tex: Future research
[gostyle.git] / gostyle.py
blob55b102fc903579d78019c531e17a34df47432f8e
1 #!/usr/bin/python
2 """
3 This file contains several objects we use to process a pattern files for Go game.
4 We use it to generate input sets for a neural network (libfann), or if we want to perform a PCA analysis to data.
5 It generates input vectors from pattern files. See `Const.pat_file_regexp' for file format in regexp.
7 ===== EXAMPLE PAT FILE =====
8 4632 (border:3 s:5000003 s:6000049 s:700004a)
9 3497 (atariescape:0 border:0 ldist:4 lldist:2 s:30011a1)
10 ...
11 ===== END =====
12 """
13 import re
14 import sys
15 import cPickle
16 import random
17 from itertools import izip,count
19 class Const:
20 """Class used to hold global const variables, such as the pat file format."""
21 pat_file_regexp = '^\s*(\d+)\s*(.+)$'
23 def print_vector(vector, where=sys.stdout):
24 """Helper method for printing vector (list of floats)."""
25 for x in vector:
26 print >> where, x,
27 print >> where
29 def print_set_to_file( data, filename):
30 """
31 Helper method for printing datasets for neural network.
32 FORMAT of the file
33 number_of_pairs len_of_input_vector len_of_output_vector
34 first_input_vector
35 first_output_vector
36 second_input_vector
37 ...
38 """
39 def print_set( data, where):
40 print >> where, len(data), len(data[0][0]), len(data[0][1])
41 for i,o in data:
42 print_vector(i, where)
43 print_vector(o, where)
44 fout = open(filename, 'w')
45 print_set(data, fout)
46 fout.close()
48 def dump_object_to_file(object, filename):
49 """Helper function to save an object to file using cPickle module."""
50 f=open(filename,'w')
51 cPickle.dump(object, f,-1)
52 f.close()
54 def load_object_from_file(filename):
55 """Helper function to recover an object from file using cPickle module."""
56 f=open(filename,'r')
57 object = cPickle.load(f)
58 f.close()
59 return object
61 class VectorGenerator(object):
62 """Abstract class. When called, returns a vector (list of floats). Has output_dim param."""
63 def __call__(self):
64 raise NotImplementedError
66 class VectorToVector(VectorGenerator):
67 """Abstract class. When called with a vector (list of floats), returns a vector. Has input_dim and output_dim params."""
68 def __call__(self, vector):
69 raise NotImplementedError
71 ###
72 ### PCA Analysis
73 ### Note that you must have `numpy' and `mdp' python modules, otherwise the PCA will
74 ### fallback to doing nothing at all (but doing it completely compatibly with the rest of the code :-).
75 try:
76 from numpy import array
77 import mdp
78 class PCA(VectorToVector):
79 """
80 Object performing a PCA analysis on either a given vector (see `__call__' function),
81 or on a list of vectors (see `process_list_of_vectors' function).
82 """
83 def __init__(self, list_of_train_vectors, *args, **kwargs):
84 self.pca = mdp.nodes.PCANode(*args, **kwargs)
85 self.pca.train(array(list_of_train_vectors))
86 self.input_dim = self.pca.input_dim
87 def __call__(self, vector):
88 return list(self.pca(array([vector]))[0])
89 def process_list_of_vectors(self, list_of_vectors):
90 return [ list(vec) for vec in self.pca(array(list_of_vectors)) ]
91 def get_projection_info(self):
92 return self.pca.get_recmatrix()
94 except ImportError, e:
95 print >>sys.stderr, "Warning: %s. PCA will not work."%(str(e))
97 class PCA(VectorToVector):
98 """Default dummy class for PCA, not very useless."""
99 def __init__(self, *args, **kwargs):
100 pass
101 def __call__(self, list_of_vectors):
102 return list_of_vectors
103 def process_list_of_vectors(self, list_of_vectors):
104 return list_of_vectors
107 class Compose(VectorGenerator):
109 A class used as a composer of different objects, such as InputVectorGenerator and PCA.
110 Use this if you want to e.g. generate PCA processed vectors.
112 def __init__(self, vector_generator, vector_to_vector):
113 if not isinstance(vector_generator, VectorGenerator):
114 raise TypeError
115 if not isinstance(vector_to_vector, VectorToVector):
116 raise TypeError
117 self.vector_generator = vector_generator
118 self.vector_to_vector = vector_to_vector
119 # if vector_generator.output_dim != vector_to_vector.input_dim:
120 # raise RuntimeError("Dimensions of Composed object mismatch.")
121 def __call__(self, *args, **kwargs):
122 return self.vector_to_vector(self.vector_generator(*args, **kwargs))
124 class OccurenceVectorGenerator(VectorGenerator):
126 A class used to generate input vectors based on a relative number of occurences of some input patterns.
127 The object is initialized with a file of patterns. It takes the topmost `num_features' patterns.
129 def generate_top_pattern_dict(self):
130 rexp=re.compile(Const.pat_file_regexp)
131 self.top_pattern_dict = {}
132 self.top_pattern_str = {}
133 i = 0
134 input_file = open(self.filename)
135 for line in input_file:
136 if i >= self.output_dim:
137 break
138 s = rexp.match(line).group(2)
139 self.top_pattern_dict[s] = i
140 self.top_pattern_str[i] = s
141 i += 1
142 input_file.close()
144 def __init__(self, main_pat_file, num_features):
145 self.output_dim = num_features
146 self.filename = main_pat_file
147 self.generate_top_pattern_dict()
149 def __call__(self, pat_file):
150 vector = [0]*len(self.top_pattern_dict)
151 rexp=re.compile(Const.pat_file_regexp)
152 i = 0
153 input_file = open(pat_file)
154 for line in input_file:
155 match = rexp.match(line)
156 if not match:
157 raise IOError("Wrong file format: " + pat_file)
158 if match.group(2) in self.top_pattern_dict:
159 index=self.top_pattern_dict[match.group(2)]
160 vector[index] += int(match.group(1))
161 i += 1
162 if i >= len(self.top_pattern_dict):
163 break
164 input_file.close()
165 if len(vector) != self.output_dim:
166 raise RuntimeError
167 return vector
169 def stringof(self, i):
170 return self.top_pattern_str[i]
172 class Rescale(VectorToVector):
173 """Class that rescales vectors to a given interval!"""
