平行因子分析的代码
时间: 2023-07-02 14:19:59 浏览: 96
以下是平行因子分析的代码示例:
```python
import numpy as np
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
# Generate data
np.random.seed(1234)
A = np.random.rand(100, 50)
B = np.random.rand(70, 50)
C = np.random.rand(50, 30)
# Initialize factors randomly
np.random.seed(rank)
X = np.random.rand(100, 10)
Y = np.random.rand(70, 10)
Z = np.random.rand(30, 10)
# Define number of iterations and learning rate
n_iter = 100
lr = 0.01
# Main loop
for i in range(n_iter):
# Compute local gradients
grad_X = np.dot(np.dot(X, Y.T), Z) - np.dot(np.dot(A, Y), Z)
grad_Y = np.dot(np.dot(Y, X.T), Z) - np.dot(np.dot(B, X), Z)
grad_Z = np.dot(np.dot(Z, Y.T), X) - np.dot(np.dot(C, Y), X)
# Sum gradients across processes
comm.Allreduce(MPI.IN_PLACE, grad_X, op=MPI.SUM)
comm.Allreduce(MPI.IN_PLACE, grad_Y, op=MPI.SUM)
comm.Allreduce(MPI.IN_PLACE, grad_Z, op=MPI.SUM)
# Update factors
X -= lr * grad_X
Y -= lr * grad_Y
Z -= lr * grad_Z
```
该代码使用MPI并行化实现了平行因子分析,其中`A`、`B`和`C`是输入数据,`X`、`Y`和`Z`是要学习的因子,`n_iter`是迭代次数,`lr`是学习率。在每个迭代中,每个进程计算其本地梯度,然后使用MPI Allreduce操作将所有进程的梯度相加,最后更新因子。
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