├── main.pdf ├── .gitignore ├── main.py ├── GaussianProcessMultiDim.py ├── README.md ├── cymath.pyx ├── testdata1d_000.txt ├── testdata1d_001.txt ├── testdata1d_002.txt ├── testdata1d_003.txt ├── testdata1d_004.txt ├── GaussianProcess.pyx ├── testdata2d_003.txt ├── testdata2d_004.txt ├── testdata2d_000.txt ├── testdata2d_001.txt ├── testdata2d_002.txt ├── GPSegmentation.py └── LICENSE /main.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/naka-lab/GP-HSMM/HEAD/main.pdf -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | GaussianProcess\.so 3 | 4 | \.DS_Store 5 | 6 | *.pyc 7 | 8 | local_data/ 9 | 10 | learn/ 11 | 12 | recog/ 13 | 14 | *.so 15 | 16 | *.pyd 17 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import unicode_literals 3 | from __future__ import print_function 4 | from GPSegmentation import GPSegmentation 5 | import time 6 | 7 | def learn( savedir ): 8 | gpsegm = GPSegmentation(2,5) 9 | 10 | files = [ "testdata2d_%03d.txt" % j for j in range(4) ] 11 | gpsegm.load_data( files ) 12 | 13 | start = time.time() 14 | for it in range(5): 15 | print( "-----", it, "-----" ) 16 | gpsegm.learn() 17 | gpsegm.save_model( savedir ) 18 | print( "lik =", gpsegm.calc_lik() ) 19 | print( time.time()-start ) 20 | return gpsegm.calc_lik() 21 | 22 | 23 | def recog( modeldir, savedir ): 24 | gpsegm = GPSegmentation(2,5) 25 | 26 | gpsegm.load_data( [ "testdata2d_%03d.txt" % j for j in range(4) ] ) 27 | gpsegm.load_model( modeldir ) 28 | 29 | 30 | start = time.time() 31 | gpsegm.recog() 32 | print( "lik =", gpsegm.calc_lik() ) 33 | print( time.time()-start ) 34 | gpsegm.save_model( savedir ) 35 | 36 | 37 | def main(): 38 | learn( "learn/" ) 39 | recog( "learn/" , "recog/" ) 40 | return 41 | 42 | if __name__=="__main__": 43 | main() 44 | -------------------------------------------------------------------------------- /GaussianProcessMultiDim.py: -------------------------------------------------------------------------------- 1 | # encoding: utf8 2 | from __future__ import unicode_literals 3 | from __future__ import print_function 4 | import pyximport 5 | import numpy as np 6 | pyximport.install(setup_args={'include_dirs':[np.get_include()]}, inplace=True) 7 | import GaussianProcess 8 | import matplotlib.pyplot as plt 9 | 10 | 11 | class GPMD: 12 | def __init__(self, dim): 13 | self.__dim = dim 14 | self.__gp = [ GaussianProcess.GP() for d in range(self.__dim) ] 15 | 16 | def learn(self,x, y, same_cov=True ): 17 | y = np.array(y, dtype=float).reshape( (-1,self.__dim) ) 18 | x = np.array(x,dtype=float) 19 | i_cov = None 20 | 21 | for d in range(self.__dim): 22 | if not same_cov: 23 | i_cov = None 24 | 25 | if len(y)!=0: 26 | i_cov = self.__gp[d].learn( x, y[:,d], i_cov ) 27 | else: 28 | i_cov = self.__gp[d].learn( x, np.array([]), i_cov ) 29 | 30 | 31 | 32 | 33 | def calc_lik(self, x, y ): 34 | lik = 0.0 35 | 36 | if self.__dim==1: 37 | y = np.asarray(y, dtype=float).reshape( (-1,self.__dim) ) 38 | #x = np.asarray(x,dtype=np.float) 39 | for d in range(self.__dim): 40 | lik += self.__gp[d].calc_lik( x , y[:,d] ) 41 | 42 | return lik 43 | 44 | def plot(self, x ): 45 | for d in range(self.__dim): 46 | plt.subplot( self.__dim, 1, d+1 ) 47 | 48 | mus, sigmas = self.__gp[d].predict(x) 49 | y_min = mus - sigmas*2 50 | y_max = mus + sigmas*2 51 | 52 | plt.fill_between( x, y_min, y_max, facecolor="lavender" , alpha=0.9 , edgecolor="lavender" ) 53 | plt.plot(x, y_min, 'b--') 54 | plt.plot(x, mus, 'b-') 55 | plt.plot(x, y_max, 'b--') 56 | 57 | def predict(self, x ): 58 | params = [] 59 | for d in range(self.__dim): 60 | mus, sigmas = self.