174 def __init__(self, a=-1.0, b=1.0):
175 if a > b:
176 raise RuntimeError
177 self.a = a
178 self.tot = b - a #abs(a) + abs(b)
179 def __call__(self, vector):
180 if len(vector) == 0:
181 raise RuntimeError
182 to_zero = 0 - min(vector)
183 maximum = max(vector) + to_zero
184 return [ self.tot * (x + to_zero) / maximum + self.a for x in vector ]
186 class InputVectorGenerator(VectorGenerator):
188 First we generate an occurence vector by OccurenceVectorGenerator.
189 Then, an input vector is generated as a relative number of occurences of the topmost patterns.
190 The occurences are mapped so that the most frequently used
191 one is mapped to 1.0 and the rest is mapped relatively on the scale (1.0,-1.0). See `__call__' function.
193 def __init__(self, *args, **kwargs):
194 self.ovg = OccurenceVectorGenerator(*args, **kwargs)
195 self.gen = Compose(self.ovg, Rescale(-1.0, 1.0))
196 def __call__(self, *args, **kwargs):
197 return self.gen(*args, **kwargs)
198 def ovg(self):
199 return self.ovg
201 def linear_combination(list_of_vectors, coefs):
202 if len(list_of_vectors) != len(coefs):
203 raise Exception("len(list_of_vectors) != len(coefs)")
204 if len(list_of_vectors) == 0:
205 return
206 len_vec=len(list_of_vectors[0])
207 res_vec=[0]*len_vec
208 for p in xrange(len(list_of_vectors)):
209 for i in xrange(len_vec):
210 res_vec[i] += coefs[p] * list_of_vectors[p][i]
211 return res_vec
213 def get_random_norm_coefs( num ):
214 coefs=[]
215 rnd_nums= [ random.random() for i in xrange(num-1) ] + [1]
216 rnd_nums.sort()
217 first=0
218 for next in rnd_nums:
219 coefs.append(next-first)
220 first=next
221 return coefs
223 class Combinator(object):
224 def __init__(self, num_lincombs = 1, skip_subset_len = [0], max_len = 2):
225 self.num_lincombs = num_lincombs
226 self.skip_subset_len = skip_subset_len
227 self.max_len = max_len
228 def get_subsets(self, set):
229 if len(set) == 0:
230 return [[]]
231 sub=self.get_subsets(set[1:])
232 return sub + filter( lambda x : ( self.max_len==0 or len(x)<=self.max_len ), [ set[:1]+subset for subset in sub] )
233 def combine(self, data):
234 combinations = []
235 for subset in self.get_subsets(range(len(data))):
236 if len(subset) in self.skip_subset_len:
237 continue
238 input_vectors = [ data[index][0] for index in subset ]
239 output_vectors = [ data[index][1] for index in subset ]
240 for i in xrange(self.num_lincombs):
241 coefs = get_random_norm_coefs(len(subset))
242 combinations += [(linear_combination(input_vectors, coefs), linear_combination(output_vectors, coefs))]
243 return combinations
245 class PlayerStrategyIdentificator(object):
246 """Object holding information about default strategies for players."""
247 def __init__(self, strategy_players):
248 self.strategy_players = strategy_players
250 self.player_strategy={}
251 self.all_players = []
252 self.all_strategies = []
253 for strategy, players in self.strategy_players.items():
254 self.all_strategies += [strategy]
255 for player in players:
256 self.all_players += [player]
257 self.player_strategy[player] = strategy
259 def __call__(self, player_name):
260 return self.player_strategy[player_name]
262 class StrategyOutputVectorGenerator(VectorGenerator):
264 This object generates output vectors for players with strategies specified in `PlayerStrategyIdentificator' object.
265 It is initialized with a list of strategies `valid_strategies' it shall take into acount.
266 When called (see `__call__') with a name of a player with a strategy from `valid_strategies' it returns a vector
267 that corresponds to the strategy like this.
269 def __init__(self, strategy_players, valid_strategies=None):
270 self.identificator = PlayerStrategyIdentificator(strategy_players)
271 if valid_strategies == None:
272 valid_strategies = self.identificator.all_strategies
273 index=0
274 self.valid_strategy_index={}
275 for s in valid_strategies:
276 self.valid_strategy_index[s]= index
277 index += 1
278 def __call__(self, player_name):
279 try:
280 player_strat = self.identificator(player_name)
281 player_strat_index = self.valid_strategy_index[player_strat]
282 return [ 1.0 if i == player_strat_index else -1.0 for i in xrange(len(self.valid_strategy_index)) ]
283 except KeyError:
284 return None
286 class PlanarOutputVectorGenerator(VectorGenerator):
287 """Class that explicitly returns predefined output vectors for given players."""
288 def __init__(self, player_vector):
289 self.player_vector = player_vector
290 self.players = player_vector.keys()
291 def __call__(self, player_name):
292 try:
293 return self.player_vector[player_name]
294 except KeyError:
295 return None
297 if __name__ == '__main__':
298 print >>sys.stderr, "This is just a library file..."