Ревізії MP Distribution

riccardo ревизій цього gist 11 months ago. До ревизії

1 file changed, 2 insertions, 1 deletion

mp_distr.py

			@@ -10,7 +10,8 @@ class MarchenkoPastur:
10	10		Parameters
11	11		----------
12	12		ratio : float
13		-	The ratio between the number of variables (columns) and the size of the sample (rows) contained in the data matrix. For numerical stability, it should be less than 1.
	13	+	The ratio between the number of variables (columns) and the size of the sample (rows) contained in the data matrix.
	14	+	For numerical stability, it should be less than 1.
14	15		sigma : float
15	16		The standard deviation of the distribution of values, by default, 1.0.
16	17

riccardo ревизій цього gist 11 months ago. До ревизії

Без змін

riccardo ревизій цього gist 1 year ago. До ревизії

1 file changed, 2 insertions, 2 deletions

mp_distr.py

			@@ -27,8 +27,8 @@ class MarchenkoPastur:
27	27		self.sigma = sigma
28	28
29	29		# Compute the limits of the distribution
30		-	self.l_bottom = sigma*2 (1.0 - np.sqrt(self.ratio))**2
31		-	self.l_upper = sigma*2 (1.0 + np.sqrt(self.ratio))**2
	30	+	self.l_bottom = self.sigma*2 (1.0 - np.sqrt(self.ratio))**2
	31	+	self.l_upper = self.sigma*2 (1.0 + np.sqrt(self.ratio))**2
32	32
33	33		def pdf(self, x: float \| ArrayLike) -> float \| ArrayLike:
34	34		"""

riccardo ревизій цього gist 1 year ago. До ревизії

1 file changed, 4 insertions, 1 deletion

mp_distr.py

			@@ -47,7 +47,10 @@ class MarchenkoPastur:
47	47		if not np.isscalar(x):
48	48		return np.vectorize(self.pdf, otypes=[float])(x)
49	49
	50	+	if x == 0.0:
	51	+	return 0.0
	52	+
50	53		num = np.sqrt(max(self.l_upper - x, 0.0) * max(x - self.l_bottom, 0.0))
51	54		den = 2.0 * np.pi * self.sigma*2 self.ratio * x
52	55
53		-	return float(num / den) if den != 0.0 else 0.0
	56	+	return float(num / den)

riccardo ревизій цього gist 1 year ago. До ревизії

1 file changed, 53 insertions

mp_distr.py(файл створено)

		@@ -0,0 +1,53 @@
1	+	import numpy as np
2	+
3	+	from numpy.typing import ArrayLike
4	+
5	+	class MarchenkoPastur:
6	+	"""Definition of a Marchenko-Pastur distribution"""
7	+
8	+	def __init__(self, ratio: float, sigma: float = 1.0):
9	+	"""
10	+	Parameters
11	+	----------
12	+	ratio : float
13	+	The ratio between the number of variables (columns) and the size of the sample (rows) contained in the data matrix. For numerical stability, it should be less than 1.
14	+	sigma : float
15	+	The standard deviation of the distribution of values, by default, 1.0.
16	+
17	+	Raises
18	+	------
19	+	ValueError
20	+	If ratio or sigma are not strictly positive.
21	+	"""
22	+	if ratio <= 0.0:
23	+	raise ValueError("The ratio must be strictly positive, but found %s <= 0.0!" % ratio)
24	+	self.ratio = ratio
25	+	if sigma <= 0.0:
26	+	raise ValueError("The standard deviation must be strictly positive, but found %s <= 0.0!" % sigma)
27	+	self.sigma = sigma
28	+
29	+	# Compute the limits of the distribution
30	+	self.l_bottom = sigma*2 (1.0 - np.sqrt(self.ratio))**2
31	+	self.l_upper = sigma*2 (1.0 + np.sqrt(self.ratio))**2
32	+
33	+	def pdf(self, x: float \| ArrayLike) -> float \| ArrayLike:
34	+	"""
35	+	Return the value of the probability distribution function.
36	+
37	+	Parameters
38	+	----------
39	+	x : float \| ArrayLike
40	+	The value(s) at which to compute the PDF.
41	+
42	+	Returns
43	+	-------
44	+	float \| ArrayLike
45	+	The value(s) of the PDF
46	+	"""
47	+	if not np.isscalar(x):
48	+	return np.vectorize(self.pdf, otypes=[float])(x)
49	+
50	+	num = np.sqrt(max(self.l_upper - x, 0.0) * max(x - self.l_bottom, 0.0))
51	+	den = 2.0 * np.pi * self.sigma*2 self.ratio * x
52	+
53	+	return float(num / den) if den != 0.0 else 0.0

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