__gp[d].predict(np.array(x, dtype=float)) 61 | params.append( (mus, sigmas) ) 62 | return params 63 | 64 | def estimate_hyperparams(self, niter): 65 | for d in range(self.__dim): 66 | self.__gp[d].estimate_hyperparams(niter) 67 | 68 | 69 | def main(): 70 | pass 71 | 72 | if __name__ == '__main__': 73 | main() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GP-HSMM 2 | 3 | This is an implementation of time series data segmentation using Gaussian Processes (GP) and Hidden Semi-Markov Models (HSMM). For details, please refer to the following paper: 4 | 5 | Tomoaki Nakamura, Takayuki Nagai, Daichi Mochihashi, Ichiro Kobayashi, Hideki Asoh and Masahide Kaneko, “Segmenting Continuous Motions with Hidden Semi-Markov Models and Gaussian Processes”, Frontiers in Neurorobotics, vol.11, article 67, pp. 1-11, Dec. 2017 [[PDF]](https://github.com/naka-lab/GP-HSMM/raw/master/main.pdf) 6 | 7 | **A fast and scalable implementation called [RFF-GP-HSMM](https://github.com/naka-lab/RFF-GP-HSMM), which solves the slow computation problem of GP-HSMM, is also available.** 8 | 9 | 10 | ## How to Run 11 | 12 | ``` 13 | python main.py 14 | ``` 15 | 16 | Programs written in Cython will be automatically compiled at runtime. 17 | If you encounter compilation errors with the Visual Studio compiler on Windows, please edit: 18 | 19 | ``` 20 | (Python installation directory)/Lib/distutils/msvc9compiler.py 21 | ``` 22 | 23 | Inside the `get_build_version()` function, replace the following line: 24 | 25 | ``` 26 | majorVersion = int(s[:-2]) - 6 27 | ``` 28 | 29 | with the version number of the Visual Studio you wish to use. 30 | For example, for VS2012, set: 31 | 32 | ``` 33 | majorVersion = 11 34 | ``` 35 | 36 | ## Output Files 37 | 38 | When executed, the following files and directories will be created in the specified folder: 39 | 40 | | File Name| Description | 41 | | ---- | --- | 42 | | class{c}.npy | A collection of segments classified into class c | 43 | | class{c}\_dim{d}.png | Plot of the d-th dimension of segments classified into class c | 44 | | segm{n}.txt | Segmentation result of the n-th sequence. Column 1: segment class, Column 2: flag indicating segment boundary | 45 | | trans\_bos.npy | Probability that each class appears at the beginning of a sequence | 46 | | trans\_eos.npy | Probability that each class appears at the end of a sequence | 47 | | trans.npy | Transition probabilities of each class appearing after a given class | 48 | 49 | 50 | # LICENSE 51 | This program is freely available for free non-commercial use. 52 | If you publish results obtained using this program, please cite: 53 | 54 | ``` 55 | @article{nakamura2017segmenting, 56 | title={Segmenting continuous motions with hidden semi-markov models and gaussian processes}, 57 | author={Nakamura, Tomoaki and Nagai, Takayuki and Mochihashi, Daichi and Kobayashi, Ichiro and Asoh, Hideki and Kaneko, Masahide}, 58 | journal={Frontiers in neurorobotics}, 59 | volume={11}, 60 | pages={67}, 61 | year={2017}, 62 | publisher={Frontiers} 63 | } 64 | ``` 65 | -------------------------------------------------------------------------------- /cymath.pyx: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | from __future__ import unicode_literals 4 | import numpy as np 5 | import random 6 | import matplotlib.mlab as mlab 7 | import sys 8 | import math 9 | 10 | cdef extern from "math.h": 11 | double log(double) 12 | double exp(double) 13 | 14 | 15 | cpdef logsumexp( double[:,:] a ): 16 | cdef double max_val = -sys.float_info.max 17 | cdef double sum_exp = 0 18 | cdef int I = a.shape[0] 19 | cdef int J = a.shape[1] 20 | 21 | for i in range(I): 22 | for j in range(J): 23 | if max_valb: 36 | max_val = a 37 | else: 38 | max_val = b 39 | 40 | sum_exp = exp(a-max_val)+ exp(b-max_val) 41 | return log(sum_exp) + max_val 42 | 43 | cpdef calc_forward_probability(double[:,:,:] emission_prob_all, double[:,:] trans_prob, double[:] trans_prob_bos, double[:] trans_prob_eos, int T, int MIN_LEN, int SKIP_LEN, int MAX_LEN, int num_class): 44 | cdef int t, k, c, tt, kk, cc 45 | cdef double foward_prob 46 | cdef double[:,:,:] log_a = np.zeros( (T, MAX_LEN, num_class) ) - 99999999999999 47 | cdef double[:,:,:] valid = np.zeros( (T, MAX_LEN, num_class) ) # 計算された有効な値可どうか．