Incremental GSC parsing (exact copy of the tutorials shared as a separate project) / __pycache__ / gsc.cpython-35.pyc
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_add_names�_generate_encodings�_compute_TPmat�np�zeros� num_bindings�WC�bC� _build_model� _set_weights� _set_biases�_set_quant_listr� � actC_prev�C2N�act�act_prev�extC� extC_prev�ext�ext_prevr� r� �numbers�Number�onesr� �"_compute_recommended_bowl_strength�check_bowl_strengthr� �zeta�t�speed� ema_speed�clamped�q�Tr� �reset)r r� r� r� r� r r r r � sX
zGscNet.__init__c C s= g } x' | j D] } | j | j | � � q W| | _ d S)N)rd r �
find_rolesrm )r rm rn r r r r� ; s zGscNet._set_quant_listc C s] i | _ d d d d d d d d d g | j d
<t j | j d <d | j d
<d | j d <d, t j | j d � | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d! <d | j d" <d# | j d$ <d# | j d% <d# | j d& <d' | j d( <d) | j d* <d | j d+ <d S)-z'Set option variables to default values.r� �H�H0�Qr� r� r� r� r� �trace_varnames�norm_ord�N�coordg����MbP?�
ema_factorr �ema_taur� g �T_min� T_decay_rater� g i@�q_maxg $@�q_rateg �?�cr� Nr� g�������?r� r� T�H0_on�H1_on�Hq_ong{�G�z�?�max_dtg����Mb@?�min_dt�q_policyrr )r� r� �inf�log)r r r r r� A s. (
!
zGscNet._set_optsc C s� | d k r� x� | D]� } | | j k r� | | | j | <| d k rf d t j | j | � | j d <| d k r� t j d | j | � | j d <q t j d � q Wd S)z Update option variable valuesNr� r r� z Check [opts]rr rr )r� r� r� �exprp rq )r r� r r r r r� ] s
! $zGscNet._update_optsc C s� i | _ d | j d <d | j d <d | j d <d | j d <d | j d <d | j d <d | j d
<d | j d <d | j d <d | j d
<d | j d <d S)z)Set encoding variables to default values.g �dp_f�dp_r�dist�coord_f�coord_rN�dim_f�dim_rr� rd �F�R�
similarity)r� )r r r r r� k s
zGscNet._set_encodingsc C sA | d k r= x. | D]& } | | j k r | | | j | <q Wd S)zUpdate encoding variablesN)r� )r r� r r r r r� { s
zGscNet._update_encodingsc s � j d k r� � j d d k r/ t j d � � j d d k rO t j d � � j d � _ � j d � _ � f d d � � j D� � _ nU t � j t � r� � j j j
� _ � j j j
� _ � j j � _ n
t j d � t � j � � _
t � j � � _ t � j � � _ d S) z8Add filler, role, and binding names to the GscNet objectNr� z&Please provide a list of filler names.rd z$Please provide a list of role names.c s. g | ]$ } � j D] } | d | � q q S)r� )r� )r9 r| r� )r r r r: � s z%GscNet._add_names.<locals>.<listcomp>z1[hg] is not an instance of HarmonicGrammar class.)r� r� rp rq r� rd r� r� r} r r rY r! � num_fillersrZ r� )r r )r r r� � s
"
zGscNet._add_namesc s� � j d d k r�t j t j � j � � } t j t j � j � � } x^� j d D]O} t � f d d � | d D� � r� | d | � j j | d d � � j j | d d � f <| d | � j j | d d � � j j | d d � f <qW t � f d d � | d D� � r�| d | � j j | d d � � j j | d d � f <| d | � j j | d d � � j j | d d � f <qW t
j d � qW W| � j d <| � j d
<t � j d � j d d
� j d d � j d �� _
t � j d � j d d
� j d
d � j d �� _ � j d d k rY� j d � _
� j d d k r|� j d � _ � j
j d � _ � j j d � _ � j � j � _ t t � j � � � � f d d � t t � j � � D� � _ d S)z?Generate vector encodings of fillers, roles, and their bindingsr� Nc 3 s | ] } | � j k Vq d S)N)r� )r9 r& )r r r � <genexpr>� s z-GscNet._generate_encodings.<locals>.<genexpr>r r c 3 s | ] } | � j k Vq d S)N)rd )r9 r& )r r r r� � s z6Cannot find some f/r bindings in your similarity list.r� r� r� r� �dp�dimr� r� r� r� r� c s- g | ]# } d t | d � j � � � q S)�Ur )rz �zfill)r9 r. )�ndigitsr r r: � s z.GscNet._generate_encodings.<locals>.<listcomp>)r� r� �diagr� r� rZ �allr� �indexrd rp rq �encode_symbolsr� r� �shaper� r� � num_unitsr! rz r r+ �
unit_names)r r� r� r� r )r r r r� � s@ #<?#<?
