Final_Version_Scenario_1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJQRCPdqSKJ7"
      },
      "source": [
        "##Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "muEpFwvlSTxT"
      },
      "outputs": [],
      "source": [
        "from torch.cuda import device_of\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import math\n",
        "import time\n",
        "\n",
        "def set_general_params(n_0 = 1, p_s = 1, B = 4, K = 4, P = 4, N = 2, L = 2,\n",
        "                       T = 5,epochs=15, J=10,X_is_variable = False,\n",
        "                       W_is_phase_only = True, W_is_block_diag = True,\n",
        "                       train_size = 1500, batch_size = 20, pga_iters=400,\n",
        "                       optimizer_learning_rate=0.03):\n",
        "    \"\"\"\n",
        "    This function sets the general parameters of the network.\n",
        "     used in order to save space in the code when running multiple simulations.\n",
        "    :param n_0: noise power\n",
        "    :param p_s: signal power\n",
        "    :param B: number of frequency bins\n",
        "    :param K: number of users to be served\n",
        "    :param P: number of panels in the base station\n",
        "    :param N: number of antennas in each panel\n",
        "    :param L: number of outputs in each panel\n",
        "    :param T: number of inputs to the CPU\n",
        "    :param epochs: number of epochs in the training\n",
        "    :param J: number of iteration to be unfolded , usually 10.\n",
        "    :param X_is_variable: if True, X is a variable in the network. usually False.\n",
        "    :param W_is_phase_only: if True, W is a phase matrix. usually True.\n",
        "    :param W_is_block_diag: if True, W is a block diagonal matrix. usually True.\n",
        "    :param train_size: number of samples in the training set.\n",
        "    :param batch_size: batch size in the training part.\n",
        "    :param pga_iters: number of iterations in the PGA algorithm to be compared with. in order of 300-400.\n",
        "    :param optimizer_learning_rate: learning rate of the optimizer. denoted as eta in the paper.\n",
        "    :return: all the parameters.\n",
        "    \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    M = P * N  # Total number of antennas in the Base station\n",
        "    snr = p_s / n_0\n",
        "    T_opt = max(math.floor(M * (K - L) / K + 1), K)  # minimal number of inputs to the CPU that allows loss-less processing. as described in \"Trade-Offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition\".\n",
        "    if T==5: # build the A matrix using the example given in \"Trade-Offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition\"\n",
        "        A = torch.empty((L * P, T))\n",
        "        A[:T, :] = torch.eye(T)\n",
        "        A[T:, :3] = torch.eye(3)\n",
        "        A[T:, 3:] = 1\n",
        "        A = A.to(dtype=torch.cfloat).to(device)\n",
        "    if T == 61: # build the A matrix using the example given in \"Trade-Offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition\"\n",
        "        #Make sure that L*P = 100\n",
        "        A = torch.empty((100, 61))\n",
        "        A[ :61, :] = torch.eye(61)\n",
        "        A[61:, :39] = torch.eye(39)\n",
        "        A[61:83, 39:] = torch.eye(22)\n",
        "        A[83:, 39:56] = torch.eye(17)\n",
        "        A[83:88, 56:] = torch.eye(5)\n",
        "        A[88:93, 56:] = torch.eye(5)\n",
        "        A[93:98, 56:] = torch.eye(5)\n",
        "        A[98:, 56:58] = torch.eye(2)\n",
        "        A[98:, 58:60] = torch.eye(2)\n",
        "        A = A.to(dtype=torch.cfloat).to(device)\n",
        "    Ik = torch.eye(T, dtype=torch.cfloat).to(device)\n",
        "    Im = torch.eye(M, dtype=torch.cfloat).to(device)\n",
        "    D = torch.zeros((M, L * P)).to(device)\n",
        "    for p in range(P):\n",
        "        p_n, p_l = p * N, p * L\n",
        "        D[p_n:(p_n + N), p_l:(p_l + L)] = torch.ones((N, L))\n",
        "    return device, n_0, p_s, B, K, P, N, M, L, T, snr, T_opt, A, Ik, Im, D,J, epochs, W_is_block_diag, W_is_phase_only, X_is_variable, train_size, batch_size, pga_iters, optimizer_learning_rate\n",
