def write(self):
if not (len(self.examples) > 0):
print 'EmbodiedLaserDetector.write: no examples to record'
return
#dataset = matrix_to_dataset(ut.list_mat_to_mat(self.examples, axis=1), type=self.type)
inputs = ut.list_mat_to_mat(self.examples, axis = 1)
outputs = ut.list_mat_to_mat(self.labels, axis = 1)
print 'EmbodiedLaserDetector.write: inputs.shape, outputs.shape', inputs.shape, outputs.shape
dim_reduce_set = rf.LinearDimReduceDataset(inputs, outputs)
print 'EmbodiedLaserDetector.write: appending examples from disk to dataset'
n = append_examples_from_file(dim_reduce_set, file=LaserPointerDetector.DEFAULT_DATASET_FILE)
print 'EmbodiedLaserDetector.write: calculating pca projection vectors'
dim_reduce_set.set_projection_vectors(dr.pca_vectors(dim_reduce_set.inputs, percent_variance=LaserPointerDetector.PCA_VARIANCE_RETAIN))
print 'EmbodiedLaserDetector.write: writing...'
dump_pickle(dim_reduce_set, LaserPointerDetector.DEFAULT_DATASET_FILE)
print 'EmbodiedLaserDetector: recorded examples to disk. Total in dataset', n
self.examples = []
self.labels = []
def write(self):
if not (len(self.examples) > 0):
rospy.loginfo('EmbodiedLaserDetector.write: no examples to record')
return
inputs = ut.list_mat_to_mat(self.examples, axis = 1)
outputs = ut.list_mat_to_mat(self.labels, axis = 1)
#import pdb
#pdb.set_trace()
rospy.loginfo('EmbodiedLaserDetector.write: inputs.shape, outputs.shape ' + str(inputs.shape) + ' ' + str(outputs.shape))
dim_reduce_set = rf.LinearDimReduceDataset(inputs, outputs)
rospy.loginfo('EmbodiedLaserDetector.write: appending examples from disk to dataset')
n = append_examples_from_file(dim_reduce_set, file=self.dataset_file)
rospy.loginfo('EmbodiedLaserDetector.write: calculating pca projection vectors')
dim_reduce_set.set_projection_vectors(dr.pca_vectors(dim_reduce_set.inputs, percent_variance=self.left_detector.PCA_VARIANCE_RETAIN))
rospy.loginfo('EmbodiedLaserDetector.write: writing...')
ut.dump_pickle(dim_reduce_set, self.dataset_file)
rospy.loginfo('EmbodiedLaserDetector: recorded examples to disk. Total in dataset ' + str(n))
self.examples = []
self.labels = []
def write(self):
if not (len(self.examples) > 0):
print 'EmbodiedLaserDetector.write: no examples to record'
return
#dataset = matrix_to_dataset(ut.list_mat_to_mat(self.examples, axis=1), type=self.type)
inputs = ut.list_mat_to_mat(self.examples, axis=1)
outputs = ut.list_mat_to_mat(self.labels, axis=1)
print 'EmbodiedLaserDetector.write: inputs.shape, outputs.shape', inputs.shape, outputs.shape
dim_reduce_set = rf.LinearDimReduceDataset(inputs, outputs)
print 'EmbodiedLaserDetector.write: appending examples from disk to dataset'
n = append_examples_from_file(
dim_reduce_set, file=LaserPointerDetector.DEFAULT_DATASET_FILE)
print 'EmbodiedLaserDetector.write: calculating pca projection vectors'
dim_reduce_set.set_projection_vectors(
dr.pca_vectors(
dim_reduce_set.inputs,
percent_variance=LaserPointerDetector.PCA_VARIANCE_RETAIN))
print 'EmbodiedLaserDetector.write: writing...'
dump_pickle(dim_reduce_set, LaserPointerDetector.DEFAULT_DATASET_FILE)
print 'EmbodiedLaserDetector: recorded examples to disk. Total in dataset', n
self.examples = []
self.labels = []
