From 0dfd058a8bf3561a21c04004c8dbbc254ad87995 Mon Sep 17 00:00:00 2001
From: Andrea Del Prete <andre.delprete@gmail.com>
Date: Wed, 20 Oct 2021 11:44:08 -0700
Subject: [PATCH] [python] Update python tests to use np array instead of
matrix
---
tests/python/test_Constraint.py | 16 ++++++++--------
tests/python/test_Solvers.py | 34 ++++++++++++++-------------------
2 files changed, 22 insertions(+), 28 deletions(-)
diff --git a/tests/python/test_Constraint.py b/tests/python/test_Constraint.py
index 89c567e..db6dcac 100644
--- a/tests/python/test_Constraint.py
+++ b/tests/python/test_Constraint.py
@@ -7,12 +7,12 @@ print("")
print("Test Constraint Bound")
print("")
-se3.switchToNumpyMatrix()
+#se3.switchToNumpyMatrix()
tol = 1e-5
n = 5
-lb = np.matrix(-1.0 * np.ones(n)).transpose()
-ub = np.matrix(np.ones(n)).transpose()
+lb = -1.0 * np.ones(n)
+ub = np.ones(n)
ConstBound = tsid.ConstraintBound("bounds", lb, ub)
assert ConstBound.isBound
@@ -41,8 +41,8 @@ print("Test Constraint Equality")
print("")
n = 5
m = 2
-A = np.matrix(np.ones((m, n)))
-b = np.matrix(np.ones(m)).transpose()
+A = np.ones((m, n))
+b = np.ones(m)
equality = tsid.ConstraintEquality("equality", A, b)
assert not equality.isBound
@@ -71,9 +71,9 @@ print("")
n = 5
m = 2
-A = np.matrix(np.ones((m, n)))
-lb = np.matrix(-np.ones(m)).transpose()
-ub = np.matrix(np.ones(m)).transpose()
+A = np.ones((m, n))
+lb = -np.ones(m)
+ub = np.ones(m)
inequality = tsid.ConstraintInequality("inequality", A, lb, ub)
assert not inequality.isBound
diff --git a/tests/python/test_Solvers.py b/tests/python/test_Solvers.py
index 50668d7..60fdc2e 100644
--- a/tests/python/test_Solvers.py
+++ b/tests/python/test_Solvers.py
@@ -1,16 +1,10 @@
-import copy
-
import numpy as np
-import pinocchio as se3
-
import tsid
print("")
print("Test Solvers")
print("")
-se3.switchToNumpyMatrix()
-
EPS = 1e-3
nTest = 100
n = 60
@@ -29,15 +23,15 @@ solver = tsid.SolverHQuadProg("qp solver")
solver.resize(n, neq, nin)
HQPData = tsid.HQPData()
-A1 = np.matrix(np.random.rand(n, n)) + 0.001 * np.matrix(np.eye(n))
-b1 = np.matrix(np.random.rand(n)).transpose()
+A1 = np.random.rand(n, n) + 0.001 * np.eye(n)
+b1 = np.random.rand(n)
cost = tsid.ConstraintEquality("c1", A1, b1)
-x = np.linalg.inv(A1) * b1
-A_in = np.matrix(np.random.rand(nin, n))
-A_lb = np.matrix(np.random.rand(nin)).transpose() * NORMAL_DISTR_VAR
-A_ub = np.matrix(np.random.rand(nin)).transpose() * NORMAL_DISTR_VAR
-constrVal = A_in * x
+x = np.linalg.inv(A1) @ b1
+A_in = np.random.rand(nin, n)
+A_lb = np.random.rand(nin) * NORMAL_DISTR_VAR
+A_ub = np.random.rand(nin) * NORMAL_DISTR_VAR
+constrVal = A_in @ x
for i in range(0, nin):
if A_ub[i] <= A_lb[i]:
@@ -50,8 +44,8 @@ for i in range(0, nin):
A_lb[i] = constrVal[i] - MARGIN_PERC * np.abs(constrVal[i])
in_const = tsid.ConstraintInequality("ini1", A_in, A_lb, A_ub)
-A_eq = np.matrix(np.random.rand(neq, n))
-b_eq = A_eq * x
+A_eq = np.random.rand(neq, n)
+b_eq = A_eq @ x
eq_const = tsid.ConstraintEquality("eq1", A_eq, b_eq)
const1 = tsid.ConstraintLevel()
@@ -73,8 +67,8 @@ HQPData.print_all()
gradientPerturbations = []
hessianPerturbations = []
for i in range(0, nTest):
- gradientPerturbations.append(np.matrix(np.random.rand(n) * GRADIENT_PERTURBATION_VARIANCE).transpose())
- hessianPerturbations.append(np.matrix(np.random.rand(n, n) * HESSIAN_PERTURBATION_VARIANCE))
+ gradientPerturbations.append(np.random.rand(n) * GRADIENT_PERTURBATION_VARIANCE)
+ hessianPerturbations.append(np.random.rand(n, n) * HESSIAN_PERTURBATION_VARIANCE)
for i in range(0, nTest):
cost.setMatrix(cost.matrix + hessianPerturbations[i])
@@ -82,6 +76,6 @@ for i in range(0, nTest):
HQPoutput = solver.solve(HQPData)
- assert np.linalg.norm(A_eq * HQPoutput.x - b_eq, 2) < EPS
- # assert (A_in * HQPoutput.x <= A_ub + EPS).all()
- # assert (A_in * HQPoutput.x > A_lb - EPS).all()
+ assert np.linalg.norm(A_eq @ HQPoutput.x - b_eq, 2) < EPS
+ # assert (A_in @ HQPoutput.x <= A_ub + EPS).all()
+ # assert (A_in @ HQPoutput.x > A_lb - EPS).all()
--
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