        "\n",
        "\n",
        "def gen_data(seed = 43, train_size = 1500, batch_size = 20, valid_size = 250):\n",
        "    device = torch.device('cpu')  # generating all samples on the CPU for consistency\n",
        "    torch.manual_seed(seed)\n",
        "    H_train = torch.randn((train_size, B, M, K), dtype=torch.cfloat, device=device) # creating H_train in an i.i.d. manner\n",
        "    H_valid = torch.randn((valid_size, B, M, K), dtype=torch.cfloat, device=device) # creating H_valid in an i.i.d. manner\n",
        "    X = torch.eye(T, dtype=torch.cfloat)\n",
        "    W = torch.randn((M, L*P), dtype=torch.cfloat)\n",
        "\n",
        "    return H_train, H_valid, X, W\n",
        "\n",
        "\n",
        "def project_onto_block_diagonal(w,D):\n",
        "    \"\"\"\n",
        "    This function projects the matrix w onto the block diagonal matrix D.\n",
        "    :param w: the matrix to be projected\n",
        "    :param D: a predefined block diagonal matrix consisting of ones and zeros.\n",
        "    :return: the projection of w onto a block diagonal matrix.\n",
        "    \"\"\"\n",
        "    return w * D\n",
        "\n",
        "\n",
        "def project_onto_phases(w):\n",
        "    \"\"\"\n",
        "    This function projects the matrix w onto the phases of w. after projection, all entries of W has a unit magnitude.\n",
        "    :param w: the matrix to be projected\n",
        "    \"\"\"\n",
        "    W_phases = torch.exp(1j * torch.angle(w))\n",
        "    return W_phases\n",
        "\n",
        "\n",
        "class UnfoldedModel(nn.Module):\n",
        "    \"\"\"\n",
        "    Building the unfolded model. in our code, this model is used as a skeleton for the Unfolded + Momentum algorithm.\n",
        "    Significantly outperforms the PGA algorithm.\n",
        "    Reaches almost the same performance as the Unfolded + Momentum algorithm.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, Mu_W, Mu_X, J):\n",
        "        super().__init__()\n",
        "        self.Mu_W = Mu_W\n",
        "        self.Mu_X = Mu_X\n",
        "        self.J = J\n",
        "        self.D = D\n",
        "\n",
        "\n",
        "    def forward(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"\n",
        "        This function describes the flow of the unfolded model. Here, the values of Mu_W vary in each iteration.\n",
        "        :param H: a matrix of size BxMxK containing the channel realizations for each frequency bin.\n",
        "        :param W: the inital value of W. usually randomized. It is important not to take a scaled identity matrix as the initial value of W, since it harms the convergence of the algorithm.\n",
        "        :param X: the inital value of X. set to be identity matrix.\n",
        "        :param A: the initial value of A. predefined in the set_genral_params function.\n",
        "        :return: the list of rates obtained in each iteration, the the final value of W and X.\n",
        "        \"\"\"\n",
        "        W_1 = W.detach()\n",
        "        X_1 = X.detach()\n",
        "        Rs = torch.zeros(self.J, dtype=torch.cfloat).to(device)\n",
        "        # project W onto constraints:\n",
        "        if W_is_phase_only:\n",
        "            W_1 = project_onto_phases(W_1)\n",
        "        if W_is_block_diag:\n",
        "            W_1 = project_onto_block_diagonal(W_1, self.D)\n",
        "        # starting the iterations:\n",
        "        for j in range(self.J):\n",
        "            dR_dW = self.calc_dR_dW(H, W_1, X_1, A, p_s, n_0)\n",
        "            W_1 = W_1 + self.Mu_W[j] * dR_dW # making a gradient step\n",
        "            if X_is_variable:\n",
        "                dR_dX = self.calc_dR_dX(H, W_1, X_1, A, p_s, n_0)\n",
        "                X_1 = X_1 + self.Mu_X[j] * dR_dX\n",
        "            if W_is_phase_only: # projecting W onto constraints\n",
        "                W_1 = project_onto_phases(W_1)\n",
        "            if W_is_block_diag:\n",
        "                W_1 = project_onto_block_diagonal(W_1,self.D)\n",
