def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.output_file == None:
raise FLANNException("No output file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset, algorithm = self.options.algorithm,
trees=self.options.trees, branching=self.options.branching,
iterations=self.options.max_iterations, centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print "Searching for nearest neighbors"
matches,dists = flann.nn_index(testset, self.options.nn, checks = self.options.checks)
print "Writing matches to", self.options.output_file
write(matches, self.options.output_file, format="dat")
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.algorithm == None:
raise FLANNException("No algorithm specified")
if self.options.test_file == None:
raise FLANNException("No test file given.")
if self.options.output_file == None:
raise FLANNException("No output file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
flann = FLANN(log_level=self.options.log_level)
flann.build_index(dataset,
algorithm=self.options.algorithm,
trees=self.options.trees,
branching=self.options.branching,
iterations=self.options.max_iterations,
centers_init=self.options.centers_init)
print 'Reading test dataset from', self.options.test_file
testset = read(self.options.test_file)
print "Searching for nearest neighbors"
matches, dists = flann.nn_index(testset,
self.options.nn,
checks=self.options.checks)
print "Writing matches to", self.options.output_file
write(matches, self.options.output_file, format="dat")
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
if self.options.output_file==None:
raise FLANNException("Need an output file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file, dtype=numpy.dtype(self.options.dtype))
print "Writing to file %s"%self.options.output_file
write(dataset,self.options.output_file, format=self.options.format)
def execute(self):
if self.options.count>0 and self.options.length>0 and self.options.filename!=None:
print "Saving a random (%d,%d) matrix in file %s... "%(self.options.count,self.options.length, self.options.filename),
stdout.flush()
data = float32(random((self.options.count,self.options.length)))
write(data,self.options.filename)
print "done"
else:
raise FLANNException("Error: Incorrect arguments specified (a filename must be given and the count and length must be positive)")
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
if self.options.output_file==None:
raise FLANNException("Need an output file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file, dtype=numpy.dtype(self.options.dtype))
print "Writing to file %s"%self.options.output_file
write(dataset,self.options.output_file, format=self.options.format)
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file)
if self.options.count>0:
print "Sampling %d features"%self.options.count
sampledset = sample_dataset(dataset, self.options.count)
print "Writing sampled dataset to file %s"%self.options.save_file
write(sampledset,self.options.save_file, format=self.options.format)
def execute(self):
if self.options.count > 0 and self.options.length > 0 and self.options.filename != None:
print "Saving a random (%d,%d) matrix in file %s... " % (
self.options.count, self.options.length,
self.options.filename),
stdout.flush()
data = float32(random((self.options.count, self.options.length)))
write(data, self.options.filename)
print "done"
else:
raise FLANNException(
"Error: Incorrect arguments specified (a filename must be given and the count and length must be positive)"
)
def execute(self):
if self.options.input_file == None:
raise FLANNException("Need an input file")
print "Reading input data from file " + self.options.input_file
dataset = read(self.options.input_file)
if self.options.count > 0:
print "Sampling %d features" % self.options.count
sampledset = sample_dataset(dataset, self.options.count)
print "Writing sampled dataset to file %s" % self.options.save_file
write(sampledset,
self.options.save_file,
format=self.options.format)
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.clusters_file == None:
raise FLANNException("No clusters file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
print "Computing clusters"
flann = FLANN(log_level = self.options.log_level)
num_clusters = self.options.clusters
branching = self.options.branching
num_branches = (num_clusters-1)/(branching-1)
clusters = flann.hierarchical_kmeans(dataset, branching, num_branches,
self.options.max_iterations, centers_init=self.options.centers_init)
print "Saving %d clusters to file %s"%(clusters.shape[0],self.options.clusters_file)
write(clusters, self.options.clusters_file, format="dat")
def execute(self):
if self.options.input_file == None:
raise FLANNException("Need an input file")
print "Reading input data from file " + self.options.input_file
dataset = read(self.options.input_file)
if self.options.test_file == None:
raise FLANNException("Need a test file")
if isfile(self.options.test_file):
print "Reading test data from file " + self.options.test_file
testset = read(self.options.test_file)
else:
print "Sampling test file"
testset = sample_dataset(dataset, self.options.count, remove=True)
dataset = dataset[0:dataset.shape[0] - self.options.count]
print "Wrinting new dataset file"
write(dataset, "new_" + self.options.input_file)
print "Writing testset file"
write(testset, self.options.test_file)
print "Computing ground truth"
start = time.clock()
match = compute_ground_truth(dataset, testset, self.options.nn)
print "It took %g seconds" % (time.clock() - start)
print "Writing match file"
write(match, self.options.match_file, format="dat")
def execute(self):
if self.options.input_file==None:
raise FLANNException("Need an input file")
print "Reading input data from file "+self.options.input_file
dataset = read(self.options.input_file)
if self.options.test_file==None:
raise FLANNException("Need a test file")
if isfile(self.options.test_file):
print "Reading test data from file "+self.options.test_file
testset = read(self.options.test_file)
else:
print "Sampling test file"
testset = sample_dataset(dataset, self.options.count, remove=True)
dataset = dataset[0:dataset.shape[0]-self.options.count]
print "Wrinting new dataset file"
write(dataset,"new_"+self.options.input_file)
print "Writing testset file"
write(testset,self.options.test_file)
print "Computing ground truth"
start = time.clock()
match = compute_ground_truth(dataset, testset, self.options.nn)
print "It took %g seconds"%(time.clock()-start)
print "Writing match file"
write(match,self.options.match_file, format="dat")
def execute(self):
if self.options.input_file == None:
raise FLANNException("No input file given.")
if self.options.clusters_file == None:
raise FLANNException("No clusters file given.")
print 'Reading input dataset from', self.options.input_file
dataset = read(self.options.input_file)
print "Computing clusters"
flann = FLANN(log_level=self.options.log_level)
num_clusters = self.options.clusters
branching = self.options.branching
num_branches = (num_clusters - 1) / (branching - 1)
clusters = flann.hierarchical_kmeans(
dataset,
branching,
num_branches,
self.options.max_iterations,
centers_init=self.options.centers_init)
print "Saving %d clusters to file %s" % (clusters.shape[0],
self.options.clusters_file)
write(clusters, self.options.clusters_file, format="dat")
