From ae4c379f66c42c1e48564ea1bd85aafe6394abff Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Mon, 9 Mar 2020 22:26:49 -0700
Subject: Match CM timings to LJM
---
ml_exp/representations.py | 59 ++++++++++++++++++-----------------------------
1 file changed, 22 insertions(+), 37 deletions(-)
diff --git a/ml_exp/representations.py b/ml_exp/representations.py
index 51ed309b9..ab2dcdd21 100644
--- a/ml_exp/representations.py
+++ b/ml_exp/representations.py
@@ -112,50 +112,35 @@ size. Arrays are not of the right shape.')
'instead of (size).')
size = n
- lj = np.zeros((size, size), dtype=np.float64)
+ lj = np.zeros((n, n), dtype=np.float64)
# Actual calculation of the lennard-jones matrix.
- for i, xyz_i in enumerate(coords):
- for j, xyz_j in enumerate(coords):
- if i == j:
- if diag_value is None:
- lj[i, j] = (0.5*nc[i]**2.4)
- else:
- lj[i, j] = diag_value
- else:
- # Calculations are done after i==j is checked
- # so no division by zero is done.
-
- # A little play with r exponents
- # so no square root is calculated.
- # Conversion factor is included in r^2.
- rv = xyz_i - xyz_j
- r = np.linalg.norm(rv)/cr
-
- # 1/r^n
- r_2 = sigma**2/r**2
- r_6 = r_2**3
- r_12 = r_6**2
- lj[i, j] = (4*epsilon*(r_12 - r_6))
+ for i in range(n):
+ if diag_value is None:
+ lj[i, i] = 0.5*nc[i]**2.4
+ else:
+ lj[i, i] = diag_value
- # Now the value will be returned.
- if as_eig:
- lj_sorted = np.sort(np.linalg.eig(lj)[0])[::-1]
- # Thanks to SO for the following lines of code.
- # https://stackoverflow.com/a/43011036
+ # Calculates the values row-wise for faster timings.
+ # Don't calculate the last element (it's only the diagonal element).
+ for i in range(n - 1):
+ rv = coords[i + 1:] - coords[i]
+ r = (sigma*cr)/np.linalg.norm(rv, axis=1)
- # Keep zeros at the end.
- mask = lj_sorted != 0.
- f_mask = mask.sum(0, keepdims=1) >\
- np.arange(lj_sorted.shape[0]-1, -1, -1)
+ # 1/r^n
+ r_6 = r**6
+ r_12 = r**12
+ val = (4*epsilon*(r_12 - r_6))
+ lj[i, i + 1:] = val
+ lj[i + 1:, i] = val
- f_mask = f_mask[::-1]
- lj_sorted[f_mask] = lj_sorted[mask]
- lj_sorted[~f_mask] = 0.
+ # Now the value will be returned.
+ if as_eig:
+ lj_eigs = np.sort(np.linalg.eig(lj)[0])[::-1]
- return lj_sorted
+ return np.pad(lj_eigs, (0, size - n), 'constant')
else:
- return lj
+ return np.pad(lj, ((0, size - n), (0, size - n)), 'constant')
def get_helping_data(coords,
--
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