        "            Rs[j] = self.calc_R(H, W_1, X_1, A, p_s, n_0)\n",
        "        return Rs, W_1, X_1\n",
        "\n",
        "\n",
        "    def calc_R(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"gets a channel realization H and returns the achievable rate of it. H is of dimension (B,M,K)\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = W @ A @ X\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            R += torch.log((Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()).det())\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_max_r(self, H):\n",
        "        \"\"\"calclulates the maximal rate of the channel H, susbstituting G to be the matched filter. not used in the algorithm\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = H # the matched filter is obtained when G = H^H\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B): # iterating over each frequency bin\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G[b, :, :]\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            R += torch.log((Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()).det())\n",
        "        return R/B # averaging over the frequency bins\n",
        "\n",
        "\n",
        "    def calc_dR_dW(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to W. the gradient has the same dimension as W\"\"\"\n",
        "        grad = 0\n",
        "        for b in range(B): #iterating over each frequency bin\n",
        "            h1 = H[b, :, :]\n",
        "            snr = p_s / n_0\n",
        "            X_con = X.conj()\n",
        "            W_con = W.conj()\n",
        "            H_con = h1.conj()\n",
        "            G = W @ A @ X\n",
        "            G_herm = G.transpose(0, 1).conj()\n",
        "            h1_herm = h1.transpose(0, 1).conj()\n",
        "            G_psu_inv = torch.inverse(G_herm @ G)\n",
        "            Proj_mat = G @ G_psu_inv @ G_herm\n",
        "            Z = snr * G_psu_inv @ G_herm @ h1 @ h1_herm @ G\n",
        "            dR_dZ = torch.inverse(Ik + Z)\n",
        "            grad += snr * (\n",
        "                        (A @ X) @ dR_dZ @ (2 * G_psu_inv @ G_herm) @ h1 @ h1_herm @ (Im - Proj_mat)).conj().transpose(0,                                                                                                       1)\n",
        "        return grad / B # averaging over the frequency bins\n",
        "\n",
        "\n",
        "    def calc_dR_dX(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to X. the gradient has the same dimension as X\"\"\"\n",
        "        \"\"\" not used in our code! \"\"\"\n",
        "        snr = p_s / n_0\n",
        "        X_con = X.conj()\n",
        "        W_con = W.conj()\n",
        "        H_con = H.conj()\n",
        "        G = W @ A @ X\n",
        "        G_herm = G.transpose(0, 1).conj()\n",
        "        H_herm = H.transpose(1, 2).conj()\n",
        "        G_psu_inv = torch.inverse(G_herm @ G)\n",
        "        Proj_mat = G @ G_psu_inv @ G_herm\n",
        "        Z = snr * G_psu_inv @ G_herm @ H @ H_herm @ G\n",
        "        dR_dZ = torch.inverse(Ik + Z)\n",
        "        return snr * (dR_dZ @ (2 * G_psu_inv @ G_herm) @ H @ H_herm @ (Im - Proj_mat) @ W @ A).conj().transpose(1, 2)\n",
        "\n",
        "\n",
        "    def batch_rate(self, batch, W, X, A, p_s, n_0):\n",
        "        \"\"\" Used in order to calculate the average rate of a batch. Calling the forward functon for each sample in the batch\"\"\"\n",
        "        batch_rate = 0\n",
        "        for sample_num in range(batch.shape[0]):\n",
        "            sample = batch[sample_num, :, :, :]\n",
        "            Rs, W_1, X_1 = self.forward(sample, W, X, A, p_s, n_0)\n",
        "            batch_rate += Rs\n",
        "        return batch_rate / (batch.shape[0]), W_1, X_1\n",
        "\n",
        "\n",
        "class PGAModel():\n",
        "    def __init__(self, Mu, pga_iters):\n",
        "        \"\"\"\n",
        "        The classical implementation of the PGA algorithm, a first order method the approch a maximum in a convex problem.\n",