計算されていない場所の確率を0にするため． 48 | cdef double[:] z = np.ones( T ) # 正規化定数 49 | cdef double[:,:] m = np.zeros( (T, num_class) ) # t-kの計算結果を入れるバッファ 50 | 51 | for t in range(T): 52 | for k in range(MIN_LEN, MAX_LEN, SKIP_LEN): 53 | if t-k<0: 54 | break 55 | 56 | for c in range(num_class): 57 | out_prob = emission_prob_all[c,k,t-k] 58 | foward_prob = 0.0 59 | 60 | # 遷移確率 61 | tt = t-k-1 62 | if tt>=0: 63 | #if m[tt]==0: 64 | # m[tt] = logsumexp( log_a[tt,:,:] + z[tt] + np.log(trans_prob[:,c]) ) 65 | #foward_prob = m[tt] + out_prob 66 | 67 | if m[tt,c]==0: 68 | s = -999999999999 69 | for kk in range(MAX_LEN): 70 | for cc in range(num_class): 71 | s = _logsumexp( s, log_a[tt,kk,cc] + z[tt] + log(trans_prob[cc, c])) 72 | m[tt,c] = s 73 | foward_prob = m[tt,c] + out_prob 74 | else: 75 | # 最初の単語 76 | foward_prob = out_prob + log(trans_prob_bos[c]) 77 | 78 | if t==T-1: 79 | # 最後の単語 80 | foward_prob += log(trans_prob_eos[c]) 81 | 82 | # 正規化を元に戻す 83 | log_a[t,k,c] = foward_prob 84 | valid[t,k,c] = 1.0 85 | if math.isnan(foward_prob): 86 | print( "a[t=%d,k=%d,c=%d] became NAN!!" % (t,k,c) ) 87 | sys.exit(-1) 88 | # 正規化 89 | if t-MIN_LEN>=0: 90 | z[t] = logsumexp( log_a[t,:,:] ) 91 | #log_a[t,:,:] -= z[t] 92 | for k in range(MAX_LEN): 93 | for c in range(num_class): 94 | log_a[t, k, c] -= z[t] 95 | 96 | return np.exp(log_a)*valid 97 | -------------------------------------------------------------------------------- /testdata1d_000.txt: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 1.000000000000000000e+00 3 | 1.000000000000000000e+00 4 | 1.000000000000000000e+00 5 | 1.000000000000000000e+00 6 | 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-------------------------------------------------------------------------------- /GaussianProcess.pyx: -------------------------------------------------------------------------------- 1 | # encoding: utf8 2 | from __future__ import unicode_literals 3 | import numpy as np 4 | import random 5 | import matplotlib.mlab as mlab 6 | import cython 7 | import math 8 | 9 | 10 | cdef extern from "math.h": 11 | double exp(double) 12 | double sqrt(double) 13 | double log(double) 14 | 15 | 16 | cdef class GP: 17 | cdef int ns 18 | cdef double[:] xt, yt 19 | cdef double[:,:] i_cov 20 | cdef double[:] param 21 | cdef dict param_cache 22 | 23 | cdef double beta 24 | cdef double theta0 25 | cdef double theta1 26 | cdef double theta2 27 | cdef double theta3 28 | 29 | cdef double covariance_func(self, double xi, double xj): 30 | return self.theta0 * exp(-0.5 * self.theta1 * (xi - xj) * (xi - xj)) + self.theta2 + self.theta3 * xi * xj 31 | 32 | cdef double normpdf(self, double x, double mu, double sigma): 33 | return 1./(sqrt(2*np.pi)*sigma)*exp(-0.5 * ((x - mu)/sigma)**2) 34 | 35 | def __init__( self ): 36 | self.param_cache = {} 37 | 38 | self.beta = 10.0 39 | self.theta0 = 1.0 40 | self.theta1 = 1.0 41 | self.theta2 = 0 42 | self.theta3 = 16.0 43 | 44 | 45 | cpdef learn(self, double[:] xt, double[:] yt, double[:,:] i_cov=None ): 46 | cdef int i,j 47 | cdef double c 48 | self.xt = np.array( xt ) 49 | self.yt = np.array( yt ) 50 | self.ns = len(xt) 51 | cdef double[:,:] cov = np.zeros((self.ns, self.ns)) 