zGscNet._generate_encodingsc C sn t | t � r/x*| j D]} t | d � d k r_ | j | d d | d d | d � q t | d � d k r| d d | j k r� | j | d d | d � q(| d d | j k r� | j | d d | d � q(| d d | j k r(| j
| d d | d � q t j d | � q Wn
t j d � t
j | j | j j � d k rjt j d � d S) zCSet the weight and bias values using a HarmonicGrammar object [hg].r r) r z(Check the rule in your Harmonic Grammar:zDThe given grammar as hg is not an instance of HarmonicGrammar class.Fz?The weight matrix (2D array) is not symmetric. Please check it.N)r� r} r� r! �
set_weightr� �set_biasr� �set_filler_biasrd �
set_role_biasrp rq r� �allclose�Wr� )r r� r� r r r r� � s *
!zGscNet._build_modelc C s� t j | j | j � } | j d | j d k rD t j | � } n t j | � } | | _ | | _ | j j
j | j � | _ d S)zuCompute the matrices of change of basis from conceptual to neural and from neural to conceptual coordinates.
r r N)
r� �kronr� r� r r �inv�pinv�TP�TPinvr� �dot�Gc)r r r r r r r� � s zGscNet._compute_TPmatc C s+ | j j j | j � j | j � | _ d S)zJCompute the weight values in the neural space (distributed representation)N)r r� r r� r )r r r r r� � s zGscNet._set_weightsc C s | j j j | j � | _ d S)zHCompute the bias values in the neural space (distributed representation)N)r r� r r� r� )r r r r r� � s zGscNet._set_biasesc C s� t j j | j � \ } } t | � } t j t | j � � d k r� | j d k r� | j | j
| j } | j | j
| d | j } nC t | j | j
| j � } t | j | j
| d | j � } t | | | � } n | } | S)z^Compute the recommended value of bowl strength. Note that the value depends on external input.r r ) r� r �eighr� �max�sum�absr� r� r� r� �min)r �eigvalsZeigvecsZeig_maxZbeta1Zbeta2�valr r r r� � s " %z)GscNet._compute_recommended_bowl_strengthTc C sU | j � } | j d | k r0 t j d | � | rQ t d | j d | f � d S)zaCompute and print the recommended beta value
given the weights and biases in the C-space.r� z*Bowl strength should be greater than %.4f.zB(Current bowl strength: %.3f) must be greater than (minimum: %.3f)N)r� r� rp rq rF )r �dispZbeta_minr r r r� s
zGscNet.check_bowl_strengthc C s� t | t j � p! t | t j � s1 t j d � t | t j � rY | t j | j � } | j d | j k r| t j d � | | _
| j d | j
� | _ d S)a� Update the bowl center
Usage:
>>> net.update_bowl_center(0.3) # Set the bowl center to 0.3 * ec{1}
>>>
>>> import numpy as np
>>> bowl_center = np.random.random(net.num_bindings)
>>> net.update_bowl_center(bowl_center)
: bowl_center: float or 1d NumPy array (size=number of bindings). the bowl center.
z:You must provide a scalar or a NumPy array as bowl_center.r zvWhen you provide a NumPy array as bowl_center, it must have the same number of elements as the number of f/r bindings.r� N)
r� r� �ndarrayr� r� rp rq r� r� r r� r� r� )r r� r r r �update_bowl_center s $
zGscNet.update_bowl_centerc C sN | d k r- | j � | j d | j d <n
| | j d <| j d | _ d S)a0 Replace the current bowl strength with
the recommended bowl strength (+ offset)
Usage:
>>> net = gsc.GscNet(...)