        "        :param Mu: The step size of the algorithm, a hyperparameter\n",
        "        :param pga_iters: The duration of the algorithm, a hyperparameter\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        self.Mu = Mu\n",
        "        self.pga_iters = pga_iters\n",
        "        self.D = D\n",
        "\n",
        "\n",
        "    def forward(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\" based on the forward of the unfolded algorithm, but the value of Mu is fixed \"\"\"\n",
        "        W_1 = W.detach()\n",
        "        X_1 = X.detach()\n",
        "        Rs = torch.zeros(self.pga_iters, dtype=torch.cfloat).to(device)\n",
        "        if W_is_phase_only:\n",
        "            W_1 = project_onto_phases(W_1)\n",
        "        if W_is_block_diag:\n",
        "            W_1 = project_onto_block_diagonal(W_1, self.D)\n",
        "        for j in range(self.pga_iters):\n",
        "            dR_dW = self.calc_dR_dW(H, W_1, X_1, A, p_s, n_0)\n",
        "            W_1 = W_1 + self.Mu * dR_dW\n",
        "            if X_is_variable:\n",
        "                dR_dX = self.calc_dR_dX(H, W_1, X_1, A, p_s, n_0)\n",
        "                X_1 = X_1 + self.Mu * dR_dX\n",
        "            if W_is_phase_only:\n",
        "                W_1 = project_onto_phases(W_1)\n",
        "            if W_is_block_diag:\n",
        "                W_1 = project_onto_block_diagonal(W_1, self.D)\n",
        "            Rs[j] = self.calc_R(H, W_1, X_1, A, p_s, n_0)\n",
        "        return Rs, W_1, X_1\n",
        "\n",
        "\n",
        "    def calc_R(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"gets a channel realization and returns the achievable rate of it. H is of dimension (B,M,K)\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = W @ A @ X\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            I_plus_Z = Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()\n",
        "            det_I_plus_Z = torch.det(I_plus_Z.to(torch.complex128))\n",
        "            R += torch.log(det_I_plus_Z)\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_max_r(self, H):\n",
        "        \"\"\"calclulates the maximal rate of the channel H, susbstituting G to be the matched filter. not used in the algorithm\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = H\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G[b, :, :]\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            I_plus_Z = Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()\n",
        "            det_I_plus_Z = torch.det(I_plus_Z.to(torch.complex128))\n",
        "            R += torch.log(det_I_plus_Z)\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_dR_dW(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to W. the gradient has the same dimension as W\"\"\"\n",
        "        grad= 0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            snr = p_s / n_0\n",
        "            X_con = X.conj()\n",
        "            W_con = W.conj()\n",
        "            H_con = h1.conj()\n",
        "            G = W @ A @ X\n",
        "            G_herm = G.transpose(0, 1).conj()\n",
        "            h1_herm = h1.transpose(0, 1).conj()\n",
        "            G_psu_inv = torch.inverse(G_herm @ G)\n",
        "            Proj_mat = G @ G_psu_inv @ G_herm\n",
        "            Z = snr * G_psu_inv @ G_herm @ h1 @ h1_herm @ G\n",
        "            dR_dZ = torch.inverse(Ik + Z)\n",
        "            grad += snr * ((A @ X) @ dR_dZ @ (2 * G_psu_inv @ G_herm) @ h1 @ h1_herm @ (Im - Proj_mat)).conj().transpose(0, 1)\n",
        "        return grad / B\n",
        "\n",
        "\n",
        "    def calc_dR_dX(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to X. the gradient has the same dimension as X\"\"\"\n",
        "        \"\"\" not used in our code! \"\"\"\n",
        "        snr = p_s / n_0\n",
        "        X_con = X.conj()\n",
        "        W_con = W.conj()\n",
        "        H_con = H.conj()\n",
        "        G = W @ A @ X\n",
        "        G_herm = G.transpose(0,1).conj()\n",
        "        H_herm = H.transpose(1, 2).conj()\n",
        "        G_psu_inv = torch.inverse(G_herm @ G)\n",
