element-wise mul的pyton实现

在Python中，可以使用numpy库来实现element-wise multiplication。具体的实现方式如下：

import numpy as np

# 生成两个数组
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

# 对两个数组进行element-wise multiplication
c = np.multiply(a, b)

print(c) # 输出 [ 4 10 18]

其中，`np.multiply()`函数可以对两个数组进行element-wise multiplication操作。

相关问题

KP-ABE算法java实现

以下是KP-ABE算法的Java实现示例：

import it.unisa.dia.gas.jpbc.Element;
import it.unisa.dia.gas.jpbc.Pairing;
import it.unisa.dia.gas.jpbc.PairingFactory;
import it.unisa.dia.gas.jpbc.Field;
import it.unisa.dia.gas.jpbc.Field.ElementPowPreProcessing;
import it.unisa.dia.gas.plaf.jpbc.pairing.a.TypeACurveGenerator;
import it.unisa.dia.gas.plaf.jpbc.pairing.parameters.PropertiesParameters;
import it.unisa.dia.gas.plaf.jpbc.pairing.parameters.PropertiesParametersGenerator;
import it.unisa.dia.gas.plaf.jpbc.util.ElementUtils;
import java.util.ArrayList;import java.util.HashMap;
import java.util.Map;

public class KPABE {
    private Pairing pairing;
    private Field G1, GT, Zr;
    private Element g, h, f;
    private Map<String, Element> D;
    private Map<String, ElementPowPreProcessing> D_pre;
    private Map<String, Element> D1;
    private Map<String, ElementPowPreProcessing> D1_pre;

    public KPABE() {
        PropertiesParametersGenerator pg = new PropertiesParametersGenerator();
        pg.init(new TypeACurveGenerator(3, 32));
        PropertiesParameters params = pg.generate();
        this.pairing = PairingFactory.getPairing(params);
        this.G1 = pairing.getG1();
        this.GT = pairing.getGT();
        this.Zr = pairing.getZr();
        this.g = G1.newRandomElement().getImmutable();
        this.h = G1.newRandomElement().getImmutable();
        this.f = GT.newRandomElement().getImmutable();
        this.D = new HashMap<String, Element>();
        this.D_pre = new HashMap<String, ElementPowPreProcessing>();
        this.D1 = new HashMap<String, Element>();
        this.D1_pre = new HashMap<String, ElementPowPreProcessing>();
    }

    public void setup(int n) {
        Element alpha = Zr.newRandomElement().getImmutable();
        Element beta = Zr.newRandomElement().getImmutable();
        Element g_alpha = g.powZn(alpha).getImmutable();
        Element h_beta = h.powZn(beta).getImmutable();
        Element f_alpha = f.powZn(alpha).getImmutable();
        D.put("g_alpha", g_alpha);
        D_pre.put("g_alpha", G1.newElement().set(g_alpha).getImmutable().getElementPowPreProcessing());
        D.put("h_beta", h_beta);
        D_pre.put("h_beta", G1.newElement().set(h_beta).getImmutable().getElementPowPreProcessing());
        D.put("f_alpha", f_alpha);
        D_pre.put("f_alpha", GT.newElement().set(f_alpha).getImmutable().getElementPowPreProcessing());
        for (int i = 1; i <= n; i++) {
            Element r = Zr.newRandomElement().getImmutable();
            Element g_r = g.powZn(r).getImmutable();
            Element h_r = h.powZn(r).getImmutable();
            D.put("g_" + i, g_r);
            D_pre.put("g_" + i, G1.newElement().set(g_r).getImmutable().getElementPowPreProcessing());
            D.put("h_" + i, h_r);
            D_pre.put("h_" + i, G1.newElement().set(h_r).getImmutable().getElementPowPreProcessing());
            Element D1_i = g_alpha.mul(h_beta).mul(g_r).getImmutable();
            D1.put("D1_" + i, D1_i);
            D1_pre.put("D1_" + i, G1.newElement().set(D1_i).getImmutable().getElementPowPreProcessing());
        }
    }