52 | 53 | if i_cov==None: 54 | for i in range(self.ns): 55 | for j in range(i+1): 56 | c = self.covariance_func(xt[i], xt[j]) 57 | cov[i,j] = c 58 | cov[j,i] = c 59 | if i==j: 60 | cov[i,j] += 1/self.beta 61 | 62 | 63 | #self.i_cov = np.linalg.inv(cov) 64 | # 高速化：方程式 cov * x = e の解は x = i_cov * e = i_cov になることを利用 65 | self.i_cov = np.linalg.solve(cov, np.identity(self.ns)) 66 | else: 67 | self.i_cov = i_cov 68 | self.param = np.dot(self.i_cov, self.yt) 69 | 70 | self.param_cache.clear() 71 | 72 | return self.i_cov 73 | 74 | 75 | cpdef predict( self, double[:] x ): 76 | cdef int k, i 77 | cdef double mu, sigma, c 78 | cdef int N = x.shape[0] 79 | cdef double[:] v 80 | cdef double[:] tt = self.yt #[y - np.random.normal() / self.beta for y in self.yt] 81 | cdef double[:] mus = np.zeros( N ) 82 | cdef double[:] sigmas = np.zeros(N) 83 | 84 | for k in range(N): 85 | v = np.zeros((self.ns)) 86 | for i in range(self.ns): 87 | v[i] = self.covariance_func(x[k], self.xt[i]) 88 | c = self.covariance_func(x[k], x[k]) + 1.0 / self.beta 89 | 90 | mus[k] = np.dot(v, np.dot(self.i_cov, tt)) 91 | sigmas[k] = c - np.dot(v, np.dot(self.i_cov, v)) 92 | 93 | return mus, sigmas 94 | 95 | cpdef double calc_lik( self, double[:] xs, double[:] ys ): 96 | cdef int k,i 97 | cdef int n = len(xs) 98 | cdef double lik = 0 99 | cdef int ns = self.ns 100 | cdef double c,p,mu,sigma 101 | cdef double[:] v= np.zeros((ns)) 102 | 103 | for k in range(n): 104 | # 計算結果をキャッシュして使い回す 105 | if xs[k] in self.param_cache: 106 | mu, sigma = self.param_cache[ xs[k] ] 107 | else: 108 | v = np.zeros((ns)) 109 | for i in range(ns): 110 | v[i] = self.covariance_func(xs[k], self.xt[i]) 111 | c = self.covariance_func(xs[k], xs[k]) + 1.0 / self.beta 112 | mu = np.dot(v, self.param) 113 | sigma = c - np.dot(v, np.dot(self.i_cov, v)) 114 | 115 | self.param_cache[ xs[k] ] = (mu, sigma) 116 | 117 | p = self.normpdf( ys[k] , mu, sigma ) 118 | if p<=0: 119 | p = 0.000000000001 120 | lik += log( p ) 121 | 122 | return lik 123 | 124 | cpdef estimate_hyperparams(self, int niter, double step=0.2): 125 | cdef int itr, d 126 | cdef double init_lik = self.calc_lik( self.xt, self.yt ) 127 | cdef double max_lik = init_lik 128 | cdef double old_lik = init_lik 129 | 130 | max_params = [self.beta, self.theta0, self.theta1, self.theta2, self.theta3] 131 | new_params = [self.beta, self.theta0, self.theta1, self.theta2, self.theta3] 132 | 133 | for itr in range(niter): 134 | 135 | for d in range(5): 136 | old_params = [self.beta, self.theta0, self.theta1, self.theta2, self.theta3] 137 | 138 | # 対数空間でランダムに動かす 139 | # p' = exp( log(p) + rand ) 140 | new_params[d] = old_params[d] * exp (step * random.gauss(0,1)) 141 | 142 | # 新しいパラメータで学習・尤度計算 143 | self.beta, self.theta0, self.theta1, self.theta2, self.theta3 = new_params 144 | self.learn(self.xt, self.yt) 145 | new_lik = self.calc_lik(self.xt, self.yt) 146 | 147 | 148 | # accept or reject 149 | if math.exp(new_lik-old_lik)>random.random(): 150 | # acceptの場合は更新 151 | old_lik = new_lik 152 | else: 153 | # rejectの場合は元に戻す 154 | self.beta, self.theta0, self.theta1, self.theta2, self.theta3 = old_params 155 | 156 | # 最大のものを保存する 157 | if max_lik", max_lik) 163 | return 164 | 165 | 166 | 167 | 168 | if __name__=='__main__': 169 | pass -------------------------------------------------------------------------------- /testdata2d_003.txt: -------------------------------------------------------------------------------- 1 | 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from __future__ import print_function 4 | import GaussianProcessMultiDim 5 | import random 6 | import math 7 | import matplotlib.pyplot as plt 8 | import time 9 | import numpy as np 10 | import sys 11 | import os 12 | #from scipy.special import logsumexp 13 | import pyximport 14 | pyximport.install(setup_args={'include_dirs':[np.get_include()]}, inplace=True) 15 | from cymath import logsumexp, calc_forward_probability 16 | 17 | 18 | class GPSegmentation(): 19 | # parameters 20 | MAX_LEN = 20 21 | MIN_LEN = 5 22 | AVE_LEN = 10 23 | SKIP_LEN = 1 24 | 25 | def __init__(self, dim, nclass): 26 | self.dim = dim 27 | self.numclass = nclass 28 | self.segmlen = 3 29 | self.gps = [ GaussianProcessMultiDim.GPMD(dim) for i in range(self.numclass) ] 30 | self.segm_in_class= [ [] for i in range(self.numclass) ] 31 | self.segmclass = {} 32 | self.segments = [] 33 | self.trans_prob = np.ones( (nclass,nclass) ) 34 | self.trans_prob_bos = np.ones( nclass ) 35 | self.trans_prob_eos = np.ones( nclass ) 36 | self.is_initialized = False 37 | 38 | self.prior_table = [ i*math.log(self.AVE_LEN) -self.AVE_LEN - sum(np.log(np.arange(1,i+1))) for i in range(1,self.MAX_LEN+1) ] 39 | 40 | def load_data(self, filenames, classfile=None ): 41 | self.data = [] 42 | self.segments = [] 43 | self.is_initialized = False 44 | 45 | for n, fname in enumerate(filenames): 46 | y = np.loadtxt( fname ) 47 | segm = [] 48 | self.data.append( y ) 49 | 50 | # ランダムに切る 51 | i = 0 52 | while i=len(y): 56 | length = len(y)-i 57 | 58 | segm.append( y[i:i+length+1] ) 59 | 60 | i+=length 61 | 62 | self.segments.append( segm ) 63 | 64 | # ランダムに割り振る 65 | for i,s in enumerate(segm): 66 | c = random.randint(0,self.numclass-1) 67 | self.segmclass[(n,i)] = c 68 | 69 | # 遷移確率更新 70 | self.calc_trans_prob() 71 | 72 | 73 | 74 | 75 | def load_model( self, basename ): 76 | # GP読み込み 77 | for c in range(self.numclass): 78 | filename = basename + "class%03d.npy" % c 79 | self.segm_in_class[c] = np.load( filename, allow_pickle=True) 80 | self.update_gp( c ) 81 | 82 | # 遷移確率更新 83 | self.trans_prob = np.load( basename+"trans.npy", allow_pickle=True ) 84 | self.trans_prob_bos = np.load( basename+"trans_bos.npy", allow_pickle=True ) 85 | self.trans_prob_eos = np.load( basename+"trans_eos.npy", allow_pickle=True ) 86 | 87 | 88 | def update_gp(self, c ): 89 | datay = [] 90 | datax = [] 91 | for s in self.segm_in_class[c]: 92 | datay += [ y for y in s ] 93 | datax += range(len(s)) 94 | 95 | self.gps[c].learn( datax, datay ) 96 | 97 | 98 | def calc_emission_logprob( self, c, segm ): 99 | gp = self.gps[c] 100 | slen = len(segm) 101 | 102 | if len(segm) > 2: 103 | #plen = self.AVE_LEN**slen * math.exp(-self.AVE_LEN) / math.factorial(slen) 104 | log_plen = (slen*math.log(self.AVE_LEN) + (-self.AVE_LEN)*math.log(math.e)) - (sum(np.log(np.arange(1,slen+1)))) 105 | p = gp.calc_lik( np.arange(len(segm), dtype=float) , segm ) 106 | #return p + math.log(plen) 107 | return p + log_plen 108 | else: 109 | return math.log(1.0e-100) 110 | 111 | def calc_emission_logprob_all(self, d): 112 | T = len(d) 113 | # 出力確率を計算 114 | emission_prob_all = np.zeros( ( self.numclass, self.MAX_LEN, len(d)) ) 115 | for c in range(self.numclass): 116 | params = self.gps[c].predict( range(self.MAX_LEN) ) 117 | for k in range(self.MAX_LEN): 118 | for dim in range( self.dim ): 119 | mu = params[dim][0][k] 120 | sig = params[dim][1][k] 121 | emission_prob_all[c, k, 0:T-k] += -math.log(math.sqrt( 2*math.pi*sig**2)) - (d[k:,dim]-mu)**2 / (2*sig**2) 122 | 123 | # 累積確率にする 124 | for k in range(1, self.MAX_LEN): 125 | emission_prob_all[:, k, :] += emission_prob_all[:, k-1, :] 126 | 127 | for k in range(self.MAX_LEN): 128 | emission_prob_all[:,k,:] += self.prior_table[k] 129 | 130 | return emission_prob_all 131 | 132 | 133 | def save_model(self, basename ): 134 | if not os.path.exists(basename): 