        "        Proj_mat = G @ G_psu_inv @ G_herm\n",
        "        Z = snr * G_psu_inv @ G_herm @ H @ H_herm @ G\n",
        "        dR_dZ = torch.inverse(Ik + Z)\n",
        "        return snr * (dR_dZ @ (2 * G_psu_inv @ G_herm) @ H @ H_herm @ (Im - Proj_mat) @ W @ A).conj().transpose(1, 2)\n",
        "\n",
        "\n",
        "    def batch_rate(self, batch, W, X, A, p_s, n_0):\n",
        "        \"\"\" Used in order to calculate the average rate of a batch. Calling the forward functon for each sample in the batch\"\"\"\n",
        "        batch_rate = 0\n",
        "        for sample_num in range(batch.shape[0]):\n",
        "            sample = batch[sample_num, :, :, :]\n",
        "            Rs, W_1, X_1 = self.forward(sample, W, X, A, p_s, n_0)\n",
        "            batch_rate += Rs\n",
        "        return batch_rate / (batch.shape[0]), W_1, X_1\n",
        "\n",
        "\n",
        "class PGAModelMomentum():\n",
        "    def __init__(self, Mu,beta, pga_iters):\n",
        "        \"\"\" Works exactly the same like the PGAModel, but has a value of beta, which is the momentum parameter. \"\"\"\n",
        "        super().__init__()\n",
        "        self.Mu = Mu  # step size parameter.\n",
        "        self.pga_iters = pga_iters  # number of iterations of the PGA algorithm.\n",
        "        self.D = D  # projction matrix\n",
        "        self.beta = beta  # momentum parameter.\n",
        "\n",
        "\n",
        "    def batch_rate(self, batch, W, X, A, p_s, n_0):\n",
        "        \"\"\" Used in order to calculate the average rate of a batch. Calling the forward functon for each sample in the batch\"\"\"\n",
        "        batch_rate = 0\n",
        "        for sample_num in range(batch.shape[0]):\n",
        "            sample = batch[sample_num, :, :, :]\n",
        "            Rs, W_1, X_1 = self.forward(sample, W, X, A, p_s, n_0)\n",
        "            batch_rate += Rs\n",
        "        return batch_rate / (batch.shape[0]), W_1, X_1\n",
        "\n",
        "\n",
        "    def forward(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"\n",
        "        This function describes the flow of the PGA + Momentum model.\n",
        "        Mu and beta are fixed scalars.\n",
        "       :param H: a matrix of size BxMxK containing the channel realizations for each frequency bin.\n",
        "       :param W: the inital value of W. usually randomized. It is important not to take ones or zeros matrix as the initial value of W, since it harms the convergence of the algorithm.\n",
        "       :param X: the inital value of X. set to be identity matrix.\n",
        "       :param A: the initial value of A. predefined in the set_genral_params function.\n",
        "       :return: the list of rates obtained in each iteration, the the final value of W and X.\n",
        "       \"\"\"\n",
        "        W_1 = W.detach()\n",
        "        X_1 = X.detach()\n",
        "        if W_is_phase_only:\n",
        "            W_1 = project_onto_phases(W_1)\n",
        "        if W_is_block_diag:\n",
        "            W_1 = project_onto_block_diagonal(W_1, self.D)\n",
        "        W_0 = torch.zeros_like(W_1) # initizlized as the zero matrix.\n",
        "        X_0 = torch.zeros_like(X_1) # initizlized as the zero matrix.\n",
        "        Rs = torch.zeros(self.pga_iters, dtype=torch.cfloat).to(device)\n",
        "        for j in range(self.pga_iters):\n",
        "            dR_dW = self.calc_dR_dW(H, W_1, X_1, A, p_s, n_0)\n",
        "            W_2 = W_1 + self.Mu * dR_dW + self.beta * (W_1 - W_0)\n",
        "            if X_is_variable:\n",
        "                dR_dX = self.calc_dR_dX(H, W_2, X_1, A, p_s, n_0)\n",
        "                X_2 = X_1 + self.Mu * dR_dX + self.beta * (X_1 - X_0)\n",
        "            else:\n",
        "                X_2 = X_1\n",
        "            if W_is_phase_only:\n",
        "                W_2 = project_onto_phases(W_2)\n",
        "            if W_is_block_diag:\n",
        "                W_2 = project_onto_block_diagonal(W_2, self.D)\n",
        "            Rs[j] = self.calc_R(H, W_2, X_2, A, p_s, n_0)\n",
