Xc@`sldZddlmZmZmZddlZddlmZddlZddlm Z ddl m Z m Z ddl Z ddl mZddlmZdd l mZdd lmZd Zd ejfd YZe jjeddeZdejfdYZdZdZddddddZddddddZ dddddddZ!dZ"ddddddZ#dZ$dde%ddddZ&ddddd Z'd!Z(ddddd"Z)d#Z*ddddgddd$Z+ej,egd%Z-ej.d&ej/e-d'e%d(d)d*d+e0fd,YZ1dS(-sHDefine random number Type (`RandomStateType`) and Op (`RandomFunction`).i(tabsolute_importtprint_functiontdivisionN(tcopy(t string_types(treducetxrange(ttensor(topt(tgof(toptdbsrestructuredtext entRandomStateTypecB`sYeZdZdZeddZdZdZdZ dZ e dZ RS( s1 A Type wrapper for numpy.random.RandomState. The reason this exists (and `Generic` doesn't suffice) is that RandomState objects that would appear to be equal do not compare equal with the '==' operator. This Type exists to provide an equals function that is used by DebugMode. cC`sdS(NR ((tself((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt__str__scC`s |j|r|StdS(N(tis_valid_valuet TypeError(R tdatatstricttallow_downcast((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytfilter"scC`st|tjjkS(N(ttypetnumpytrandomt RandomState(R ta((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyR(scC`s|j}|j}|d|dkr0tStj|d|dksQtS|d|dkritS|d|dkrtS|ddkr|d|dkrtSntS(Niiiii(t get_statetFalseRtalltTrue(R Rtbtsatsb((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt values_eq+s  cC`sdS(N(tNone(R tobj((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytget_shape_infoAscC`stjjj}d}x|D]}t|trJ|t|7}q"t|tjrs||j|j 7}q"t|t r|tj dj 7}q"t|t r|tj dj 7}q"t q"W|S(Nitinttfloat(RRRRt isinstancetstrtlentndarraytsizetitemsizeR$tdtypeR%tNotImplementedError(R t shape_infotstateR*telem((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytget_sizeDs  cC`s ||kS(N((RR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytmay_share_memoryUsN( t__name__t __module__t__doc__R RR!RRR R#R1t staticmethodR2(((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyR s      sV Py_XDECREF(%(oname)s); %(oname)s = %(iname)s; Py_XINCREF(%(oname)s); itRandomFunctioncB`skeZdZdZeddZdZdZd Zd Z d Z d Z d Z dZ RS(s Op that draws random numbers from a numpy.random.RandomState object. Parameters ---------- fn : string or function reference A member function of numpy.random.RandomState. A string will be interpreted as the name of a member function of numpy.random.RandomState. Technically, any function with a signature like the ones in numpy.random.RandomState will do. This function must accept the shape (sometimes called size) of the output as the last positional argument. outtype The theano Type of the output. args A list of default arguments for the function kwargs If the 'inplace' key is there, its value will be used to determine if the op operates inplace or not. If the 'ndim_added' key is there, its value indicates how many more dimensions this op will add to the output, in addition to the shape's dimensions (used in multinomial and permutation). tfntouttypetinplacet ndim_addedicC`s|j||||gdS(N(t __setstate__(R R8R9R:R;((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt__init__scC`s0t|j}|d=d|kr,|d=n|S(Ntexec_fnt destroy_map(tdictt__dict__(R td((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt __getstate__s   cC`st|trD|d|d|d|dg}|jj|n|}|\}}}}||_t|trttjj ||_ n ||_ ||_ ||_ |j ridgd6|_ n||_dS(NR8R9R:R;i(R&R@RAtupdateR8RtgetattrRRRR>R9R:R?R;(R tdctR/R8R9R:R;((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyR<s"      cC`sd|jjS(NsRandomFunction{%s}(R>R3(R ((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyR scG`stj|dd}|dkr3|jd}n|}|jjdksQt|jjdks{|jjdks{tt|jtst ddt j nt t tj|}tj|||g||j|jgS(sV Parameters ---------- r A numpy.random.RandomState instance, or a Variable of Type RandomStateType that will contain a RandomState instance. shape An lvector with a shape defining how many samples to draw. In the case of scalar distributions, it is the shape of the tensor output by this Op. In that case, at runtime, the value associated with this lvector must have a length equal to the number of dimensions promised by `self.outtype`. In a more general case, the number of output dimensions, len(self.outtype), is equal to len(shape)+self.ndim_added. The special case where len(shape) == 0 means that the smallest shape compatible with the