    public Map<String, Element> keygen(String[] attrs) {
        Map<String, Element> sk = new HashMap<String, Element>();
        Element r = Zr.newRandomElement().getImmutable();
        Element D2 = g.powZn(r).getImmutable();
        sk.put("D2", D2);
        for (String attr : attrs) {
            Element r_attr = Zr.newRandomElement().getImmutable();
            Element D3_attr = g.powZn(r_attr).getImmutable();
            Element D4_attr = D.get("h_" + attr).powZn(r).mul(h).powZn(r_attr).getImmutable();
            sk.put("D3_" + attr, D3_attr);
            sk.put("D4_" + attr, D4_attr);
        }
        return sk;
    }

    public Element encrypt(String[] attrs, String message) {
        Element s = Zr.newRandomElement().getImmutable();
        Element C0 = f.mul(pairing.pairing(g, h.powZn(s))).getImmutable();
        Element C1 = g.powZn(s).getImmutable();
        ArrayList<Element> C2_list = new ArrayList<Element>();
        for (String attr : attrs) {
            Element C2_attr = D.get("g_" + attr).powZn(s).getImmutable();
            C2_list.add(C2_attr);
        }
        Element C2 = ElementUtils.getProdOfElements(C2_list).getImmutable();
        Element C3 = GT.newElement().setToHash(message.getBytes(), 0, message.getBytes().length).powZn(s).getImmutable();
        return pairing.getGT().newElement().setToCipherText(C0, C1, C2, C3).getImmutable();
    }

    public Element decrypt(Map<String, Element> sk, Element ct) {
        Element D2 = sk.get("D2");
        ArrayList<Element> D3_list = new ArrayList<Element>();
        ArrayList<Element> D4_list = new ArrayList<Element>();
        for (String attr : sk.keySet()) {
            if (attr.startsWith("D3_")) {
                D3_list.add(sk.get(attr));
            } else if (attr.startsWith("D4_")) {
                D4_list.add(sk.get(attr));
            }
        }
        Element numerator = pairing.pairing(ct.get(1), D2).getImmutable();
        ArrayList<Element> denominator_list = new ArrayList<Element>();
        for (int i = 0; i < D3_list.size(); i++) {
            Element D3_i = D3_list.get(i);
            Element D4_i = D4_list.get(i);
            Element C2_i = ct.get(2).powZn(D3_i).mul(D4_i).getImmutable();
            denominator_list.add(pairing.pairing(D_pre.get("h_" + i), C2_i));
        }
        Element denominator = ElementUtils.getProdOfElements(denominator_list).getImmutable();
        return ct.get(3).mul(numerator.div(denominator)).getImmutable();
    }

    public static void main(String[] args) {
        KPABE kpabe = new KPABE();
        kpabe.setup(5);
        Map<String, Element> sk = kpabe.keygen(new String[]{"1", "2", "3"});
        Element ct = kpabe.encrypt(new String[]{"1", "2"}, "hello world");
        Element m = kpabe.decrypt(sk, ct);
        System.out.println(m);
    }
}

L-SGD算法pytor实现

L-SGD（Layer-wise SGD）算法是一种用于深度神经网络训练的优化算法，其思想是将神经网络中的每一层看作一个单独的模型，使用SGD算法对每一层的模型进行优化。这种方法可以加速模型训练，提高模型的泛化能力。

下面是使用PyTorch实现L-SGD算法的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

class LayerwiseSGD(optim.Optimizer):
    def __init__(self, params, lr=0.1, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False):
        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
                        weight_decay=weight_decay, nesterov=nesterov)
        super(LayerwiseSGD, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(LayerwiseSGD, self).__setstate__(state)

    @torch.no_grad()
    def step(self, closure=None):
        loss = None
        for group in self.param_groups:
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']
            weight_decay = group['weight_decay']

            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad

                if weight_decay != 0:
                    d_p.add_(weight_decay, p)

                if momentum != 0:
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        buf = param_state['momentum_buffer'] = torch.zeros_like(p.grad)
                        buf.mul_(momentum).add_(d_p)
                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(1 - dampening, d_p)
                    if nesterov:
                        d_p = d_p.add(momentum, buf)
                    else:
                        d_p = buf

                # update parameters
                p.add_(-group['lr'], d_p)

        return loss

在使用该算法时，只需要将优化器替换为上述代码中定义的LayerwiseSGD即可：

optimizer = LayerwiseSGD(model.parameters(), lr=0.1)

需要注意的是，L-SGD算法在训练深度神经网络时可能会遇到一些问题，如梯度消失和梯度爆炸等。因此，在实际使用中需要根据具体情况进行调整和优化。