        "            # updating the matrices:\n",
        "            W_0 = W_1\n",
        "            W_1 = W_2\n",
        "            X_0 = X_1\n",
        "            X_1 = X_2\n",
        "        return Rs, W_2, X_2\n",
        "\n",
        "\n",
        "    def calc_R(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"gets a channel realization and returns the achievable rate of it. h is of dimension (B,M,K)\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = W @ A @ X\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            I_plus_Z = Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()\n",
        "            det_I_plus_Z = torch.det(I_plus_Z.to(torch.complex128))\n",
        "            R += torch.log(det_I_plus_Z)\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_max_r(self, H):\n",
        "        \"\"\"calclulates the maximal rate of the channel H, susbstituting G to be the matched filter. not used in the algorithm\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = H\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G[b, :, :]\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            I_plus_Z = Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()\n",
        "            det_I_plus_Z = torch.det(I_plus_Z.to(torch.complex128))\n",
        "            R += torch.log(det_I_plus_Z)\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_dR_dW(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to W. the gradient has the same dimension as W\"\"\"\n",
        "        grad = 0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            snr = p_s / n_0\n",
        "            X_con = X.conj()\n",
        "            W_con = W.conj()\n",
        "            H_con = h1.conj()\n",
        "            G = W @ A @ X\n",
        "            G_herm = G.transpose(0, 1).conj()\n",
        "            h1_herm = h1.transpose(0, 1).conj()\n",
        "            G_psu_inv = torch.inverse(G_herm @ G)\n",
        "            Proj_mat = G @ G_psu_inv @ G_herm\n",
        "            Z = snr * G_psu_inv @ G_herm @ h1 @ h1_herm @ G\n",
        "            dR_dZ = torch.inverse(Ik + Z)\n",
        "            grad += snr * ((A @ X) @ dR_dZ @ (2 * G_psu_inv @ G_herm) @ h1 @ h1_herm @ (Im - Proj_mat)).conj().transpose(0,1)\n",
        "        return grad / B\n",
        "\n",
        "\n",
        "    def calc_dR_dX(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to X. the gradient has the same dimension as X\"\"\"\n",
        "        \"\"\" not used in our code! \"\"\"\n",
        "        snr = p_s / n_0\n",
        "        X_con = X.conj()\n",
        "        W_con = W.conj()\n",
        "        H_con = H.conj()\n",
        "        # Im = torch.eye(M)\n",
        "        G = W @ A @ X\n",
        "        G_herm = G.transpose(0, 1).conj()\n",
        "        H_herm = H.transpose(1, 2).conj()\n",
        "        G_psu_inv = torch.inverse(G_herm @ G)\n",
        "        Proj_mat = G @ G_psu_inv @ G_herm\n",
        "        Z = snr * G_psu_inv @ G_herm @ H @ H_herm @ G\n",
        "        dR_dZ = torch.inverse(Ik + Z)\n",
        "        return snr * (dR_dZ @ (2 * G_psu_inv @ G_herm) @ H @ H_herm @ (Im - Proj_mat) @ W @ A).conj().transpose(1, 2)\n",
        "\n",
        "\n",
        "class MomentumModel(nn.Module):\n",
        "    \"\"\" The model that outperforms all others. This is the innovative part in our work \"\"\"\n",
        "    def __init__(self, Mu_W, Mu_X,betas, J):\n",
        "        \"\"\" Works exactly the same like the Unfolded Model, but has a value of beta, which is the momentum parameter. \"\"\"\n",
        "        super().__init__()\n",
        "        self.Mu_W = Mu_W  # the step size for each entry of W. varies within every iteration of the algorhtim. Has the shape of W.\n",
        "        self.Mu_X = Mu_X\n",
        "        self.J = J\n",
        "        self.D = D\n",
        "        self.betas = betas  # the momentum parameter for every entry of W. varies within every iteration of the algorhtim. Has the shape of W.\n",
        "\n",
        "\n",
        "    def batch_rate(self, batch, W, X, A, p_s, n_0):\n",
