_sum
new_precision_mean = new_precision * weighted_mean_sum
# another_new_precision_mean = another_new_precision * another_weighted_mean_sum
new_message = Gaussian.from_precision_mean(new_precision_mean, new_precision)
old_marginal_without_message = marginal0 / message0
new_marginal = old_marginal_without_message * new_message
messages[0].value = new_message
variables[0].value = new_marginal
final_diff = new_marginal - marginal0
return final_diff
def v_exceeds_margin(team_performance_difference, draw_margin):
denominator = Gaussian.cumulative_to(team_performance_difference - draw_margin)
if (denominator < 2.22275874e-162):
return -team_performance_difference + draw_margin
return Gaussian.at(team_performance_difference - draw_margin) / denominator
def send_message_variable(self, message, variable):
marginal = variable.value
message_value = message.value
log_z = Gaussian.log_product_normalization(marginal, message_value)
variable.value = marginal * message_value
return log_z
def testCumulativeTo(self):
expected = 0.691462461274013
answer = Gaussian.cumulative_to(0.5)
self.assertAlmostEqual(expected, answer, None,
"testCumulativeTo expected %.15f, got %.15f" % (expected, answer),
GaussianDistributionTest.ERROR_TOLERANCE)
def send_message_variable(self, message, variable):
marginal = variable.value
message_value = message.value
log_z = Gaussian.log_product_normalization(marginal, message_value)
variable.value = marginal * message_value
return log_z
def log_normalization(self):
return Gaussian.log_ratio_normalization(self.variables[0].value,
self.messages[0].value)
def testAt(self):
expected = 0.352065326764300
answer = Gaussian.at(0.5)
self.assertAlmostEqual(expected, answer, None,
"testAt expected %.15f, got %.15f" % (expected, answer),
GaussianDistributionTest.ERROR_TOLERANCE)
def __init__(self, mean, variance, variable):
GaussianFactor.__init__(self, "Prior value going to %s" % variable)
self.new_message = Gaussian(mean, sqrt(variance))
new_message = Message(Gaussian.from_precision_mean(0, 0),
"message from %s to %s" % (self, variable))
self.create_variable_to_message_binding_with_message(variable, new_message)
def log_normalization(self):
return Gaussian.log_ratio_normalization(
self.variables[0].value,
self.messages[0].value
)
def v_exceeds_margin(team_performance_difference, draw_margin):
denominator = Gaussian.cumulative_to(team_performance_difference -
draw_margin)
if (denominator < 2.22275874e-162):
return -team_performance_difference + draw_margin
return Gaussian.at(team_performance_difference - draw_margin) / denominator
def log_normalization(self):
result = 0.0
for i in range(1, len(self.variables)):
result += Gaussian.log_ratio_normalization(self.variables[i].value, self.messages[i].value)
return result