135 | os.mkdir( basename ) 136 | 137 | for n,segm in enumerate(self.segments): 138 | classes = [] 139 | cut_points = [] 140 | for i, s in enumerate(segm): 141 | c = self.segmclass[(n,i)] 142 | classes += [ c for j in range(len(s)) ] 143 | cut_points += [0] * len(s) 144 | cut_points[-1] = 1 145 | np.savetxt( basename+"segm%03d.txt" % n, np.vstack([classes,cut_points]).T, fmt=str("%d") ) 146 | 147 | 148 | # 各クラスに分類されたデータを保存 149 | for c in range(len(self.gps)): 150 | for d in range(self.dim): 151 | plt.clf() 152 | for data in self.segm_in_class[c]: 153 | if self.dim==1: 154 | plt.plot( range(len(data)), data, "o-" ) 155 | else: 156 | plt.plot( range(len(data[:,d])), data[:,d], "o-" ) 157 | plt.ylim( -1, 1 ) 158 | plt.savefig( basename+"class%03d_dim%03d.png" % (c, d) ) 159 | 160 | # テキストでも保存 161 | np.save( basename + "trans.npy" , self.trans_prob ) 162 | np.save( basename + "trans_bos.npy" , self.trans_prob_bos ) 163 | np.save( basename + "trans_eos.npy" , self.trans_prob_eos ) 164 | 165 | for c in range(self.numclass): 166 | np.save( basename+"class%03d.npy" % c, np.array(self.segm_in_class[c], dtype=object) ) 167 | 168 | def calc_vitervi_path(self, d ): 169 | T = len(d) 170 | log_a = np.zeros( (len(d), self.MAX_LEN, self.numclass) ) - 99999999999999 171 | valid = np.zeros( (len(d), self.MAX_LEN, self.numclass) ) # 計算された有効な値可どうか．計算されていない場所の確率を0にするため． 172 | z = np.ones( T ) # 正規化定数 173 | path_kc = -np.ones( (len(d), self.MAX_LEN, self.numclass, 2), dtype=np.int32 ) 174 | emission_prob_all = self.calc_emission_logprob_all( d ) 175 | 176 | 177 | # 前向き確率計算 178 | for t in range(T): 179 | for k in range(self.MIN_LEN,self.MAX_LEN,self.SKIP_LEN): 180 | if t-k<0: 181 | break 182 | 183 | segm = d[t-k:t+1] 184 | for c in range(self.numclass): 185 | #out_prob = self.calc_emission_logprob( c, segm ) 186 | out_prob = emission_prob_all[c,k,t-k] 187 | foward_prob = 0.0 188 | 189 | # 遷移確率 190 | tt = t-k-1 191 | if tt>=0: 192 | prev_prob = log_a[tt,:,:] + z[tt] + np.log(self.trans_prob[:,c]) 193 | 194 | # 最大値を取る 195 | idx = np.argmax( prev_prob.reshape( self.MAX_LEN*self.numclass )) 196 | kk = int(idx/self.numclass) 197 | cc = idx % self.numclass 198 | 199 | path_kc[t, k, c, 0] = kk 200 | path_kc[t, k, c, 1] = cc 201 | 202 | foward_prob = prev_prob[kk, cc] + out_prob 203 | else: 204 | # 最初の単語 205 | foward_prob = out_prob + math.log(self.trans_prob_bos[c]) 206 | 207 | path_kc[t, k, c, 0] = t+1 208 | path_kc[t, k, c, 1] = -1 209 | 210 | 211 | if t==T-1: 212 | # 最後の単語 213 | foward_prob += math.log(self.trans_prob_eos[c]) 214 | 215 | 216 | log_a[t,k,c] = foward_prob 217 | valid[t,k,c] = 1.0 218 | if math.isnan(foward_prob): 219 | print( "a[t=%d,k=%d,c=%d] became NAN!!" % (t,k,c) ) 220 | sys.exit(-1) 221 | # 正規化 222 | if t-self.MIN_LEN>=0: 223 | z[t] = logsumexp( log_a[t,:,:] ) 224 | log_a[t,:,:] -= z[t] 225 | #z[t] = logsumexp( a[t,:,:] ) 226 | #a[t,:,:] -= z[t] 227 | 228 | # バックトラック 229 | t = T-1 230 | idx = np.argmax( log_a[t].reshape( self.MAX_LEN*self.numclass )) 231 | k = int(idx/self.numclass) 232 | c = idx % self.numclass 233 | 234 | segm = [ d[t-k:t+1] ] 235 | segm_class = [ c ] 236 | 237 | while True: 238 | kk, cc = path_kc[t, k, c] 239 | 240 | t = t-k-1 241 | k = kk 242 | c = cc 243 | 244 | if t<=0: 245 | break 246 | 247 | if t-k-1<=0: 248 | #先頭 249 | s = d[0:t+1] 250 | else: 251 | #先頭以外 252 | s = d[t-k:t+1] 253 | 254 | segm.insert( 0, s ) 255 | segm_class.insert( 0, c ) 256 | 257 | return segm, segm_class 258 | 259 | 260 | 261 | def forward_filtering(self, d ): 262 | #### cythonで処理 ####### 263 | # 全時刻の出力確率をあらかじめ計算 264 | emission_prob_all = self.calc_emission_logprob_all( d ) 265 | forward_prob = calc_forward_probability( emission_prob_all, self.trans_prob, self.trans_prob_bos, self.trans_prob_eos, len(d), self.MIN_LEN, self.SKIP_LEN, self.MAX_LEN, self.numclass ) 266 | return forward_prob 267 | 268 | """ 269 | ### Pythonで計算 ######## 270 | T = len(d) 271 | log_a = np.log( np.zeros( (len(d), self.MAX_LEN, self.numclass) ) + 1.0e-100 ) # 前向き確率．