Package csb :: Package statistics :: Module maxent :: Class MaxentModel |
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# Class MaxentModel

source code

             object --+
                      |
    pdf.AbstractDensity --+
                          |
            pdf.BaseDensity --+
                              |
                             MaxentModel

Fourier expansion of a biangular log-probability density

Nested Classes
Inherited from `pdf.AbstractDensity`: `__metaclass__`

Instance Methods
__init__(self, n, beta=1.0) x.__init__(...) initializes x; see help(type(x)) for signature | source code
calculate_statistics(self, data) Calculate the sufficient statistics for the data. | source code
energy(self, x, y=None) Return the energy at positions (x,y). | source code
entropy(self, n=500) Calculate the entropy of the model. | source code
get(self) Return current expansion coefficients. | source code
load(self, aa, f_name) Load set of expansion coefficients from isd+. | source code
load_old(self, aa, f_name) Load set of expansion coefficients from isd. | source code
array |
log_prob(self, x, y) Return the energy at positions (x,y). | source code
log_z(self, n=500, integration=`'``simpson``'`) Calculate the log partion function. | source code
normalize(self, normalize_full=True) Remove parameter, which do not have any influence on the model and compute the partition function. | source code
prob(self, x, y) Return the probability of the configurations x cross y. | source code
sample_weights(self) Create a random set of expansion coefficients. | source code
set(self, coef) Set the fourier expansion coefficients and calculations the new partation function. | source code
z(self) Calculate the partion function . | source code
Inherited from `pdf.AbstractDensity`: `__call__`, `__getitem__`, `__setitem__`, `__str__`, `estimate`, `evaluate`, `get_params`, `random`, `set_params` Inherited from `object`: `__delattr__`, `__format__`, `__getattribute__`, `__hash__`, `__new__`, `__reduce__`, `__reduce_ex__`, `__repr__`, `__setattr__`, `__sizeof__`, `__subclasshook__`

Class Variables
| __abstractmethods__ = ``frozenset([``])``

Properties
float | beta Inverse temperature
int | n Order of the fourier expansion
Inherited from `pdf.AbstractDensity`: `estimator`, `parameters` Inherited from `object`: `__class__`

Method Details

### __init__(self, n, beta=1.0) (Constructor) | source code
x.__init__(...) initializes x; see help(type(x)) for signature Parameters: `n` (int) - order of the fourier expansion `beta` (float) - inverse temperature Overrides: object.__init__

### energy(self, x, y=None) | source code
Return the energy at positions (x,y). Parameters: `x` (array-like) - x-coordinates for evaluation `y` (array-like) - y-coordinates for evaluation

### entropy(self, n=500) | source code
Calculate the entropy of the model. Parameters: `n` (integer) - number of integration points for numerical integration

### load(self, aa, f_name) | source code
Load set of expansion coefficients from isd+. Parameters: `aa` - Amino acid type `f_name` - File containing ramachandran definition

### load_old(self, aa, f_name) | source code
Load set of expansion coefficients from isd. Parameters: `aa` - Amino acid type `f_name` - File containing ramachandran definition

### log_prob(self, x, y) | source code
Return the energy at positions (x,y). Parameters: `x` (array-like) - x-coordinates for evaluation `y` (array-like) - y-coordinates for evaluation Returns: array Overrides: pdf.AbstractDensity.log_prob

### normalize(self, normalize_full=True) | source code
Remove parameter, which do not have any influence on the model and compute the partition function. Parameters: `normalize_full` (boolean) - compute partition function

### set(self, coef) | source code
Set the fourier expansion coefficients and calculations the new partation function. Parameters: `coef` (array like, with shape (4,n,n)) - expansion coefficents

Property Details

### beta Inverse temperature Get Method: unreachable.beta(self) - Inverse temperature Type: float

### n Order of the fourier expansion Get Method: unreachable.n(self) - Order of the fourier expansion Type: int