Generate or learn various random processes.
npm install @totemstan/randprGenerate or learn various random processes.
In its generation mode, RANDPR will generate a first-order, K-state Markov process given
its first-order, KxK transition probabilites. RANDPR also computes equilibrium probabilities (if they
exist), mean recurrence times, time to first absorption, absorption probabilites and the statistical
auto-covariance function. RANDPR can create ergodic, regular, absorptive processes, Weiner
(stationary in first increments), and wide-sense stationary processes.
In its learning mode, RANDPR produces supervised and unsupervised estimates:
MLEs of the underlying transition probabilities, number of coherence intervals (and related SNR),
and the underlying intensity profile. (MLE for the Weiner process, e.g. first time to exit, have not
yet been implemented).
Both discrete- and continious-time models are supported in either forward or reverse mode.
RANDPR can be customized with onStep(), onBatch(), onEnd(), and filter() methods to
sample metrics in both forward and reverse modes. Both modes can make use of piped
streams to minimize memory usage.
RANDPR supports the following processes.
K-state process governed by a prescribed conditional independency network:
eqP: [pr, ...] the K equilibrium probs
net: [ {var: probs, ...}, ... ] the conditional dependencies
or expressed as a DAG:
dag: { ... }
Inhomogenious K-state process with specified transition probabilties:
states: number of states
where its K^2 transition probs are synthesized using the gillespie model.
K-state process with specified transition probabilities:
TxPrs: [ [...], ....] the K^2 (K^2-K independent) transition probs
or:
states: K
TxPrs: { from: {to: pr, ... } , ... "from, ..." : "to, ..." }
where from-to transition probs must be specified to conserve prob, i.e. sum_k TxPrs[n][k] = 1.
Correlated, stateless random process whose parameters are typically derived (see man)
for a process with known correlation intervals M = T/Tc or SNR = sqrt{ M / ( 1 + deltaC / M) }.
values: [ ... ] pc eigen values [unitless]
vectors: [ [... ], ...] pc eigen values [sqrt Hz]
ref: reference eigenvalue
dim: max pc dimension (M = T/Tc )
mean: mean count in observation interval T
Stateless process with moving 2nd moment (but stationary in 1st increments) where:
walks: number of walks at each time step (0 disables)
This still needs a quick debug. May need to define false Markov parms to init it.
Stateless Ornstein-Ulenbeck process with:
theta: 0-pi
a: sigma/sqrt(2 theta)
Gauss mixing process with specified mu,sigma (mean, covar), or specified snr, cone, mixes, oncov, offcov
* www.statslab.cam.ac.uk/~rrw1
* www.stat.yale.edu/~pollard
* people.math.gatech.edu/~randall
* www.stat.berkeley.edu/~pitman
* www.math.dartmouth.edu/~pw
npm install @totemstan/randpr # install
npm run start [ ? | $ | ...] # Unit test
npm run verminor # Roll minor version
npm run vermajor # Roll major version
npm run redoc # Regen documentation
Require the module:
var RAN = require("randpr");
then create a new instance:
var ran = new RAN({
key: value, // set key
"key.key": value, // indexed set
"key.key.": value // indexed append
}, function (err) {
console.log( err ? "something evil is lurking" : "look mom - Im running!");
});
where configuration keys follow ENUMS deep copy conventions
The instance can then be piped:
ran.pipe()
to genenerate a process (if configured in the forward/generate mode), or learn process parameters (if
configured in the reverse/learning mode).
Requires: module:man, module:stream
Example
``js
R2.1 - config methods:
var ran = new RAN({
p: [.4],
//markov: [[0.1, 0.9], [0.1, 0.9]]
//markov: { states: 3, 0: {1: 0.8, 2: 0.1}, 1: {0: 0.1} }
});
`
Example js
R2.3 - config methods:
var ran = new RAN({
emP: {
dims: [3,3],
weights: [1,1]
},
markov: { states: 9, 0: {1: 0.8, 2: 0.1}, 1: {0: 0.1}, "0,1": { "1,0": .4} }
});
`
Example js
R2.4 - config methods:
var ran = new RAN({
emP: {
dims: [2,2,2],
weights: [1,1,1]
},
markov: "random"
});
`
Example js
R3 sync pipe with various textbook examples, custom filtering with supervised learning validation:
var ran = new RAN({
// these have same eqprs [.5, .5] (symmetry -> detailed balance --> eqP[k] = 1/K eqpr)
//markov: [[.6, .4],[.4, .6]],
//markov: [[0.83177, 0.16822], [0.17152, 0.82848]],
//markov: [[.5, .5], [.5, .5]],
//markov: [[0.1, 0.9], [0.9, 0.1]],
// textbook exs
markov: [[0.1, 0.9], [0.1, 0.9]], // pg142 ex3
//markov: [[1/2, 1/3, 1/6], [3/4, 0, 1/4], [0,1,0]], // pg142 ex2 eqpr [.5, .333, .1666]
//markov: [[1,0,0], [1/4, 1/2, 1/4], [0,0,1]], // pg143 ex8 no eqprs
// these have different eqprs
//markov: [[0.9,0.1],[0.1,0.9]],
//markov: [[0.1, 0.9], [0.1, 0.9]], // bernoulli scheme has identical rows
//markov: [[0.1, 0.9], [0.3, 0.7]],
//markov: [[0.1, 0.9], [0.4, 0.6]],
// textbook exs
//markov: [[0,1],[1,0]], // pg433 ex16 regular (all states reachable) absorbing/non on even/odd steps non-regular non-absorbing but ergodic so --> eqpr [.5, .5]
//markov: [[0.5,0.25,0.25],[0.5,0,0.5],[0.25,0.25,0.5]], // pg406 ex1 regular (after 2 steps) thus ergodic so eqpr [.4, .2, .4]
