From aeb10e18810f8426f0017d5727c9af1a848a142c Mon Sep 17 00:00:00 2001 From: andersx Date: Sat, 19 Jan 2019 11:47:50 +0100 Subject: [PATCH 1/2] Fixed error and warnings caused by latest numpy 1.16.0 --- qml/fchl/fchl_kernel_functions.py | 2 +- setup.py | 2 +- test/test_fchl_scalar.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/qml/fchl/fchl_kernel_functions.py b/qml/fchl/fchl_kernel_functions.py index 0b90ac563..4306f8a6f 100644 --- a/qml/fchl/fchl_kernel_functions.py +++ b/qml/fchl/fchl_kernel_functions.py @@ -28,7 +28,7 @@ import scipy from scipy.special import binom -from scipy.misc import factorial +from scipy.special import factorial def get_gaussian_parameters(tags): diff --git a/setup.py b/setup.py index c33bbfde6..3df9d79bd 100755 --- a/setup.py +++ b/setup.py @@ -26,7 +26,7 @@ # UNCOMMENT TO FORCE LINKING TO MKL with GNU compilers: if mkl_exists(verbose=True): - LINKER_FLAGS = ["-lgomp", " -lpthread", "-lm", "-ldl"] + LINKER_FLAGS = ["-lgomp", "-lpthread", "-lm", "-ldl"] MATH_LINKER_FLAGS = ["-L${MKLROOT}/lib/intel64", "-lmkl_rt"] # For clang without OpenMP: (i.e. most Apple/mac system) diff --git a/test/test_fchl_scalar.py b/test/test_fchl_scalar.py index 7b58d9321..03c3ffd52 100644 --- a/test/test_fchl_scalar.py +++ b/test/test_fchl_scalar.py @@ -29,7 +29,7 @@ import scipy from scipy.special import jn from scipy.special import binom -from scipy.misc import factorial +from scipy.special import factorial from qml.data import Compound From 4f5c0a67a7c7c2bb0d91af3791604cc0eca56bd1 Mon Sep 17 00:00:00 2001 From: andersx Date: Sat, 19 Jan 2019 11:55:51 +0100 Subject: [PATCH 2/2] Fixed non-pythonic character causing errors in Python2 --- qml/qmlearn/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qml/qmlearn/models.py b/qml/qmlearn/models.py index cc6dc7ca8..d4a0b93aa 100644 --- a/qml/qmlearn/models.py +++ b/qml/qmlearn/models.py @@ -345,7 +345,7 @@ def _remove_constant_features(self, representations): """ if self._representation_type == 'atomic': - # Due to how the atomic neural network iś constructed, + # Due to how the atomic neural network is constructed, # this cannot be done elementwise rep = representations.reshape(-1, representations.shape[-1]) if self._constant_features is None: