def test_mult_dimensions(self): tempa = [[1,1],[2,2],[3,3]] tempb = [[3,3,3],[2,2,2]] tempc = [[5,5,5],[10,10,10],[15, 15, 15]] self.a = matlib.matrix(tempa) self.b = matlib.matrix(tempb) self.c = matlib.matrix(tempc) self.assertTrue(numpy.array_...
In my case, the issue manifested itself as an unexpectedly high error in the associativity of matrix multiplication, as demonstrated by this example script: import numpy as np import jax import jax.numpy as jnp def dev_info(): dev = jax.devices()[0] info = "CPU" if dev.platform == ...
PYthon For Homomorphic Encryption Libraries, perform encrypted computations such as sum, mult, scalar product or matrix multiplication in Python, with NumPy compatibility. Uses SEAL/PALISADE as backends, implemented using Cython. - ibarrond/Pyfhel
matrix = mult_matrix(a, p_***) Convert matrix elements to float in python, a = np.array ( [ [1.0,2.0,3.0,4.0], [5.0,6.0,7.0,8.0]]) the previous matrices are examples, the main idea is convert strings which inner concept values are numbers. Direct convertion won't work: a = [...
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(number of columns of A and B matrices) l2_reg : Regularization pameter for the L2 norm of the A and B matrices l1_reg : Regularization pameter for the L1 norm of the A and B matrices w_mult : Weight multiplier for the positive entries in X step_size : Initial step size for PGD...
Python Integration • CUV plays well with python and numpy. That is, once you wrote your fast GPU functions in CUDA/C++, you can export them using Boost.Python. You can use Numpy for pre-processing and fancy stuff you have not yet implemented, then push the Numpy-matrix to the GPU, ...
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pfigutil.py plotwrap.py tens.py valunits.py scripts share test .gitattributes .gitignore MANIFEST.in README.md setup.cfg setup.py versioneer.py Latest commit Bernier typo fix in matrixutil Aug 18, 2017 0dd830a·Aug 18, 2017 History History...
numpy pandas Example to run the codes Train and evaluate the model: python ENMF.py Suggestions for parameters Two important parameters need to be tuned for different datasets, which are: parser.add_argument('--dropout', type=float, default=0.7, help='dropout keep_prob') parser.add_argument(...