MP Distribution 的修订

riccardo 修订了这个 Gist 11 months ago. 转到此修订

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 修订了这个 Gist 11 months ago. 转到此修订

没有任何变更

riccardo 修订了这个 Gist 1 year ago. 转到此修订

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 修订了这个 Gist 1 year ago. 转到此修订

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 修订了这个 Gist 1 year ago. 转到此修订

1 file changed, 53 insertions

mp_distr.py(文件已创建)

		@@ -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