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Boris Glavic 2020-07-01 18:53:45 -05:00
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@ -49,7 +49,7 @@ $> initdb
<h3><a name="installing_pip_plugin">Installing the PIP Library</a></h3>
<ul>
<li>Compile PIP.
<li>Compile PIP.
<pre>
$> cd pip_plugin
$> make
@ -78,12 +78,12 @@ $> psql template_1 -f install[.ctype].sql
<p>For example:
<pre>
CREATE TABLE my_probabilistic_data(
name varchar(100),
name varchar(100),
data pip_eqn
);
INSERT INTO my_probabilistic_data
SELECT input.name,
CREATE_VARIABLE("Normal", vector(input.mean, input.stddev))
SELECT input.name,
CREATE_VARIABLE("Normal", ROW(input.mean, input.stddev))
FROM input;
</pre>
This example creates a random variable by iterating over each row of the table 'input'. The Normal distribution requires two parameters: a mean and a standard deviation, both of which are drawn from the corresponding row of the input table.<p>
@ -194,23 +194,23 @@ FROM results;</pre>
<h2><a name="reference_dist_list">Reference: Predefined Distributions</a></h2>
<dt>Zero</dt>
<dd>A distribution that is always zero (i.e., the <a href="http://en.wikipedia.org/wiki/Dirac_delta">Dirac Delta</a> as a distribution).
<pre>CREATE_VARIABLE("Zero", vector())</pre></dd>
<pre>CREATE_VARIABLE("Zero", ROW())</pre></dd>
<dt>Exponential</dt>
<dd><a href="http://en.wikipedia.org/wiki/Exponential_distribution">The Exponential Distribution</a>. The Exponential Distribution takes one parameter, lambda: the <b>inverse</b> of the expectation of the variable being created.
<pre>CREATE_VARIABLE("Exponential", vector(lambda))</pre></dd>
<pre>CREATE_VARIABLE("Exponential", ROW(lambda))</pre></dd>
<dt>Normal</dt>
<dd><a href="http://en.wikipedia.org/wiki/Normal_distribution">The Normal Distribution</a>. The Normal Distribution takes two parameters, the mean, and the standard deviation of the variable being created.
<pre>CREATE_VARIABLE("Normal", vector(mean, stddev))</pre></dd>
<pre>CREATE_VARIABLE("Normal", ROW(mean, stddev))</pre></dd>
<dt>Poisson</dt>
<dd><a href="http://en.wikipedia.org/wiki/Poisson_distribution">The Poisson Distribution</a>. The Poisson distribution takes one parameter, lambda: the expectation of the variable being created.
<pre>CREATE_VARIABLE("Poisson", vector(lambda))</pre></dd>
<pre>CREATE_VARIABLE("Poisson", ROW(lambda))</pre></dd>
<dt>Uniform</dt>
<dd><a href="http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)">The Uniform Distribution</a>. The uniform distribution takes two parameters, the endpoints of the distribution; The endpoints may be provided in any order.
<pre>CREATE_VARIABLE("Uniform", vector(low, high))</pre></dd>
<pre>CREATE_VARIABLE("Uniform", ROW(low, high))</pre></dd>
</dl>
<hr />
@ -230,7 +230,7 @@ The components of a PIP distribution are as follows:
<dt><code>init_fn</code></dt>
<dd>A pointer to a constructor function for your distribution with the following schema:<pre>
void init_fn(pip_var *var, HeapTupleHeader params)</pre>
When your initializer is invoked, <code>var</code> will contain a pointer to initialized pip variable. <code>var->group_state</code> will contain a pointer to an allocated, but uninitialized block of [paramsize] bytes. <code>params</code> is a pointer to the postgres vector() of parameters. See below for information on utility functions for parsing the parameter vector.</dd>
When your initializer is invoked, <code>var</code> will contain a pointer to initialized pip variable. <code>var->group_state</code> will contain a pointer to an allocated, but uninitialized block of [paramsize] bytes. <code>params</code> is a pointer to the postgres ROW() of parameters. See below for information on utility functions for parsing the parameter vector.</dd>
<dt><code>gen_fn</code></dt>
<dd>A pointer to a generator function for your distribution with the following schema: <pre>
@ -262,7 +262,7 @@ When invoked, <code>str</code> will contain a pointer to a (large) allocated, bu
<dt><code>input_fn</code></dt>
<dd>NULL, or a pointer to a function for parsing a human-readable representation of the distribution's parameters, with the following schema:<pre>
int input_fn(pip_var *var, char *str)</pre>
When the input function is invoked, <code>var</code> will contain a pointer to initialized pip variable. <code>var->group_state</code> will contain a pointer to an allocated, but uninitialized block of [paramsize] bytes. <code>str</code> references a C string containing a human-readable representation of this distribution's parameters, as used in [output_fn]. The input function should parse this string (e.g., using sscanf) and initialize <code>var->group_state</code> in the same way as [init_fn]. <b>Note:</b> Although this function is optional, be aware that not including it will prevent users from being able to import data defined in terms of this distribution.
When the input function is invoked, <code>var</code> will contain a pointer to initialized pip variable. <code>var->group_state</code> will contain a pointer to an allocated, but uninitialized block of [paramsize] bytes. <code>str</code> references a C string containing a human-readable representation of this distribution's parameters, as used in [output_fn]. The input function should parse this string (e.g., using sscanf) and initialize <code>var->group_state</code> in the same way as [init_fn]. <b>Note:</b> Although this function is optional, be aware that not including it will prevent users from being able to import data defined in terms of this distribution.
</dl>
</p>
<hr />
@ -286,7 +286,7 @@ PIP includes a library of utility functions for use in defining distributions.
</dd>
<dt><code>void pip_box_muller(float8 *X, float8 *Y, int64 *seed)</code></dt>
<dd>Generate two random (but deterministic based on <code>seed</code>), independent, normally distributed floating point numbers (with mean of 0 and standard deviation of 1) using the <a href="http://en.wikipedia.org/wiki/Box-Muller_transform">Box-Muller method</a>. The <b>independent</b> variables will be stored in <code>X</code> and <code>Y</code> -- either or both may be used. The <code>seed</code> value is passed by reference, and is stepped to its next value automatically (note that the Box-Muller method requires two random numbers as input and as a consequence, <code>seed</code> is actually stepped twice).
<dd>Generate two random (but deterministic based on <code>seed</code>), independent, normally distributed floating point numbers (with mean of 0 and standard deviation of 1) using the <a href="http://en.wikipedia.org/wiki/Box-Muller_transform">Box-Muller method</a>. The <b>independent</b> variables will be stored in <code>X</code> and <code>Y</code> -- either or both may be used. The <code>seed</code> value is passed by reference, and is stepped to its next value automatically (note that the Box-Muller method requires two random numbers as input and as a consequence, <code>seed</code> is actually stepped twice).
</dd>
</body></html>
</body></html>