>>> net.set_weight('a/(0,1)', 'b/(1,2)', 2.0)
>>> net.update_bowl_strength()
: bowl_strength : float or None (=default)
Nr� r� )r� r� r� )r r� r r r �update_bowl_strength0 s
zGscNet.update_bowl_strengthc C sl | j | � } | j | � } | rK | | j | | f <| j | | f <n | | j | | f <| j � d S)z�Set the weight of a connection between binding1 and binding2.
When symmetric is set to True (default), the connection weight from
binding2 to binding1 is set to the same value.N)�
find_bindingsr� r� )r Z
binding_name1Z
binding_name2�weight� symmetric�idx1Zidx2r r r r
E s 'zGscNet.set_weightc C s* | j | � } | | j | <| j � d S)z+Set bias values of [binding_name] to [bias]N)r" r� r� )r Z binding_name�bias�idxr r r r R s
zGscNet.set_biasc s� d d � | j D� } t | t � s. | g } xF t | � D]8 \ } � � f d d � t | � D� } | | j | <q; W| j � d S)zPSet the bias of bindings of all roles
with particular fillers to [bias].c S s# g | ] } | j d � d � q S)r� r )rx )r9 �bbr r r r: ] s z*GscNet.set_filler_bias.<locals>.<listcomp>c s( g | ] \ } } � | k r | � q Sr r )r9 r. �ff)r/ r r r: a s N)r� r� r rD r� r� )r � filler_namer& � filler_listr� r' r )r/ r r Y s zGscNet.set_filler_biasc s� d d � | j D� } t | t � s. | g } xF t | � D]8 \ } � � f d d � t | � D� } | | j | <q; W| j � d S)zPSet the bias of bindings of all fillers
with particular roles to [bias].c S s# g | ] } | j d � d � q S)r� r )rx )r9 r( r r r r: j s z(GscNet.set_role_bias.<locals>.<listcomp>c s( g | ] \ } } � | k r | � q Sr r )r9 r. �rr)r\ r r r: n s N)r� r� r rD r� r� )r rn r& � role_listr� r' r )r\ r r
f s zGscNet.set_role_biasc s/ t | t � s | g } � f d d � | D� S)z@Find the indices of the bindings from the list of binding names.c s" g | ] } � j j | � � q Sr )r� r )r9 r( )r r r r: ~ s z(GscNet.find_bindings.<locals>.<listcomp>)r� r )r r� r )r r r" y s zGscNet.find_bindingsc s~ t | t � s | g } d d � | j D� } g } xC t | � D]5 \ } � � f d d � t | � D� } | | 7} qA W| S)Nc S s# g | ] } | j d � d � q S)r� r )rx )r9 r( r r r r: � s z'GscNet.find_fillers.<locals>.<listcomp>c s( g | ] \ } } � | k r | � q Sr r )r9 r. r) )r/ r r r: � s )r� r r� rD )r r* r+ Z
filler_idxr� r' r )r/ r � find_fillers� s zGscNet.find_fillersc s~ t | t � s | g } d d � | j D� } g } xC t | � D]5 \ } � � f d d � t | � D� } | | 7} qA W| S)Nc S s# g | ] } | j d � d � q S)r� r )rx )r9 r( r r r r: � s z%GscNet.find_roles.<locals>.<listcomp>c s( g | ] \ } } � | k r | � q Sr r )r9 r. r, )r\ r r r: � s )r� r r� rD )r rn r- Zrole_idxr� r' r )r\ r r� � s zGscNet.find_rolesc C s4 | d k r | j � } | j | j | j d d �S)z�Convert an activation state vector to a matrix form
in which each row corresponds to a filler
and each column corresponds to a role.N�orderr� )ZS2C�reshaper� rZ )r r� r r r �vec2mat� s zGscNet.vec2matc C s% | d k r | j } | j j | � S)z6Change basis: from conceptual/pattern to neural space.N)r� r r )r r� r r r r� � s z
GscNet.C2Nc C s% | d k r | j } | j j | � S)z6Change basis: from neural to conceptual/pattern space.N)r� r r )r r� r r r �N2C� s z
GscNet.N2Cr� c C ss | d d k rA t t j t | | � d | j d | j �� n. t t j t | | � d | j d | j �� d S)zQPrint the weight matrix in a readable format
(in the pattern coordinate).r �Cr �columnsNrr )rF �pd� DataFrame�getattrr� r )r r� r r r � read_weight� s zGscNet.read_weightr� c C s | d d k r� | rV t t j t | | � j | j d � d | j d d g �� qt t j t | | � j d | j � d d g d | j �� n� | r� t t j t | | � j | j d � d | j d d g �� n= t t j t | | � j d | j � d d g d | j �� d S)z2Print the bias vector (in the pattern coordinate).r r3 r r4 r& Nrr )rF r5 r6 r7 r0 r� r� r )r r� Zprint_verticalr r r � read_bias� s zGscNet.read_biasc C sV | d k r | j } | j | j | � � } t t j | d | j d | j �� d S)z[Print the current state (C-SPACE) in a readable format.