対数で確率を保持．1.0e-100で確率0を近似的に表現． 272 | #a = np.zeros( (len(d), self.MAX_LEN, self.numclass) ) + 1.0-e100 273 | valid = np.zeros( (len(d), self.MAX_LEN, self.numclass) ) # 計算された有効な値可どうか．計算されていない場所の確率を0にするため． 274 | z = np.ones( T ) # 正規化定数 275 | m = np.zeros( len(d) ) # t-kの計算結果を入れるバッファ 276 | 277 | # 全時刻の出力確率をあらかじめ計算 278 | emission_prob_all = self.calc_emission_logprob_all( d ) 279 | 280 | for t in range(T): 281 | for k in range(self.MIN_LEN,self.MAX_LEN,self.SKIP_LEN): 282 | if t-k<0: 283 | break 284 | 285 | #segm = d[t-k:t+1] 286 | for c in range(self.numclass): 287 | #out_prob = self.calc_emission_logprob( c, segm ) 288 | out_prob = emission_prob_all[c,k-1,t-k] 289 | foward_prob = 0.0 290 | 291 | # 遷移確率 292 | tt = t-k-1 293 | if tt>=0: 294 | #for kk in range(self.MAX_LEN): 295 | # for cc in range(self.numclass): 296 | # foward_prob += a[tt,kk,cc] * self.trans_prob[cc, c] 297 | #foward_prob = math.log(np.sum( a[tt,:,:] * self.trans_prob[:,c] )) + out_prob 298 | 299 | 300 | if m[tt]==0: 301 | #m[tt] = torch.logsumexp( torch.Tensor(log_a[tt,:,:] + z[tt] + np.log(self.trans_prob[:,c])).flatten(), -1 ) 302 | m[tt] = logsumexp( log_a[tt,:,:] + z[tt] + np.log(self.trans_prob[:,c]) ) 303 | foward_prob = m[tt] + out_prob 304 | #foward_prob = logsumexp( log_a[tt,:,:] + z[tt] + np.log(self.trans_prob[:,c]) ) + out_prob 305 | 306 | 307 | else: 308 | # 最初の単語 309 | foward_prob = out_prob + math.log(self.trans_prob_bos[c]) 310 | 311 | if t==T-1: 312 | # 最後の単語 313 | foward_prob += math.log(self.trans_prob_eos[c]) 314 | 315 | # 正規化を元に戻す 316 | log_a[t,k,c] = foward_prob 317 | #a[t,k,c] = foward_prob 318 | valid[t,k,c] = 1.0 319 | if math.isnan(foward_prob): 320 | print( "a[t=%d,k=%d,c=%d] became NAN!!" % (t,k,c) ) 321 | sys.exit(-1) 322 | # 正規化 323 | if t-self.MIN_LEN>=0: 324 | z[t] = logsumexp( log_a[t,:,:] ) 325 | log_a[t,:,:] -= z[t] 326 | #z[t] = logsumexp( a[t,:,:] ) 327 | #a[t,:,:] -= z[t] 328 | return np.exp(log_a)*valid 329 | """ 330 | 331 | 332 | def sample_idx(self, prob ): 333 | accm_prob = [0,] * len(prob) 334 | for i in range(len(prob)): 335 | accm_prob[i] = prob[i] + accm_prob[i-1] 336 | 337 | rnd = random.random() * accm_prob[-1] 338 | for i in range(len(prob)): 339 | if rnd <= accm_prob[i]: 340 | return i 341 | 342 | 343 | def backward_sampling(self, a, d): 344 | T = a.shape[0] 345 | t = T-1 346 | 347 | segm = [] 348 | segm_class = [] 349 | 350 | c = -1 351 | while True: 352 | if t==T-1: 353 | transp = self.trans_prob_eos 354 | else: 355 | transp = self.trans_prob[:,c] 356 | 357 | idx = self.sample_idx( (a[t]*transp).reshape( self.MAX_LEN*self.numclass )) 358 | 359 | k = int(idx/self.numclass) 360 | c = idx % self.numclass 361 | 362 | if t-k-1<=0: 363 | #先頭 364 | s = d[0:t+1] 365 | else: 366 | #先頭以外 367 | s = d[t-k:t+1] 368 | 369 | segm.insert( 0, s ) 370 | segm_class.insert( 0, c ) 371 | 372 | t = t-k-1 373 | 374 | if t<=0: 375 | break 376 | 377 | return segm, segm_class 378 | 379 | 380 | def calc_trans_prob( self ): 381 | self.trans_prob = np.zeros( (self.numclass,self.numclass) ) 382 | self.trans_prob_bos = np.zeros( self.numclass ) 383 | self.trans_prob_eos = np.zeros( self.numclass ) 384 | self.trans_prob += 0.1 385 | self.trans_prob_bos += 0.1 386 | self.trans_prob_eos += 0.1 387 | # 数え上げ 388 | for n,segm in enumerate(self.segments): 389 | if (n,0) in self.segmclass: 