argument's shape will be used. args The values associated with these variables will be passed to the RandomState function during perform as extra "*args"-style arguments. These should be castable to variables of Type TensorType. Returns ------- Apply Apply with two outputs. The first output is a gof.generic Variable from which to draw further random numbers. The second output is the outtype() instance holding the random draw. tndimitint64tint32s;WARNING: RandomState instances should be in RandomStateTypetfile((Rtas_tensor_variabletastypeRRGtAssertionErrorR,R&R tprinttsyststderrtlisttmapR tApplyR9(R trtshapetargstshape_((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt make_nodes *  cC`s|jdd!\}}tt|ddddgdk}|jdkr\| r\|}n tjdgt|jdj D]}||^qgS(NiiRi( tinputsR(RER;Rt ShapeErrorR!RtoutputsRG(R tnodeti_shapesRTtshpt unknown_shapet sample_shpti((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt infer_shapes '  c C`s|\}}|d|d|d}}}t|tjjks\tt||ft|dkrwd}n t|}|dk r|jj t||j krt d|jj t||j fn|j st |}n||d<|j|||g} t| tj sIt| j|jdjjkrntj| d|jdjj} n|dkr| j dkr|jj dkr| jdg|jj } nt| j|jj krt d|jj t| jfn|dk r|j dkrC|| jkrCt d|| jfq|j dkr|| j|j krt d||j | jfqn| |dR&R)R'R,R[ttheanot_asarraytreshapeRU( R R\RYtout_trouttoutRTRURVtrval((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytperformsB  0   %  "%-" cC`s8gt|D]'\}}tjj|||d^q S(Ns1No gradient defined through raw random numbers op(t enumerateRetgradienttgrad_undefined(R RYR[tktinp((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytgrad(scC`sg|D] }d^qS(N(R!(R RYt eval_pointsRa((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytR_op-s(sfnsouttypesinplaces ndim_added(R3R4R5t __props__RR=RCR<R RXRbRlRrRt(((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyR7fs    6  ; cG`sz|rtd|D}nd}t|ttfr#t|}|dkr[|}n+||krtdd|||fng}g}xRt|D]D\}}t|dr|j t |j |q|dkr|j t j ||j |dkq|dkrd} x|D]i} ||| j kr(| d7} || j |} | j| s|j | j| |j t Pqq(q(W| dkrtdq|j t j d|j tqtd |qWt|dkrt jgd d } qt j|} n|dkrt|sDtd ntd |} | j} | j}| j }n\t j |} | j dkrtd| n|dkrt j| }nt g|}| j dkrtd| n| jtj jkrtd| jn||krCtd||f|n|t|ks[t|t j| d t|fS(s Infer the number of dimensions from the shape or the other arguments. Returns ------- (int, variable, tuple) triple, where the variable is an integer vector, and the tuple contains Booleans The first element returned is the inferred number of dimensions. The second element is the shape inferred (combining symbolic and constant informations from shape and args). The third element is a broadcasting pattern corresponding to that shape. cs`s|]}|jVqdS(N(RG(t.0targ((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pys Bsis(ndim should be equal to len(shape), but s&ndim = %s, len(shape) = %s, shape = %sRiisMAuto-shape of -1 must overlapwith the shape of one of the broadcastableinputssnegative shapeR,RHsA_infer_ndim_bcast cannot infer shape without either shape or argscS`s||S(N((RR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytss2shape must be a vector or list of scalar, got '%s's'shape must be an integer vector or lists8ndim should be at least as big as required by args valueN(tmaxR&RcRQR(R!RdRmthasattrtappendRRRKRGt broadcastableRURtconstanttstackRRtget_vector_lengthR,Retinteger_dtypesRMtcast(RGRURVt args_ndimt shape_ndimtbcastt pre_v_shapeRatstn_a_iRta_itv_shapettemplate((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt_infer_ndim_bcast1s                      c G`s|f|}g|D]}dg^q}xPtt|D]<}g|D] }g^qI}tt||}|g}x|D]}||||kr|j|q||dkr|jdg||qtd||||||f|||qWxht|D]Z} xQt|D]C} x:tt|D]&} || j| | | | fq@Wq'WqW|}q<W|S(s Return indices over each shape that broadcast them to match out_shape. The first returned list is equivalent to numpy.ndindex(out_shape), the other returned lists are indices corresponding to the other shapes, such that looping over these indices produce tensors of shape out_shape. In particular, the indices over broadcasted dimensions should all be 0. The shapes should have the same length as out_shape. If they are longer, the right-most dimensions are ignored. iis<shape[%i] (%i) should be equal to out_shape[%i] (%i) or to 1((RR(RQtrangeR{Rdtzip( t out_shapetshapest all_shapesRUt ret_indicestdimt _ret_indicest out_rangetrangest prev_indext dim_indexRa((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyt_generate_broadcasting_indicess*   , gg?cC`stj|}tj|}|dkrQtjjtjj|j|j}nt ||||\}}}t dtj d|d|}|||||S(s Sample from a uniform distribution between low and high. