On 2017-12-01 01:43, Robert wrote:
On Thursday, November 30, 2017 at 6:17:05 PM UTC-5, Robert wrote:
Hi,
I am new to Python. Now I follow a thread on mask array usage on line:
https://stackoverflow.com/questions/31563970/fitting-a-binomial-distribution-with-pymc-raises-zeroprobability-error-for-certa
I understand the problem, but I don't understand the answer follow the link.
Because the 'mask' array is composed of integer, if it is assigned a fraction
number as suggested a 1.5, it will be formatted to an integer 1.
observed_values = sp.random.binomial(n = 10.0, p = 0.1, size = 100)
...
mask = sp.zeros_like(observed_values)
Are you clear the answer's meaning?
"You can give it a non-integer value in order to avoid the problem that you cite.
For example, if you fill with, say, 1.5 that should work."
Thanks in advance
Excuse me for the top post. Now I find the more specific question from the below
link:
https://pymc-devs.github.io/pymc/tutorial.html
At almost the bottom part of the web page, when I run the script
"masked_values = masked_array(disasters_array, mask=disasters_array==-999)"
has an error:
... masked_values = masked_array(disasters_array, mask=disasters_array==-999)
Traceback (most recent call last):
File "<stdin>", line 7, in <module>
NameError: name 'masked_array' is not defined
I use Python 2.7 on Ubuntu 16.04, 64-bit. Is there an error in the pymc
tutorial web page?
Can you tell me what is wrong?
Thanks,
===========================
# Switchpoint
switch = DiscreteUniform('switch', lower=0, upper=110)
# Early mean
early_mean = Exponential('early_mean', beta=1)
# Late mean
late_mean = Exponential('late_mean', beta=1)
@deterministic(plot=False)
def rate(s=switch, e=early_mean, l=late_mean):
"""Allocate appropriate mean to time series"""
out = np.empty(len(disasters_array))
# Early mean prior to switchpoint
out[:s] = e
# Late mean following switchpoint
out[s:] = l
return out
# The inefficient way, using the Impute function:
# D = Impute('D', Poisson, disasters_array, mu=r)
#
# The efficient way, using masked arrays:
# Generate masked array. Where the mask is true,
# the value is taken as missing.
masked_values = masked_array(disasters_array, mask=disasters_array==-999)
# Pass masked array to data stochastic, and it does the right thing
disasters = Poisson('disasters', mu=rate, value=masked_values, observed=True)
Earlier in the tutorial there was:
>>> masked_values = np.ma.masked_equal(x, value=None)
>>> masked_values
masked_array(# etc
This shows that 'masked_values' is a 'masked_array' object.
A quick Google shows that it's numpy.ma.masked_array, i.e. defined in
numpy.ma.
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