tex: Move expert-base knowledge info to latter sections
[gostyle.git] / gostyle.py
blobfab06098fea0a7f5b8676cef3c29f83a5ba91c75
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.pca.stop_training()
87 self.input_dim = self.pca.input_dim
88 def __call__(self, vector):
89 return list(self.pca(array([vector]))[0])
90 def process_list_of_vectors(self, list_of_vectors):
91 return [ list(vec) for vec in self.pca(array(list_of_vectors)) ]
92 def get_projection_info(self):
93 return self.pca.get_recmatrix()
94 def get_eigenvalues(self):
95 return self.pca.d
96 def get_eigenvectors(self):
97 return list(self.pca.v)
99 except ImportError, e:
100 print >>sys.stderr, "Warning: %s. PCA will not work."%(str(e))
102 class PCA(VectorToVector):
103 """Default dummy class for PCA, not very useless."""
104 def __init__(self, *args, **kwargs):
105 pass
106 def __call__(self, list_of_vectors):
107 return list_of_vectors
108 def process_list_of_vectors(self, list_of_vectors):
109 return list_of_vectors
112 class Compose(VectorGenerator):
114 A class used as a composer of different objects, such as InputVectorGenerator and PCA.
115 Use this if you want to e.g. generate PCA processed vectors.
117 def __init__(self, vector_generator, vector_to_vector):
118 if not isinstance(vector_generator, VectorGenerator):
119 raise TypeError
120 if not isinstance(vector_to_vector, VectorToVector):
121 raise TypeError
122 self.vector_generator = vector_generator
123 self.vector_to_vector = vector_to_vector
124 # if vector_generator.output_dim != vector_to_vector.input_dim:
125 # raise RuntimeError("Dimensions of Composed object mismatch.")
126 def __call__(self, *args, **kwargs):
127 return self.vector_to_vector(self.vector_generator(*args, **kwargs))
129 class OccurenceVectorGenerator(VectorGenerator):
131 A class used to generate input vectors based on a relative number of occurences of some input patterns.
132 The object is initialized with a file of patterns. It takes the topmost `num_features' patterns.
134 def generate_top_pattern_dict(self):
135 rexp=re.compile(Const.pat_file_regexp)
136 self.top_pattern_dict = {}
137 self.top_pattern_str = {}
138 i = 0
139 input_file = open(self.filename)
140 for line in input_file:
141 if i >= self.output_dim:
142 break
143 s = rexp.match(line).group(2)
144 self.top_pattern_dict[s] = i
145 self.top_pattern_str[i] = s
146 i += 1
147 input_file.close()
149 def __init__(self, main_pat_file, num_features):
150 self.output_dim = num_features
151 self.filename = main_pat_file
152 self.generate_top_pattern_dict()
154 def __call__(self, pat_file):
155 vector = [0]*len(self.top_pattern_dict)
156 rexp=re.compile(Const.pat_file_regexp)
157 i = 0
158 input_file = open(pat_file)
159 for line in input_file:
160 match = rexp.match(line)
161 if not match:
162 raise IOError("Wrong file format: " + pat_file)
163 if match.group(2) in self.top_pattern_dict:
164 index=self.top_pattern_dict[match.group(2)]
165 vector[index] += int(match.group(1))
166 i += 1
167 if i >= len(self.top_pattern_dict):
168 break
169 input_file.close()
170 if len(vector) != self.output_dim:
171 raise RuntimeError
172 return vector
174 def stringof(self, i):
175 return self.top_pattern_str[i]
177 class Rescale(VectorToVector):
178 """Class that rescales vectors to a given interval!"""
179 def __init__(self, a=-1.0, b=1.0):
180 if a >= b:
181 raise RuntimeError("a must be < b")
182 self.a = a
183 self.avg = (a + b) * 0.5
184 self.tot = b - a
185 def __call__(self, vector):
186 if len(vector) == 0:
187 raise RuntimeError
188 to_zero = 0 - min(vector)
189 maximum = max(vector) + to_zero
190 if maximum == 0:
191 return [ self.avg for _ in vector ]
192 return [ self.tot * (x + to_zero) / maximum + self.a for x in vector ]
194 class InputVectorGenerator(VectorGenerator):
196 First we generate an occurence vector by OccurenceVectorGenerator.
197 Then, an input vector is generated as a relative number of occurences of the topmost patterns.
198 The occurences are mapped so that the most frequently used
199 one is mapped to 1.0 and the rest is mapped relatively on the scale (1.0,-1.0). See `__call__' function.