Pandas should be installed.Nr r4 )r� r1 r2 rF r5 r6 r� rd )r r� r� r r r �
read_state� s
zGscNet.read_statec s� d � | d k r � j } � j � j | � � � t j � d d �} � f d d � | D� � d d � t � � j � D� � | r� � � � f d d � t | � D� } � f d d � | D� � | r� t � � � S)
z�Print a grid point close to the current state. The grid point will be
chosen by the snap-it method: a filler with the highest activation
value in each role will be chosen.g �?N�axisr c s g | ] } � j | � q Sr )r� )r9 r. )r r r r: � s z*GscNet.read_grid_point.<locals>.<listcomp>c S s g | ] } d | � q S)z%s/%sr )r9 r( r r r r: � s c sN g | ]D \ } } � | | f � k r � j | � j j j k r | � q Sr )r� r� r r
)r9 Zrole_numZ
filler_num)r� �act_minr r r r: � s c s g | ] } � | � q Sr r )r9 r| )�winnersr r r: � s ) r� r1 r2 r� �argmaxr� rd rD rF )r r� r �skipZ
winner_idxrY r )r� r< r r= r �read_grid_point� s %
zGscNet.read_grid_pointg �cAc
s/ � j � j d k r1 t j d � j � j � � j � j | k rW � j � j } d g � j } xC t � j � D]2 \ } } � f d d � � j | � D� | | <qw Wg � t j | � } � j � j d k r� t d � j � j � x� t t
j | � � D]� \ } } | d d d
k r\t d | d d d
�| d d d
k r\t d
� t | � } � j
| � � j � } | | k r�| | | <� j | � q| t j | � k r| � t j | � <| | t j | � <qWt j | � d d d � } � f d d � | D� � _ | | � _ d S)a� Get a list of the top [n] grid points with high harmony values
and compute H_0 at every grid point. Regardless of the [n] value,
the program will check every grid point. Note that the number of
grid points is [num_fillers]^[num_roles] which increases explosively
as [num_roles] increases. This method works only when the total
number of grid points is reasonably small (currently set to 1e7).g �cAz?There are too many grid points (= %d) to check all grid points.Nc s g | ] } � j | � q Sr )r� )r9 r. )r r r r: s z*GscNet.get_grid_points.<locals>.<listcomp>i' zOf %d grid points: r g ��@r z[%06d]�endry g j�@c s g | ] } � | � q Sr r )r9 r. )�gpsetr r r: $ s rr )r� rZ rp rq rD rd r� r� r� rF rV �productr � set_stater� r r �argmin�argsortrB �gpset_h)
r �nrm Zrindr\ rG r. �gpZhhr' r )rB r r �get_grid_points� s8 *"
zGscNet.get_grid_pointsc C s t j j | � d S)zSet a random number seed.N)r� �randomr� )r �numr r r r� ' s zGscNet.set_seedc C s2 | j | � t | j d � | j | j | � S)zEvalutate total harmonyr� )�Hg�floatr� r� �Qa)r r� r r r r� 7 s zGscNet.Hc C s< t | j d � | j | � t | j d � | j | � S)zEvalutate H_G (= H0 + H1)r� r� )rN r� r� �H1)r r� r r r rM < s z GscNet.Hgc C sI | d k r | j } d | j | j � j | � | j | j j | � S)z Evaluate H0Ng �?)r� r r r� r� )r r� r r r r� B s z GscNet.H0c C sJ | d k r | j } | j d | | j j j | j � j | | j � S)zEvalutate H1 (bowl harmony)Ng �?g �)r� r� r� r� r r )r r� r r r rP I s z GscNet.H1c C s4 | j d | j | � d | j d | j | � S)z5Evaluate quantization harmony Q = c * Q0 + (1-c) * Q1r� r )r� �Q0�Q1)r r� r r r r� Q s zGscNet.Qc C s@ | j d | j | � d | j d | j d | d | j � S)z5Evaluate quantization