<commit_before>import numpy as np

# number of dimensions of configuration 
M = 2 

# items for comparitive runs and plotting
ALGORITHMS = ('_smacof_single', '_smacof_with_anchors_single', '_smacof_with_distance_recovery_single')
LABELS = dict(zip(ALGORITHMS, ['Classical MDS', 'Anchored MDS', 'MDS-RFID']))

# channel estimates for each anchor as seen by the tag
ALPHA = np.array([1.6, 2.4, 1.9, 1.51, 1.6, 2.0])
ALPHA = np.array([2.0, 2.0, 2.0, 2.0, 2.0, 2.0])

# define bounding box for node deployment
'''Larger values reduce the chances that nodes will overlap'''
X_MAX = 30
X_MIN = 0
Y_MAX = 20
Y_MIN = 0

_ORIGIN = np.array([X_MIN, Y_MIN])
_ANCHORS = np.array([_ORIGIN, 
			[X_MAX, Y_MIN], 
			[X_MAX, Y_MAX],
			[X_MIN, Y_MAX],
			[(X_MAX+X_MIN)/2.0, Y_MAX],
			[(X_MAX+X_MIN)/2.0, Y_MIN],
			[X_MIN, (Y_MAX+Y_MIN)/2.0],
			[X_MAX, (Y_MAX+Y_MIN)/2.0]])


class Config(object):
	'''
	'''
	def __init__(self, no_of_anchors=4, no_of_tags=10, noise=1, mu=0, **kwargs):
		self.no_of_anchors = no_of_anchors
		if no_of_tags < 1:
			raise ValueError('number of tags cannot be less that 1')
		self.no_of_tags = no_of_tags
		self.sigma = noise
		self.mu = mu
		for k, v in kwargs.items():
			setattr(self, k, v)

		self._points = self._anchors = self._tags = None

	def generate_points(self):
		#TODO: handle overlapping points
		if self._points is None:            
			self._points = np.concatenate((self.anchors, self.tags))
		return self._points

	@property
	def points(self):
		return self._points

	@property
	def anchors(self):
		if self._anchors is None:
			if self.no_of_anchors < 1:
				self._anchors = np.array([[]])
			else:
				self._anchors = _ANCHORS[:self.no_of_anchors]
		return self._anchors

	@property
	def tags(self):
		if self._tags is None:
			x0_displacements = np.random.choice(np.arange(1, X_MAX-X_MIN-1), size=self.no_of_tags)
			y0_displacements = np.random.choice(np.arange(1, Y_MAX-Y_MIN-1), size=self.no_of_tags)
			self._tags =  np.array([_ORIGIN + pt for pt in zip(x0_displacements, y0_displacements)])
		return self._tags


# only use default for debugging
DEFAULT_POINTS = np.array([[0,20], [30,20], [30,0], [0,0], [18, 7], [4, 9], [10, 5], [7, 17], [15, 14], [12, 18], [22, 11]])<commit_msg>replace private variable with callable in property points<commit_after>import numpy as np

# number of dimensions of configuration 
M = 2 

# items for comparitive runs and plotting
ALGORITHMS = ('_smacof_single', '_smacof_with_anchors_single', '_smacof_with_distance_recovery_single')
LABELS = dict(zip(ALGORITHMS, ['Classical MDS', 'Anchored MDS', 'MDS-RFID']))

# channel estimates for each anchor as seen by the tag
ALPHA = np.array([1.6, 2.4, 1.9, 1.51, 1.6, 2.0])
ALPHA = np.array([2.0, 2.0, 2.0, 2.0, 2.0, 2.0])

# define bounding box for node deployment
'''Larger values reduce the chances that nodes will overlap'''
X_MAX = 30
X_MIN = 0
Y_MAX = 20
Y_MIN = 0

_ORIGIN = np.array([X_MIN, Y_MIN])
_ANCHORS = np.array([_ORIGIN, 
			[X_MAX, Y_MIN], 
			[X_MAX, Y_MAX],
			[X_MIN, Y_MAX],
			[(X_MAX+X_MIN)/2.0, Y_MAX],
			[(X_MAX+X_MIN)/2.0, Y_MIN],
			[X_MIN, (Y_MAX+Y_MIN)/2.0],
			[X_MAX, (Y_MAX+Y_MIN)/2.0]])


class Config(object):
	'''
	'''
	def __init__(self, no_of_anchors=4, no_of_tags=10, noise=1, mu=0, **kwargs):
		self.no_of_anchors = no_of_anchors
		if no_of_tags < 1:
			raise ValueError('number of tags cannot be less that 1')
		self.no_of_tags = no_of_tags
		self.sigma = noise
		self.mu = mu
		for k, v in kwargs.items():
			setattr(self, k, v)

		self._points = self._anchors = self._tags = None

	def generate_points(self):
		#TODO: handle overlapping points
		if self._points is None:            
			self._points = np.concatenate((self.anchors, self.tags))
		return self._points

	@property
	def points(self):
		return self.generate_points()

	@property
	def anchors(self):
		if self._anchors is None:
			if self.no_of_anchors < 1:
				self._anchors = np.array([[]])
			else:
				self._anchors = _ANCHORS[:self.no_of_anchors]
		return self._anchors

	@property
	def tags(self):
		if self._tags is None:
			x0_displacements = np.random.choice(np.arange(1, X_MAX-X_MIN-1), size=self.no_of_tags)
			y0_displacements = np.random.choice(np.arange(1, Y_MAX-Y_MIN-1), size=self.no_of_tags)
			self._tags =  np.array([_ORIGIN + pt for pt in zip(x0_displacements, y0_displacements)])
		return self._tags


# only use default for debugging
DEFAULT_POINTS = np.array([[0,20], [30,20], [30,0], [0,0], [18, 7], [4, 9], [10, 5], [7, 17], [15, 14], [12, 18], [22, 11]])
{'fraction_non_alphanumeric': 0.15053285968028418, 'fraction_numerical': 0.040408525754884544, 'mean_word_length': 4.039149888143177, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3339994', 'n_tokens_mistral': 2050, 'n_tokens_neox': 1843, 'n_words': 450}