From 53c84552df5c7a7e1f084b0aec8eac63f7813e52 Mon Sep 17 00:00:00 2001
From: David Luevano Alvarado <55825613+luevano@users.noreply.github.com>
Date: Sat, 29 Feb 2020 11:45:17 -0700
Subject: Change dtypes to np dtypes
---
ml_exp/compound.py | 8 ++++----
ml_exp/do_ml.py | 8 ++++----
ml_exp/math.py | 4 ++--
ml_exp/qm7db.py | 8 ++++----
ml_exp/representations.py | 10 +++++-----
5 files changed, 19 insertions(+), 19 deletions(-)
diff --git a/ml_exp/compound.py b/ml_exp/compound.py
index e2b7ab7fd..4428e9164 100644
--- a/ml_exp/compound.py
+++ b/ml_exp/compound.py
@@ -153,15 +153,15 @@ class Compound:
lines = f.readlines()
self.name = filename.split('/')[-1]
- self.n = int(lines[0])
+ self.n = np.int32(lines[0])
self.extra = lines[1]
self.atoms = []
- self.atoms_nc = np.empty(self.n, dtype=int)
- self.coordinates = np.empty((self.n, 3), dtype=float)
+ self.atoms_nc = np.empty(self.n, dtype=np.int64)
+ self.coordinates = np.empty((self.n, 3), dtype=np.float64)
for i, atom in enumerate(lines[2:self.n + 2]):
atom_d = atom.split()
self.atoms.append(atom_d[0])
self.atoms_nc[i] = NUCLEAR_CHARGE[atom_d[0]]
- self.coordinates[i] = np.asarray(atom_d[1:4], dtype=float)
+ self.coordinates[i] = np.asarray(atom_d[1:4], dtype=np.float64)
diff --git a/ml_exp/do_ml.py b/ml_exp/do_ml.py
index db50cc8fa..04f8abe87 100644
--- a/ml_exp/do_ml.py
+++ b/ml_exp/do_ml.py
@@ -186,13 +186,13 @@ def do_ml(db_path='data',
# Create a numpy array for the descriptors.
if 'CM' in identifiers:
- cm_data = np.array([comp.cm for comp in compounds], dtype=float)
+ cm_data = np.array([comp.cm for comp in compounds], dtype=np.float64)
if 'LJM' in identifiers:
- ljm_data = np.array([comp.ljm for comp in compounds], dtype=float)
+ ljm_data = np.array([comp.ljm for comp in compounds], dtype=np.float64)
if 'AM' in identifiers:
- am_data = np.array([comp.cm for comp in compounds], dtype=float)
+ am_data = np.array([comp.cm for comp in compounds], dtype=np.float64)
if 'BOB' in identifiers:
- bob_data = np.array([comp.bob for comp in compounds], dtype=float)
+ bob_data = np.array([comp.bob for comp in compounds], dtype=np.float64)
toc = time.perf_counter()
tictoc = toc - tic
diff --git a/ml_exp/math.py b/ml_exp/math.py
index da1fdf08f..253abe5d2 100644
--- a/ml_exp/math.py
+++ b/ml_exp/math.py
@@ -39,7 +39,7 @@ def cholesky_solve(K,
size = K.shape[0]
# Solve Lx=y for x.
- x = np.zeros(size, dtype=float)
+ x = np.zeros(size, dtype=np.float64)
x[0] = y[0] / L[0, 0]
for i in range(1, size):
temp_sum = 0.0
@@ -49,7 +49,7 @@ def cholesky_solve(K,
# Now, solve LTa=x for a.
L2 = L.T
- a = np.zeros(size, dtype=float)
+ a = np.zeros(size, dtype=np.float64)
a[size - 1] = x[size - 1] / L2[size - 1, size - 1]
for i in range(0, size - 1)[::-1]:
temp_sum = 0.0
diff --git a/ml_exp/qm7db.py b/ml_exp/qm7db.py
index 3fd46b6bc..3ba2c5814 100644
--- a/ml_exp/qm7db.py
+++ b/ml_exp/qm7db.py
@@ -42,14 +42,14 @@ def qm7db(db_path='data',
for i, line in enumerate(lines):
line = line.split()
compounds.append(Compound(f'{db_path}/{line[0]}'))
- compounds[i].pbe0 = float(line[1])
- compounds[i].delta = float(line[1]) - float(line[2])
+ compounds[i].pbe0 = np.float64(line[1])
+ compounds[i].delta = np.float64(line[1]) - np.float64(line[2])
if is_shuffled:
random.seed(r_seed)
random.shuffle(compounds)
- e_pbe0 = np.array([compound.pbe0 for compound in compounds], dtype=float)
- e_delta = np.array([compound.delta for compound in compounds], dtype=float)
+ e_pbe0 = np.array([comp.pbe0 for comp in compounds], dtype=np.float64)
+ e_delta = np.array([comp.delta for comp in compounds], dtype=np.float64)
return compounds, e_pbe0, e_delta
diff --git a/ml_exp/representations.py b/ml_exp/representations.py
index 5e99627df..31259c79c 100644
--- a/ml_exp/representations.py
+++ b/ml_exp/representations.py
@@ -53,7 +53,7 @@ size. Arrays are not of the right shape.')
'instead of (size).')
size = n
- cm = np.zeros((size, size), dtype=float)
+ cm = np.zeros((size, size), dtype=np.float64)
# Actual calculation of the coulomb matrix.
for i, xyz_i in enumerate(coords):
@@ -120,7 +120,7 @@ size. Arrays are not of the right shape.')
'instead of (size).')
size = n
- lj = np.zeros((size, size), dtype=float)
+ lj = np.zeros((size, size), dtype=np.float64)
# Actual calculation of the lennard-jones matrix.
for i, xyz_i in enumerate(coords):
@@ -259,7 +259,7 @@ def adjacency_matrix(fnm,
instead of (size).')
size = n
- am = np.zeros((size, size), dtype=float)
+ am = np.zeros((size, size), dtype=np.float64)
for i, bond_i in enumerate(bonds):
for j, bond_j in enumerate(bonds):
@@ -317,7 +317,7 @@ generated as the vector of eigenvalues, try (re-)generating the CM.')
size = n
# Bond max length, calculated using only the upper triangular matrix.
- bond_size = int((size * size - size)/2 + size)
+ bond_size = np.int32((size * size - size)/2 + size)
# List where each bag data is stored.
bags = []
@@ -350,7 +350,7 @@ generated as the vector of eigenvalues, try (re-)generating the CM.')
bonds = atom_list + bonds
# Create the final vector for the bob.
- bob = np.zeros(bond_size, dtype=float)
+ bob = np.zeros(bond_size, dtype=np.float64)
c_i = 0
for i, bond in enumerate(bonds):
checker = check_bond(bags, bond)
--
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