vectorization

问题 I have a 3D numpy array of shape (t, n1, n2) : x = np.random.rand(10, 2, 4) I need to calculate another 3D array y which is of shape (t, n1, n1) such that: y[0] = np.cov(x[0,:,:]) ...and so on for all slices along the first axis. So, a loopy implementation would be: y = np.zeros((10,2,2)) for i in np.arange(x.shape[0]): y[i] = np.cov(x[i, :, :]) Is there any way to vectorize this so I can calculate all covariance matrices in one go? I tried doing: x1 = x.swapaxes(1, 2) y = np.dot(x, x1) But

问题 This code compares based on condition the var1 and var2 and creates Results1 based on choices (this code works well): # from: https://stackoverflow.com/questions/27474921/compare-two-columns-using-pandas?answertab=oldest#tab-top # from: https://stackoverflow.com/questions/60099141/negation-in-np-select-condition import pandas as pd import numpy as np # Creating one column from two columns. We asume that in every row there is one NaN and one value and that value fills new column. df = pd

问题 I'm trying to use the Java Vector API from the Panama project to add some SIMD code to the java.math.BigInteger class. I cloned the Panama repo and built a JDK: hg clone http://hg.openjdk.java.net/panama/dev/ cd dev/ hg checkout vectorIntrinsics hg branch vectorIntrinsics bash configure make images I was able to compile and run a simple little program that uses the vector API: import static jdk.incubator.vector.Vector.Shape.S_256_BIT; import jdk.incubator.vector.IntVector; import static jdk

问题 I'm trying to use the Java Vector API from the Panama project to add some SIMD code to the java.math.BigInteger class. I cloned the Panama repo and built a JDK: hg clone http://hg.openjdk.java.net/panama/dev/ cd dev/ hg checkout vectorIntrinsics hg branch vectorIntrinsics bash configure make images I was able to compile and run a simple little program that uses the vector API: import static jdk.incubator.vector.Vector.Shape.S_256_BIT; import jdk.incubator.vector.IntVector; import static jdk

问题 I have this functions, the result is correct but the compiler don't vectorize this. How can I achive that the compiler vectorize this and how can I optimize this codes? void LongNumSet( char *L, unsigned N, char digit ) { for (int i = 0; i < N; ++i){ L[i] = digit; } } void LongNumCopy( char *Vin, char *Vout, unsigned N ) { for ( int i=0; i< N; ++i ) { Vout[i] = Vin[i]; } } char LongNumAddition( char *__restrict Vin1, char * __restrict Vin2, char * __restrict Vout, unsigned N ) { char CARRY =

问题 I have this functions, the result is correct but the compiler don't vectorize this. How can I achive that the compiler vectorize this and how can I optimize this codes? void LongNumSet( char *L, unsigned N, char digit ) { for (int i = 0; i < N; ++i){ L[i] = digit; } } void LongNumCopy( char *Vin, char *Vout, unsigned N ) { for ( int i=0; i< N; ++i ) { Vout[i] = Vin[i]; } } char LongNumAddition( char *__restrict Vin1, char * __restrict Vin2, char * __restrict Vout, unsigned N ) { char CARRY =

问题 I have two inputs in a dataframe, and I need to create an output that depends on both inputs (same row, different columns), but also on its previous value (same column, previous row). This dataframe command will create an example of what I need: df=pd.DataFrame([[0,0,0], [0,1,0], [0,0,0], [1,1,1], [0,1,1], [0,1,1], [0,0,0], [0,1,0], [0,1,0], [1,1,1], [1,1,1], [0,1,1], [0,1,1], [1,1,1], [0,1,1], [0,1,1], [0,0,0], [0,1,0]], columns=['input_1', 'input_2', 'output']) The rules are simple: If

问题 I have two inputs in a dataframe, and I need to create an output that depends on both inputs (same row, different columns), but also on its previous value (same column, previous row). This dataframe command will create an example of what I need: df=pd.DataFrame([[0,0,0], [0,1,0], [0,0,0], [1,1,1], [0,1,1], [0,1,1], [0,0,0], [0,1,0], [0,1,0], [1,1,1], [1,1,1], [0,1,1], [0,1,1], [1,1,1], [0,1,1], [0,1,1], [0,0,0], [0,1,0]], columns=['input_1', 'input_2', 'output']) The rules are simple: If

问题 This question already has answers here : SSE/SIMD shift with one-byte element size / granularity? (2 answers) How do I vectorize data_i16[0 to 15]? (1 answer) Closed 3 days ago . I'm trying to implement the strategy described in an answer to How do I vectorize data_i16[0 to 15]? Code below. The spot I'd like to fix is the for(int i=0; i<ALIGN; i++) loop I'm new to SIMD. From what I can tell I'd load the high/low nibble table by writing const auto HI_TBL = _mm_load_si128((__m128i*)HighNibble)

问题 I am not sure if this has been asked before. I couldn't find many relevant results on SO or Google. Anyway, here is what I am trying to do. I have a function that is created at runtime which takes in 4 parameters. my_func(t, x, p, a) t is a scalar float. x is a 1D numpy array of floats. p and a are dictionaries. I have a numpy array T and a 2D numpy array X. I want to call the function with each element of T and each column of X and store the result in another numpy array. The function