sigma = 1e6,
)
basis_NB = specmine.spectral.laplacian_basis(affinity_NN, k = 32)
feature_map = \
specmine.discovery.InterpolationFeatureMap(
basis_NB,
avectors_ND,
specmine.go.board_to_affinity,
)
rows = [["eigenvector", "x", "y", "value"]]
for x in xrange(9):
for y in xrange(9):
grid = numpy.zeros((9, 9), numpy.int8)
grid[x, y] = 1
features = feature_map[specmine.go.BoardState(grid)]
for i in xrange(features.shape[0]):
rows.append([i, x, y, features[i]])
with specmine.util.openz(out_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerows(rows)
if __name__ == "__main__":
specmine.script(main)
# randomly sample subset
value_dict = dict(shuffled_values[:samples])
test_samples = 20000
if off_graph:
test_values = dict(shuffled_values[-test_samples:])
else:
test_values = dict(shuffled_values[:test_samples])
logger.info("kept %i board samples", len(value_dict))
avectors_ND = numpy.array(map(specmine.go.board_to_affinity, value_dict))
#for B in numpy.r_[1:200:8j].round().astype(int):
for B in [200]:
yield (run_graph_features, ["affinity", B, avectors_ND, test_values])
with open(out_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(["map_name", "features", "score_mean", "score_variance"])
for (_, row) in condor.do(yield_jobs(), workers):
writer.writerow(row)
out_file.flush()
if __name__ == "__main__":
specmine.script(main)
# TODO move to tools/util
def get_value_list(games_path,values_path):
''' takes a list of games and a dictionary of values and builds a list of
(BoardState, value) pairs '''
with specmine.util.openz(games_path) as games_file:
games = pickle.load(games_file)
with specmine.util.openz(values_path) as values_file:
values = pickle.load(values_file)
value_list = []
for game in values.keys():
try:
vals = values[game]
boards = map(specmine.go.BoardState, games[game].grids)
value_list.extend(zip(boards,vals))
except KeyError:
print 'game unkown for ',game
# remove duplicates
value_dict = dict(value_list)
value_keys = value_dict.keys()
value_list = zip(value_keys,map(value_dict.__getitem__,value_keys))
return value_list
if __name__ == "__main__":
specmine.script(measure_feature_performance)
avectors_ND = numpy.array(map(specmine.go.board_to_affinity, boards))
affinity_NN = specmine.discovery.affinity_graph(avectors_ND, neighbors, sigma = 1e6)
for B in numpy.r_[0:250:10j].round().astype(int):
if interpolate:
yield (run_template_features, [2, 2, B, test_values])
#yield (run_template_features, [2, 3, B, test_values])
yield (run_template_features, [3, 3, B, test_values])
yield (run_random_features, [B, avectors_ND, index, test_values, interpolate], dict(aff_map = affinity_map))
yield (run_laplacian_features, ["Laplacian",B,avectors_ND, affinity_NN, index, test_values, interpolate], dict(aff_map = affinity_map))
else:
yield (run_template_features, [2, 2, B, test_values])
#yield (run_template_features, [2, 3, B, test_values])
yield (run_template_features, [3, 3, B, test_values])
yield (run_random_features, [B, avectors_ND, index, test_values, interpolate])
yield (run_laplacian_features, ["Laplacian",B,avectors_ND, affinity_NN, index, test_values, interpolate])
with open(out_path, "wb") as out_file:
writer = csv.writer(out_file)
writer.writerow(["map_name", "features", "samples", "score_mean", "score_variance"])
for (_, row) in condor.do(yield_jobs(), workers):
writer.writerow(row)
if __name__ == "__main__":
specmine.script(flat_affinity_test)
#specmine.script(clustered_affinity_test)