harmony Q = c * Q0 + (1-c) * Q1r� r r� rm )r� rQ �Q1arm )r r� r r r rO W s z GscNet.Qac C s@ | j d | j | � d | j d | j d | d | j � S)z5Evaluate quantization harmony Q = c * Q0 + (1-c) * Q1r� r r� rm )r� rQ �Q1brm )r r� r r r �Qb\ s z GscNet.Qbc C sB | d k r | j } | j | � } t j | d d | d � S)z Evaluate Q0Nr) r )r� r2 r� r )r r� r� r r r rQ a s z GscNet.Q0c C sP | d k r | j } t j t j | j | j | � � d d d �d d � S)z Evaluate Q1Nr) r; r r )r� r� r r1 r2 )r r� r r r rR i s z GscNet.Q1c C s~ | d k r | j } | d k r* | j } | j | � } d } x7 | D]/ } | | j | | � } | | d d 7} qF W| S)z Evaluate Q1 (sum of squared = 1)Nr r r) )r� rm r2 r )r r� rm r� �q1�qlist�ssqr r r rS p s
z
GscNet.Q1ac C s� | d k r | j } | d k r* | j } | j | � } d } x? | D]7 } | | j | | � } | | d d | d 7} qF W| S)z%Evaluate Q1 (sum of squared = 0 or 1)Nr r r) )r� rm r2 r )r r� rm r� rV rW rX r r r rT � s
z
GscNet.Q1bc C s2 | j | � t | j d � | j | j | � S)z;Compute the harmony gradient evaluated at the current stater� )�HgGradrN r� r� �QaGrad)r r� r r r �HGrad� s z GscNet.HGradc C s< t | j d � | j | � t | j d � | j | � S)z+Compute the gradient of grammar harmony H_Gr� r� )rN r� �H0Grad�H1Grad)r r� r r r rY � s z
GscNet.HgGradc C s3 | d k r | j } | j j | � | j | j S)N)r� r r r� r� )r r� r r r r\ � s z
GscNet.H0Gradc C s@ | d k r | j } | j | j j | � | j j | j � S)N)r� r� r r r� )r r� r r r r] � s z
GscNet.H1Gradc C s4 | j d | j | � d | j d | j | � S)Nr� r )r� �Q0Grad�Q1Grad)r r� r r r �QGrad� s z GscNet.QGradc C s@ | j d | j | � d | j d | j d | d | j � S)Nr� r r� rm )r� r^ �Q1aGradrm )r r� r r r rZ � s z
GscNet.QaGradc C s@ | j d | j | � d | j d | j d | d | j � S)Nr� r r� rm )r� r^ �Q1bGradrm )r r� r r r �QbGrad� s z
GscNet.QbGradc C sY | d k r | j } | j | � } d | d | d d | } t j d | j | � S)Nr) r zij,i)r� r2 r� �einsumr )r r� r� r r r r r^ � s
z
GscNet.Q0Gradc C s� | d k r | j } | j j | j | j | j f d d �} | j | � } | j | � } t j d | d � d } t j d | | � } d
t j d | | � S) z1Compute the gradient of quantization harmony (Q1)Nr/ r� zij->jr) r z
ij,ijk->jk� zj,jk�����)
r� r r0 r� rZ r r2 r1 r� rd )r r� ZTPinv_reshapedr� �amat�term1�term2r r r r_ � s !z
GscNet.Q1Gradc
C s� | d k r | j } | d k r* | j } | j | � } d } xh | D]` } | | } | j | d d � f } | d j � d } t j d | | � } | | | 7} qF Wd | S)z1Compute the gradient of quantization harmony (Q1)Nr r) r zi,ij->jre rf )r� rm r2 r r r� rd )
r r� rm r� �q1gradrW � curr_actC�
curr_TPinv�
curr_term1�
curr_term2r r r ra � s
zGscNet.Q1aGradc C s� | d k r | j } | d k r* | j } | j | � } d } x} | D]u } | | } | j | d d � f } | j | � } | | d d | d } t j d | | � }
| | |
7} qF Wd | S)z1Compute the gradient of quantization harmony (Q1)Nr r r) zi,ij->jre rf )r� rm r2 r r r� rd ) r r� rm r� rj rW rk rl rX rm rn r r r rb � s
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zCreate storage for traces.r r� z�Check [trace_list] that should be a list object.
