Python truehist Examples

Example #1

File: gaussian.py Project: jhamrick/pystoch

        Test.__init__(self)

num_samples = 1000

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(np.array([samples1, samples2]), "Gaussian (mean=0, stddev=1)",
         numbins=20,
         labels=["RejectionQuery", "MetropolisHastings"],
         path="../../../graphs/gaussian.pdf")

Example #2

File: uniform.py Project: jhamrick/pystoch

        Test.__init__(self)

num_samples = 1000

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(np.array([samples1, samples2]), "Uniform (low=0, high=1)",
         numbins=10,
         labels=["RejectionQuery", "MetropolisHastings"],
         path="../../../graphs/uniform.pdf")

Example #3

File: exponential.py Project: jhamrick/pystoch

        Test.__init__(self)

num_samples = 1000

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(np.array([samples1, samples2]), "Exponential (lambda=0.5)",
         numbins=20,
         labels=["RejectionQuery", "MetropolisHastings"],
         path="../../../graphs/exponential.pdf")

Example #4

File: test4.py Project: jhamrick/pystoch

        ]

    def sample(self):
        return self.coin_weight

    def condition(self):
        return self.observed_data == self.sampled_data


num_samples = 1000

print "Running metropolis hastings..."
before = datetime.datetime.now()
query = Test()

prior_samples = [np.random.uniform(0, 1) for sample in xrange(num_samples)]
samples = query.run(num_samples, 100)

after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tTime:   %s seconds" % secs
print

truehist(np.array([prior_samples, samples]),
         "Beliefs about Coin Weight",
         numbins=20,
         labels=["prior to observing data", "conditioned on observed data"],
         path="../../../graphs/test4.pdf")

Example #5

File: gaussian.py Project: jhamrick/pystoch

num_samples = 1000

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(np.array([samples1, samples2]),
         "Gaussian (mean=0, stddev=1)",
         numbins=20,
         labels=["RejectionQuery", "MetropolisHastings"],
         path="../../../graphs/gaussian.pdf")

Example #6

File: gamma.py Project: jhamrick/pystoch

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10 ** 6) / 10.0 ** 6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10 ** 6) / 10.0 ** 6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(
    np.array([samples1, samples2]),
    "Gamma (k=2, theta=2)",
    numbins=20,
    labels=["RejectionQuery", "MetropolisHastings"],
    path="../../../graphs/gamma.pdf",
)

Example #7

File: gamma.py Project: jhamrick/pystoch

        Test.__init__(self)

num_samples = 1000

print "Running rejection query..."
before = datetime.datetime.now()
query1 = TestRejectionQuery()
samples1 = [query1.run() for x in xrange(num_samples)]
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples1)
print "\tTime:   %s seconds" % secs

print "Running metropolis hastings..."
before = datetime.datetime.now()
query2 = TestMetropolisHastings()
samples2 = query2.run(num_samples, 10)
after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tResult: %s" % np.mean(samples2)
print "\tTime:   %s seconds" % secs

truehist(np.array([samples1, samples2]), "Gamma (k=2, theta=2)",
         numbins=20,
         labels=["RejectionQuery", "MetropolisHastings"],
         path="../../../graphs/gamma.pdf")

Example #8

File: test4.py Project: jhamrick/pystoch

    def sample(self):
        return self.coin_weight

    def condition(self):
        return self.observed_data == self.sampled_data


num_samples = 1000

print "Running metropolis hastings..."
before = datetime.datetime.now()
query = Test()

prior_samples = [np.random.uniform(0, 1) for sample in xrange(num_samples)]
samples = query.run(num_samples, 100)

after = datetime.datetime.now()
td = after - before
secs = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6) / 10.0**6
secs = np.round(secs, decimals=2)
print "\tTime:   %s seconds" % secs
print

truehist(np.array([prior_samples, samples]), "Beliefs about Coin Weight",
         numbins=20,
         labels=["prior to observing data",
                 "conditioned on observed data"],
         path="../../../graphs/test4.pdf")