201 def __init__(self, *args, **kwargs):
202 self.ovg = OccurenceVectorGenerator(*args, **kwargs)
203 self.gen = Compose(self.ovg, Rescale(-1.0, 1.0))
204 def __call__(self, *args, **kwargs):
205 return self.gen(*args, **kwargs)
206 def ovg(self):
207 return self.ovg
209 def linear_combination(list_of_vectors, coefs):
210 if len(list_of_vectors) != len(coefs):
211 raise Exception("len(list_of_vectors) != len(coefs)")
212 if len(list_of_vectors) == 0:
213 return
214 len_vec=len(list_of_vectors[0])
215 res_vec=[0]*len_vec
216 for p in xrange(len(list_of_vectors)):
217 for i in xrange(len_vec):
218 res_vec[i] += coefs[p] * list_of_vectors[p][i]
219 return res_vec
221 def get_random_norm_coefs( num ):
222 coefs=[]
223 rnd_nums= [ random.random() for i in xrange(num-1) ] + [1]
224 rnd_nums.sort()
225 first=0
226 for next in rnd_nums:
227 coefs.append(next-first)
228 first=next
229 return coefs
231 class Combinator(object):
232 def __init__(self, num_lincombs = 1, skip_subset_len = [0], max_len = 2):
233 self.num_lincombs = num_lincombs
234 self.skip_subset_len = skip_subset_len
235 self.max_len = max_len
236 def get_subsets(self, set):
237 if len(set) == 0:
238 return [[]]
239 sub=self.get_subsets(set[1:])
240 return sub + filter( lambda x : ( self.max_len==0 or len(x)<=self.max_len ), [ set[:1]+subset for subset in sub] )
241 def combine(self, data):
242 combinations = []
243 for subset in self.get_subsets(range(len(data))):
244 if len(subset) in self.skip_subset_len:
245 continue
246 input_vectors = [ data[index][0] for index in subset ]
247 output_vectors = [ data[index][1] for index in subset ]
248 for i in xrange(self.num_lincombs):
249 coefs = get_random_norm_coefs(len(subset))
250 combinations += [(linear_combination(input_vectors, coefs), linear_combination(output_vectors, coefs))]
251 return combinations
253 class PlayerStrategyIdentificator(object):
254 """Object holding information about default strategies for players."""
255 def __init__(self, strategy_players):
256 self.strategy_players = strategy_players
258 self.player_strategy={}
259 self.all_players = []
260 self.all_strategies = []
261 for strategy, players in self.strategy_players.items():
262 self.all_strategies += [strategy]
263 for player in players:
264 self.all_players += [player]
265 self.player_strategy[player] = strategy
267 def __call__(self, player_name):
268 return self.player_strategy[player_name]
270 class StrategyOutputVectorGenerator(VectorGenerator):
272 This object generates output vectors for players with strategies specified in `PlayerStrategyIdentificator' object.
273 It is initialized with a list of strategies `valid_strategies' it shall take into acount.
274 When called (see `__call__') with a name of a player with a strategy from `valid_strategies' it returns a vector
275 that corresponds to the strategy like this.
277 def __init__(self, strategy_players, valid_strategies=None):
278 self.identificator = PlayerStrategyIdentificator(strategy_players)
279 if valid_strategies == None:
280 valid_strategies = self.identificator.all_strategies
281 index=0
282 self.valid_strategy_index={}
283 for s in valid_strategies:
284 self.valid_strategy_index[s]= index
285 index += 1
286 def __call__(self, player_name):
287 try:
288 player_strat = self.identificator(player_name)
289 player_strat_index = self.valid_strategy_index[player_strat]
290 return [ 1.0 if i == player_strat_index else -1.0 for i in xrange(len(self.valid_strategy_index)) ]
291 except KeyError:
292 return None
294 class PlanarOutputVectorGenerator(VectorGenerator):
295 """Class that explicitly returns predefined output vectors for given players."""
296 def __init__(self, player_vector):
297 self.player_vector = player_vector
298 self.players = player_vector.keys()
299 def __call__(self, player_name):
300 try:
301 return self.player_vector[player_name]
302 except KeyError:
303 return None
305 if __name__ == '__main__':
306 print >>sys.stderr, "This is just a library file..."