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. If size is None, the output shape will be determined by the shapes of low and high. If dtype is not specified, it will be inferred from the dtype of low and high, but will be at least as precise as floatX. tuniformR,R|N( RRKR!tscaltupcastRetconfigtfloatXR,RR7t TensorType(t random_stateR*tlowthighRGR,Rtop((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs 'cC`stj|}tj|}|dkrQtjjtjj|j|j}nt ||||\}}}t dtj d|d|}|||||S(s Sample from a normal distribution centered on avg with the specified standard deviation (std). If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. If size is None, the output shape will be determined by the shapes of avg and std. If dtype is not specified, it will be inferred from the dtype of avg and std, but will be at least as precise as floatX. tnormalR,R|N( RRKR!RRReRRR,RR7R(RR*tavgtstdRGR,RR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs 'g?RHc C`s$|dk r(|}tddtjntj|}tj|}t||||\}}}|jdkryMtj j dtj dddgddd tj d d d gdd Wqt k rtj |d}qXntdtjd|dtf|}|||||S(sT Sample n times with probability of success prob for each trial, return the number of successes. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. If size is None, the output shape will be determined by the shapes of n and prob. sqDEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy.RJRHtniiiR,tpg?g?g333333?tfloat64RItbinomialR|N(R!RNRORPRRKRR,RRRtasarrayRRR7RR( RR*RRRGR,tprobRR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs M c C`s|dk rt|}nt|j|j}|j|kratd|j|f||n|j|kr|jd||j|j}n|j|krtd|j|f||n|j|kr|jd||j|j}n|dk rt|}nGd }x>t|D]0}t|j||j|}||f}q&Wt j |}t ||j|j} xEt | D]7\} } } |j d|| d|| d|| = 0. size: int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. dtype The dtype of the return value (which will represent counts). size or ndim must be given. tpoissonR,R|(RRKRR7R(RR*tlamRGR,RR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs  cC`s|jdkstt|j}|dkr<d}nt|}|j|tj|t}x*tj |D]}|j |||t|D]0}t|j||j|}||f}q:W||jdf}t j |}t ||j|jd } |j dkst xt| D]\} } } || } t j| }d|kodknr2| d} t j| }n,| ddkr^| d} t j| }n|dksvt ||jd || d | jd || g-C6 ?RtpvalsRN(i(i(grZ| ?gSt$?gSt$?(R!R(RyRGRdRgRURcRRR)RtminRMRtsumt multinomialRL(RRRR*RGRRRRjRtmitnitpitpvitpisum((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytmultinomial_helpersN $(     c C`stj|}tj|}|jdj}t||||\}}}||jjdf}tttjd|d|dd}|||||S(s Sample from one or more multinomial distributions defined by one-dimensional slices in pvals. Parameters ---------- pvals A tensor of shape "nmulti+(L,)" describing each multinomial distribution. This tensor must have the property that numpy.allclose(pvals.sum(axis=-1), 1) is true. size A vector of shape information for the output; this can also specify the "nmulti" part of pvals' shape. A -1 in the k'th position from the right means to borrow the k'th position from the right in nmulti. (See examples below.) Default ``None`` means size=nmulti. n The number of experiments to simulate for each multinomial. This can be a scalar, or tensor, it will be broadcasted to have shape "nmulti". dtype The dtype of the return value (which will represent counts) Returns ------- tensor Tensor of len(size)+1 dimensions, and shape[-1]==L, with the specified ``dtype``, with the experiment counts. See examples to understand the shape of the return value, which is derived from both size and pvals.shape. In return value rval, "numpy.allclose(rval.sum(axis=-1), n)" will be true. Extended Summary ---------------- For example, to simulate n experiments from each multinomial in a batch of size B: size=None, pvals.shape=(B,L) --> rval.shape=[B,L] rval[i,j] is the count of possibility j in the i'th distribution (row) in pvals. Using size: size=(1,-1), pvals.shape=(A,B,L) --> rval.shape=[1,B,L], and requires that A==1. rval[k,i,j] is the count of possibility j in the distribution specified by pvals[k,i]. Using size for broadcasting of pvals: size=(10, 