        "        \"\"\" Used in order to calculate the average rate of a batch. Calling the forward functon for each sample in the batch \"\"\"\n",
        "        batch_rate = 0\n",
        "        for sample_num in range(batch.shape[0]):\n",
        "            sample = batch[sample_num, :, :, :]\n",
        "            Rs, W_1, X_1 = self.forward(sample, W, X, A, p_s, n_0)\n",
        "            batch_rate += Rs\n",
        "        return batch_rate / (batch.shape[0]), W_1, X_1\n",
        "\n",
        "\n",
        "    def forward(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"\n",
        "        This function describes the flow of the Unfolded PGA + Momentum model.\n",
        "        Here, the values of Mu_W and beta vary in each iteration.\n",
        "        Mu_W and beta are matrices with the same size as W.\n",
        "       :param H: a matrix of size BxMxK containing the channel realizations for each frequency bin.\n",
        "       :param W: the inital value of W. usually randomized. It is important not to take ones or zeros matrix as the initial value of W, since it harms the convergence of the algorithm.\n",
        "       :param X: the inital value of X. set to be identity matrix.\n",
        "       :param A: the initial value of A. predefined in the set_genral_params function.\n",
        "       :return: the list of rates obtained in each iteration, the the final value of W and X.\n",
        "       \"\"\"\n",
        "        W_1 = W.detach()\n",
        "        X_1 = X.detach()\n",
        "        # projecting W on the constraints:\n",
        "        if W_is_phase_only:\n",
        "            W_1 = project_onto_phases(W_1)\n",
        "        if W_is_block_diag:\n",
        "            W_1 = project_onto_block_diagonal(W_1, self.D)\n",
        "        W_0 = torch.zeros_like(W_1)\n",
        "        Rs = torch.zeros(self.J, dtype=torch.cfloat).to(device)\n",
        "        # Starting the forward path:\n",
        "        for j in range(self.J):\n",
        "            dR_dW = self.calc_dR_dW(H, W_1, X_1, A, p_s, n_0) # calculating the gradient of the rate with respect to W\n",
        "            W_2 = W_1 + self.Mu_W[j] * dR_dW + self.betas[j] * (W_1 - W_0) # updating W\n",
        "            # projecting W on the constraints:\n",
        "            if W_is_phase_only:\n",
        "                W_2 = project_onto_phases(W_2)\n",
        "            if W_is_block_diag:\n",
        "                W_2 = project_onto_block_diagonal(W_2,self.D)\n",
        "            Rs[j] = self.calc_R(H, W_2, X_1, A, p_s, n_0)  # documenting the rate\n",
        "            # updating the matrices:\n",
        "            W_0 = W_1\n",
        "            W_1 = W_2\n",
        "        return Rs, W_2, X_1\n",
        "\n",
        "\n",
        "    def calc_R(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"gets a channel realization and returns the achievable rate of it. h is of dimension (B,M,K)\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = W @ A @ X\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            R += torch.log((Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()).det())\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_max_r(self, H):\n",
        "        \"\"\"calclulates the maximal rate of the channel H, susbstituting G to be the matched filter. not used in the algorithm\"\"\"\n",
        "        R = 0\n",
        "        B, M, K = H.shape\n",
        "        G = H\n",
        "        snr = p_s / n_0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            g1 = G[b, :, :]\n",
        "            psudo_inv_g = torch.inverse(g1.transpose(0, 1).conj() @ g1)\n",
        "            proj_mat = g1 @ psudo_inv_g @ g1.transpose(0, 1).conj()\n",
        "            R += torch.log((Im + snr * proj_mat @ h1 @ h1.transpose(0, 1).conj()).det())\n",
        "        return R/B\n",
        "\n",
        "\n",
        "    def calc_dR_dW(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to W. the gradient has the same dimension as W\"\"\"\n",
        "        grad = 0\n",
        "        for b in range(B):\n",
        "            h1 = H[b, :, :]\n",
        "            snr = p_s / n_0\n",
        "            X_con = X.conj()\n",