//markov: [[0,1,0,0,0], [0.25,0,0.75,0,0], [0,0.5,0,0.5,0], [0,0,0.75,0,0.25], [0,0,0,1,0]], // pg433 ex17 non-absorbing non-regular but ergodic so eqpr [.0625, .25, .375]
//markov: [[1,0,0,0,0],[0.5,0,0.5,0,0],[0,0.5,0,0.5,0],[0,0,0.5,0,0.5],[0,0,0,0,1]], // 2 absorbing states; non-ergodic so 3 eqpr = [.75 ... .25], [.5 ... .5], [.25 ... .75]
//markov: [[1-.2, .1, .1], [0, 1-.1, .1], [.1, .1, 1-.2]],
//markov: [[1-.2, .1, .1], [0.4, 1-.5, .1], [.1, .1, 1-.2]],
//markov: [[1-.6, .2, .2,.2], [.1, 1-.3, .1,.1], [.1, .1, 1-.4,.2],[.1,.1,1-.8,.6]], // non-ergodic
batch: 50, // supervised learning every 50 steps
filter: function (str, ev) {
switch (ev.at) {
case "config":
//Trace(ev);
str.push(ev);
break;
case "batch":
//Trace(ev.s,ev.rel_txpr_error);
Trace(ev);
break;
case "end":
Trace(ev);
var
A = ev.stats.mle_tr_probs,
B = ev.stats.mle_em_probs,
H = ev.stats.mle_holding_times;
Trace("MLEs", {
holdTimes: JSON.stringify(H),
emProbs: JSON.stringify(B),
trProbs: JSON.stringify(A)
});
str.push(ev);
break;
}
},
N: 500,
steps: 500
});
ran.pipe( function (store) {
Trace(store);
});
`
Example js
R3.1 - gen process for R3.2 with async pipe to stdout:
var ran = new RAN({
markov: [[0.1, 0.9], [0.1, 0.9]], // pg142 ex3
batch: 800, // supervised learning every 50 steps
N: 1000,
filter: function (str,ev) {
switch (ev.at) {
case "batch":
case "config":
case "end":
Trace(JSON.stringify(ev));
}
},
steps: 800
});
ran.pipe(process.stdout);
`
Example js
R3.2 - gen process for R3.3 using async pipe to stdout:
var ran = new RAN({
markov: [[0.1, 0.9], [0.1, 0.9]], // pg142 ex3
//batch: 50, // supervised learning every 50 steps
N: 10,
//keys: {state:"u", index: "n"},
filter: function (str,ev) {
switch (ev.at) {
case "jump":
Trace(ev);
break;
default:
}
},
steps: 20
});
ran.pipe(process.stdout); // stdout evs used in R3.3
`
Example js
R3.3 - supervised learning with R3.2 evs using sync pipe to store:
var
evs = [
{ at: 'jump', t: 1, s: 1, index: 3, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 1, s: 1, index: 5, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 1, s: 1, index: 6, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 1, s: 1, index: 7, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 1, s: 1, index: 8, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 1, s: 1, index: 9, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 2, s: 2, index: 2, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 2, s: 2, index: 3, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 3, s: 3, index: 2, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 3, s: 3, index: 6, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 4, s: 4, index: 6, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 5, s: 5, index: 1, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 6, s: 6, index: 1, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 8, s: 8, index: 9, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 9, s: 9, index: 3, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 9, s: 9, index: 8, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 9, s: 9, index: 9, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 10, s: 10, index: 3, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 10, s: 10, index: 8, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 11, s: 11, index: 4, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 13, s: 13, index: 4, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 13, s: 13, index: 8, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 14, s: 14, index: 8, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 15, s: 15, index: 0, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 16, s: 16, index: 0, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 16, s: 16, index: 1, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 16, s: 16, index: 3, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 17, s: 17, index: 3, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 18, s: 18, index: 1, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 18, s: 18, index: 3, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 18, s: 18, index: 6, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 18, s: 18, index: 9, state: 0, hold: 0, obs: null },
{ at: 'jump', t: 19, s: 19, index: 3, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 19, s: 19, index: 6, state: 1, hold: 0, obs: null },
{ at: 'jump', t: 19, s: 19, index: 9, state: 1, hold: 0, obs: null }
],
ran = new RAN({
learn: function (supercb) {
evs.$( true, (evs) => {
Trace( evs ? supervising ${evs.length} events : " supervised" );
if (evs) // feed supervisor
supercb(evs);
else // terminate supervisor
supercb(null);
});
},
batch: 50, // supervised learning every 50 steps
filter: function (str, ev) {
switch (ev.at) {
case "config":
Trace(ev);
str.push(ev);
break;
case "batch":
//Trace(ev.s,ev.rel_txpr_error);
Trace(ev);
break;
case "end":
//Trace(ev);
str.push(ev);
break;
}
},
markov: {},
//keys: {state:"u", index: "n"},
K: 2, // assume 2-state process
N: 50 // assume 50 members in ensemble
});
ran.pipe( function (store) {
Trace(store);
});
``
Kind: inner method of RANDPR
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