1, -1), pvals.shape=(A, B, L) --> rval.shape=[10,1,B,L], and requires that A==1. rval[l,k,i,j] is the count of possibility j in the distribution specified by pvals[k,i], in the l'th of 10 draws. iiR,R|R;i( RRKtTRRR|R7RR( RR*RRRGR,ttmpRR((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRBs>  cC`sl|j}t|trh|j rh|j\}}}}t||dtd|}|j|jjSt S(NR:R;( RR&R7R:t_propsRRXRYR[R(R\Rtop_fnt op_outtypet op_inplacet op_ndim_addedtnew_op((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytrandom_make_inplaces  Rtignore_newtreesictfast_runR:tRandomStreamsBasecB`seZdddddddZddddddZddddddZdddddd Zdd edddd Zddddd Z ddddd Z ddddgdddZ dZ RS(ig?RHc C`sJ|dk r(|}tddtjn|jt|||d|d|S(s  Sample n times with probability of success p for each trial and return the number of successes. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. sqDEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy.RJRGR,N(R!RNRORPtgenR(R R*RRRGR,R((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs gg?c C`s"|jt|||d|d|S(s  Sample a tensor of given size whose element from a uniform distribution between low and high. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. RGR,(RR(R R*RRRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs c C`s"|jt|||d|d|S(s  Sample from a normal distribution centered on avg with the specified standard deviation (std). If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. RGR,(RR(R R*RRRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs ic C`s"|jt|||d|d|S(s Sample a random integer between low and high, both inclusive. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. RGR,(RR(R R*RRRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs ic C`s%|jt||||d|d|S(s[ Choose values from `a` with or without replacement. `a` can be a 1-D array or a positive scalar. If `a` is a scalar, the samples are drawn from the range 0,...,a-1. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. RGR,(RR(R R*RRRRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs cC`s|jt||d|d|S(s0 Draw samples from a Poisson distribution. The Poisson distribution is the limit of the Binomial distribution for large N. If the size argument is ambiguous on the number of dimensions, ndim may be a plain integer to supplement the missing information. RGR,(RR(R R*RRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs cC`s|jt||d|d|S(s> Return permutations of the integers between 0 and n-1. Returns them as many times as required by size. For instance, if size=(p,q), p*q permutations will be generated, and the output shape will be (p,q,n), because each permutation is of size n. Theano tries to infer the number of dimensions from the length of the size argument and the shape of n, but you may always specify it with the `ndim` parameter. Notes ----- Note that the output will then be of dimension ndim+1. RGR,(RR(R R*RRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRsc C`s"|jt|||d|d|S(s$ Sample n times from a multinomial distribution defined by probabilities pvals, as many times as required by size. For instance, if size=(p,q), p*q samples will be drawn, and the output shape will be (p,q,len(pvals)). Theano tries to infer the number of dimensions from the length of the size argument and the shapes of n and pvals, but you may always specify it with the `ndim` parameter. Notes ----- Note that the output will then be of dimension ndim+1. RGR,(RR(R R*RRRGR,((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRscC`sR|jd|jd d|jdd|jd}tj||}||_|S(s Return a variable with every row (rightmost index) shuffled. This uses permutation random variable internally, available via the ``.permutation`` attribute of the return value. R*iRRGi(RRURGRtpermute_row_elements(R tinputtpermtshuffled((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytshuffle_row_elementss # N( R3R4R!RRRRRRRRRR(((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pyRs       (2R5t __future__RRRRORRtsixRt six.movesRRReRt theano.tensorRR ttheano.compileR t __docformat__tTypeR tcompiletregister_view_op_c_codetrandom_state_typetOpR7RRR!RRRRRRRRRRRRRtlocal_optimizerRtregistertin2outtobjectR(((s8/tmp/pip-build-X4mzal/theano/theano/tensor/raw_random.pytsT   E   r /  2     $" IJ