3. Convert NumPy Matrix to Array Using ravel() Thenumpy.ravel()function is used to create a contiguous flattened array from a given input array. This function returns a flattened one-dimensional array, meaning it collapses the input array into a flat, contiguous sequence. 3.1 Syntax of ravel(...
Convert byte array back to NumPy arrayIn numpy, we can convert a numpy array to bytes using tobytes() function. To convert it back into a numpy array, we use numpy.frombuffer().For this purpose, we will first create a numpy array and convert it into a byte array using tobytes() ...
Python code to convert map object to NumPy array# Import numpy import numpy as np # performing some operation f = lambda x: x**2 # Creating a map object seq = map(f, range(5)) # Display map object print("Map object:\n",seq,"\n") # Converting map object into numpy array arr ...
"array" "array" "sparse"/"sparse_csr" "array" "matrix" "sparse_csc" "array" "matrix" "csc" "sparse_csr_array" "array" "array" "sparse_csc_array" "array" "array" "csc" "dataframe"/"pandas" "dataframe" "polars" "dataframe" "polars" "pyarrow" "dataframe" "pyarrow" "ser...
sparse import identity as sparse_identity from qiskit_dynamics.arraylias.alias import ArrayLike, _to_dense, _numpy_multi_dispatch def _kron(A, B): return _numpy_multi_dispatch(A, B, path="kron") def vec_commutator( A: Union[ArrayLike, csr_matrix, List[csr_matrix]] ) -> Union[Array...
'''# dump to file as adjacency MatrixA = nx.adjacency_matrix(D, nodelist=list(range(len(D.nodes)))# nx.adjacency_matrix(D, nodelist=None, weight='weight') # Return type: SciPy sparse matrix# print(A) # type < SciPy sparse matrix >A_dense = A.todense()# type-> numpy.matrixlib...
def__init__(self,example_indices,feature_indices,feature_values):"""Creates a `SparseFeatureColumn` representation. Args: example_indices: A 1-D int64 tensor of shape `[N]`. Also, accepts python lists, or numpy arrays. feature_indices: A 1-D int64 tensor of shape `[N]`. Also, accep...
from numpy import array, sqrt, real, imag, pi from math import asin from scipy.sparse import dok_matrix, hstack from collections import defaultdict, deque from loadcase import load_case def build_U_matrices(G, B): S2 = sqrt(2) n = G.shape[0] Ureal = dok_matri...
from scipy.sparse import csr_matrix A = csr_matrix([[1,0,2],[0,3,0]]) >>>A <2x3 sparse matrix of type '<type 'numpy.int64'>' with 3 stored elements in Compressed Sparse Row format> >>> A.todense() matrix([[1, 0, 2], [0, 3, 0]]) >>> A.toarr...
trainMatrix: String, params: Map[String, Any], rabitEnv: Option[java.util.Map[String, String]], numRounds: Int, earlyStoppingRound: Int = 0 ): RDD[(Array[Byte], Map[String, Array[Float]])] = - rdds.mapPartitions({ rows=> + rdd.mapPartitions({ rows=> // XGBoost refuses to load...