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�tracesN) r� r� r rp rq r! �hasattrrp �
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zGscNet.initialize_tracesc C si d | j k r, | j d j t | j � � d | j k rU | j d j | j � � d | j k r~ | j d j | j � � d | j k r� | j d j | j � � d | j k r� | j d j | j � d | j k r� | j d j | j � d | j k r| j d j | j � d | j k r?| j d j | j
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Log tracesr� r� r� r� r� r� r� r� r� N) rp r r r� r� r� r� r� r� r� r� r� )r r r r rr * s$ zGscNet.update_tracesc C s5 x. | j D]# } t j | j | � | j | <q
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)r r r r� )r �Ar r r �_compute_projmatL s zGscNet._compute_projmatg �?Fc s� t | t � s | g } t | t � s0 | g } t | � d k rg t | � t | � k rg t j d � d | _ | | _ t j | j � } | r� d d � | D� } | j
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z"Clamp f/r bindings to [clamp_vals]r zMThe number of bindings clamped is not equal to the number of values provided.Tc S s# g | ] } | j d � d � q S)r� r )rx )r9 r� r r r r: g s z GscNet.clamp.<locals>.<listcomp>g c s" g | ] } | � k r | � q Sr r )r9 r( )r' r r r: t s Nr )r� r r! rp rq r� �binding_names_clampedr� r� r� r� r" � clampvecCr �aranger rx �projmatr r� �clampvec� act_clampedr� r2 r� ) r r� Z
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% z GscNet.clampc C s4 | j d k r0 | ` | ` | ` | ` d | _ d S)ZUnclampTFN)r� r} rz r| ry )r r r r �unclamp~ s zGscNet.unclampc C s, | d k r | j } | j j | � | j S)zXGet a new activation vector after projecting an activation vector
to a subspace.N)r� r| r r} )r r� r r r r~ � s zGscNet.act_clampedc C s� t | t � s | g } t | t � s0 | g } t | � d k rg t | � t | � k rg t j d � | j � | j | � } | | j | <| j | j � | _ d S)zSet external input.r z2binding_names and ext_vals have different lengths.N)
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zGscNet.set_inputc C s. t j | j � | _ | j | j � | _ d S)zRemove external inputN)r� r� r� r� r� r� )r r r r r� � s zGscNet.clear_inputc C s� | j d | _ | j d | _ d | _ | j � | j � | _ t j | j � | _
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| j � | j � t | d � r� | ` d S)z<Reset the model. q and T will be set to their initial valuesr� r� r rp N)r� r� r� r� �randomize_stater2 r� r� r� r� r� r r r� r� r� rq rp )r r r r r� � s
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zGscNet.set_stateg �?g�������?c C s: t j j d | d | d | j � | _ | j � | _ d S)zSet initial state�loc�scaler� N)r� rK �normalr� r� r� r� )r �mu�sdr r r �set_init_state� s 'zGscNet.set_init_stater r c C s7 t j j | | | j � | _ | j | j � | _ d S)zgSet the activation state to a random vector
inside a hypercube of [minact, maxact]^num_bindingsN)r� rK �uniformr� r� r� r� )r ZminactZmaxactr r r r� � s zGscNet.randomize_stater r� c C s� d | _ | j | } d | _ | r2 | j | � xe | j | k r� | j d | d | � | rj | j � | d k r5 | j d | d | � | j r5 Pq5 W| j | _ | j | j d d � <| j
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g �d S)z2Run simulations for a given amount of time [time].Fr �update_T�update_qN�tol�testvarr� r� r �xlabel�Time�xtick�ylabelZBindings� yticklabels� grayscale�colorbar� val_rangerr )� convergedr� �steprt �updaterr �check_convergence�rtr� r� r r r� rv r2 rp r� r� �linspacer r+ r �interpru �heatmapr� )r Zdurationr� r� Z log_tracers �plotr� r� r� r� Zt_maxZ
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GscNet.runc C sw | j | j d d � <| j | j d d � <| j � | j � | rc | j d d k rc | j � | rs | j � d S)z8Update state, speed, ema_speed (and optionally T, q, dt)Nr� r ) r� r� r� r� � update_state� update_speedr� r� r� )r r� r� r r r r� s
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GscNet.updatec C s� | j � } t j | j | � � } | d k rp t | j d | j d | � | _ t | j d | j � | _ | j | j 7_ | j | j | 7_ | j
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� | _ d S)zUpdate state (with noise)r r� r� N)r[ r� �sqrtr r r� r� r r� r� � add_noiser� r~ r2 r� )r ZgradZgrad_magr r r r� ( s $
zGscNet.update_statec C s= | j t j d | j | j � t j j | j � 7_ d S)z)Add noise to state in neural coordinates.r) N)r� r� r� r� r� rK �randnr )r r r r r� 8 s !zGscNet.add_noisec C sB t j | j d | j � | j | j d | j d | _ d S)zUpdate temperaturer� r� N)r� r� r� r� r� )r r r r r� > s zGscNet.update_Tc C s� | j d d k r_ t j | j | j d d d � d f | j d d d � d f � | _ n7 t t | j | j d | j | j d � d � | _ d S)z Update quantization strengthr� Nr r r� r� )r� r� r� r� r� r r r� )r r r r r� D s
FzGscNet.update_qc C s� | j d d k r& | j | j } n# | j d d k rI | j | j } t j | d | j d �t | j � | _ | j
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zGscNet.update_speedc C sO | d k r$ | j | k r$ d | _ | d k rK | j � | k rK d | _ d S)zOCheck if the convergence criterion (distance vs. ema_speed) has been satisfied.r� Tr� N)r� r� r� )r r� r� r r r r� d s zGscNet.check_convergencer3 c
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t j d � | d k r� t | j | � d | j d | j d | d | d | d d
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! zGscNet.plot_statec C s� | j d } | d k rB | j | j | d d � j � j } n
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z"Plot the trace of a given variabler� r� Nr r� �fontsize� Trr ) rp r2 r� �pltr� r� r� �grid�show)r �varnamer^ �yr r r �
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r" r. r� r1 r� r2 r8 r9 r: r@ rJ r� r� rM r� rP r� rO rU rQ rR rS rT r[ rY r\ r] r` rZ rc r^ r_ ra rb rt rr rv rx r r� r~ r� r� r� rD r� r� r� r� r� r� r� r� r� r� r� r� r r r r r� � s� L .
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8 r� r� g c C s� | d k r t j | � } n� | d k r3 | } n | | k rL t j d � t | t j � r� | t j | | f � d | t j | | � } t | | | � } | S)a� Generate the vector encodings of [num_symbols] symbols assuming a given similarity structure.
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Usage:
>>> gsc.encode_symbols(2)
>>> gsc.encode_symbols(3, coord='dist', dp=0.3, dim=5)
: num_symbols : int, number of symbols to encode
: coord : string, 'dist' (distributed representation, default) or 'local' (local representation)
: dp : float (0 [default] <= dp <= 1) or 2D-numpy array of pairwise similarity (dot product)
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:
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r i�� c C s> | j | k j � s" t j d � t j | � d k j � rJ t j d � t j j d | | � j | | d d �} d } d } d } x� t
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Don Matthias wrote the original script in MATLAB for the LDNet program.
He explains how this program works as follows:
Given square matrix dpMatrix of dimension N-by-N, find N
dim-dimensional unit vectors whose pairwise dot products match
dpMatrix. Results are returned in the columns of M. itns is the
number of iterations of search required, and may be ignored.
Algorithm: Find a matrix M such that M'*M = dpMatrix. This is done
via gradient descent on a cost function that is the square of the
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z&dot_products: dp_mat must be symmetricr z<dot_products: dp_mat must have all ones on the main diagonalr� r/ r� g�������?g�����ư>Fg{�G�z�?Tz#Didn't converge after %d iterations)r� r rp rq r� r �anyrK r� r0 r+ r r r r rF ) r� r� Zdp_matZmax_iterr� Zmin_stepr� r� Ziter_num�incr� �max_diffr r r r� � s(
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