390 | c_begin = self.segmclass[(n, 0)] 391 | self.trans_prob_bos[c_begin]+=1 392 | if (n, len(segm)-1) in self.segmclass: 393 | c_end = self.segmclass[(n, len(segm)-1)] 394 | self.trans_prob_eos[c_end]+=1 395 | for i in range(1,len(segm)): 396 | try: 397 | cc = self.segmclass[ (n, i-1) ] 398 | c = self.segmclass[ (n, i) ] 399 | except KeyError: 400 | # gibss samplingで除かれているものは無視 401 | continue 402 | self.trans_prob[cc,c] += 1 403 | # 正規化 404 | self.trans_prob = self.trans_prob / self.trans_prob.sum(1).reshape(self.numclass,1) 405 | self.trans_prob_bos = self.trans_prob_bos / np.sum( self.trans_prob_bos ) 406 | self.trans_prob_eos = self.trans_prob_eos / np.sum( self.trans_prob_eos ) 407 | 408 | 409 | # list.remove( elem )だとValueErrorになる 410 | def remove_ndarray(self, lst, elem ): 411 | l = len(elem) 412 | for i,e in enumerate(lst): 413 | if len(e)!=l: 414 | continue 415 | if (e==elem).all(): 416 | lst.pop(i) 417 | return 418 | raise ValueError( "ndarray is not found!!" ) 419 | 420 | def learn(self): 421 | if self.is_initialized==False: 422 | # GPの学習 423 | for n in range(len(self.segments)): 424 | for i, s in enumerate(self.segments[n]): 425 | c = self.segmclass[(n,i)] 426 | self.segm_in_class[c].append( s ) 427 | 428 | # 各クラス毎に学習 429 | for c in range(self.numclass): 430 | self.update_gp( c ) 431 | 432 | self.is_initialized = True 433 | 434 | self.update(True) 435 | 436 | def recog(self): 437 | #self.update(False) 438 | self.segmclass.clear() 439 | 440 | for n, d in enumerate(self.data): 441 | # viterviで解く 442 | segms, classes = self.calc_vitervi_path( d ) 443 | self.segments[n] = segms 444 | for i, (s, c) in enumerate(zip( segms, classes )): 445 | self.segmclass[(n,i)] = c 446 | 447 | def update(self, learning_phase=True ): 448 | 449 | for n in range(len(self.segments)): 450 | d = self.data[n] 451 | segm = self.segments[n] 452 | 453 | for i, s in enumerate(segm): 454 | c = self.segmclass[(n,i)] 455 | self.segmclass.pop( (n,i) ) 456 | 457 | if learning_phase: 458 | # パラメータ更新 459 | self.remove_ndarray( self.segm_in_class[c], s ) 460 | 461 | if learning_phase: 462 | # GP更新 463 | for c in range(self.numclass): 464 | self.update_gp( c ) 465 | 466 | # 遷移確率更新 467 | self.calc_trans_prob() 468 | 469 | start = time.time() 470 | print( "forward...", end="") 471 | a = self.forward_filtering( d ) 472 | 473 | print( "backward...", end="" ) 474 | segm, segm_class = self.backward_sampling( a, d ) 475 | print( time.time()-start, "sec" ) 476 | 477 | print( "Number of classified segments: [", end="") 478 | for s in self.segm_in_class: 479 | print( len(s), end=" " ) 480 | print( "]" ) 481 | 482 | 483 | self.segments[n] = segm 484 | 485 | start = time.time() 486 | for i, (s,c) in enumerate(zip( segm, segm_class )): 487 | self.segmclass[(n,i)] = c 488 | 489 | # パラメータ更新 490 | if learning_phase: 491 | self.segm_in_class[c].append(s) 492 | 493 | if learning_phase: 494 | # GP更新 495 | for c in range(self.numclass): 496 | self.update_gp( c ) 497 | 498 | # 遷移確率更新 499 | self.calc_trans_prob() 500 | print( "parameter update...", time.time()-start, "sec" ) 501 | 502 | # hyperparameter更新 503 | #for c in range(self.numclass): 504 | # self.gps[c].estimate_hyperparams(100) 505 | 506 | return 507 | 508 | def calc_lik(self): 509 | lik = 0 510 | for n, segm in enumerate(self.segments): 511 | for i, s in enumerate(segm): 512 | c = self.segmclass[(n,i)] 513 | #lik += self.gps[c].calc_lik( np.arange(len(s),dtype=np.float) , np.array(s) ) 514 | lik += self.gps[c].calc_lik( np.arange(len(s), dtype=float) , s ) 515 | 516 | return lik 517 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial 4.0 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