        "            W_con = W.conj()\n",
        "            H_con = h1.conj()\n",
        "            G = W @ A @ X\n",
        "            G_herm = G.transpose(0, 1).conj()\n",
        "            h1_herm = h1.transpose(0, 1).conj()\n",
        "            G_psu_inv = torch.inverse(G_herm @ G)\n",
        "            Proj_mat = G @ G_psu_inv @ G_herm\n",
        "            Z = snr * G_psu_inv @ G_herm @ h1 @ h1_herm @ G\n",
        "            dR_dZ = torch.inverse(Ik + Z)\n",
        "            grad += snr * ((A @ X) @ dR_dZ @ (2 * G_psu_inv @ G_herm) @ h1 @ h1_herm @ (Im - Proj_mat)).conj().transpose(0,1)\n",
        "        return grad / B\n",
        "\n",
        "\n",
        "    def calc_dR_dX(self, H, W, X, A, p_s, n_0):\n",
        "        \"\"\"calculates the gradient of the rate with respect to X. the gradient has the same dimension as X\"\"\"\n",
        "        \"\"\" not used in our code! \"\"\"\n",
        "        snr = p_s / n_0\n",
        "        X_con = X.conj()\n",
        "        W_con = W.conj()\n",
        "        H_con = H.conj()\n",
        "        G = W @ A @ X\n",
        "        G_herm = G.transpose(1, 2).conj()\n",
        "        H_herm = H.transpose(1, 2).conj()\n",
        "        G_psu_inv = torch.inverse(G_herm @ G)\n",
        "        Proj_mat = G @ G_psu_inv @ G_herm\n",
        "        Z = snr * G_psu_inv @ G_herm @ H @ H_herm @ G\n",
        "        dR_dZ = torch.inverse(Ik + Z)\n",
        "        return snr * (dR_dZ @ (2 * G_psu_inv @ G_herm) @ H @ H_herm @ (Im - Proj_mat) @ W @ A).conj().transpose(1, 2)\n",
        "\n",
        "\n",
        "def train_unfolding(H_train, H_valid, X, W, A, p_s, n_0, J, Mu_X_init, Mu_W_init,\n",
        "          optimizer_learning_rate, epochs, train_size, batch_size):\n",
        "    \"\"\" Trains the unfolded model - not the momentum model.\"\"\"\n",
        "    \"\"\" Returns the trained model and the training and validation rates. Using ADAM optimizer and maximizing the weighted sum rate function of each iteration. \"\"\"\n",
        "    # Setting the variables:\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    H_train, H_valid, X, W, A = H_train.to(device), H_valid.to(device), X.to(device), W.to(device), A.to(device)\n",
        "    Mu_W = torch.full((J, M, L * P), Mu_W_init, requires_grad=True, device = device)\n",
        "    Mu_X = torch.full((J, T, T), Mu_X_init, requires_grad=X_is_variable, device=device)\n",
        "    # crating the model and setting the optimizer\n",
        "    unfolded_model = UnfoldedModel(Mu_W, Mu_X, J).to(device)\n",
        "    optimizer = torch.optim.Adam([Mu_W], lr=optimizer_learning_rate)\n",
        "    # projecting W on the constraints\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W,D)\n",
        "    train_rates = [0]*(epochs*int(np.ceil(train_size/batch_size)))\n",
        "    validation_rates = [0]*(epochs)\n",
        "    i = 0\n",
        "    # Start training:\n",
        "    print(\"Unfolded starts training\")\n",
        "    for epoch in range(epochs):\n",
        "        print(\"epoch: \", epoch + 1, \" of \", epochs)\n",
        "        initial_time = time.time()\n",
        "        # Shuffling the training data:\n",
        "        H_train = H_train[torch.randperm(H_train.shape[0])]\n",
        "        for iter in range(0, train_size, batch_size):\n",
        "            # dividing the data into batches:\n",
        "            H = H_train[iter:iter + batch_size, :, :, :]\n",
        "            Rs, W_1, X_1 = unfolded_model.batch_rate(H, W=W, X=X, A=A, p_s=p_s, n_0=n_0)  # Rs is the list of the calculated rates in each iteration.\n",
        "            loss = -  torch.sum(Rs * torch.log(torch.arange(2, J + 2, device=device)))  # Negating the weighted loss since we want to maximize the rate.\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_rates[i] = Rs[-1]\n",
        "            i = i+1\n",
        "        # At the end of each epoch we calculate the validation rate:\n",
        "        validation_rate, _, _ = unfolded_model.batch_rate(H_valid, W=W, X=X, A=A,p_s=p_s,n_0=n_0)\n",
        "        validation_rates[epoch] = validation_rate\n",
        "        print(\"epoch time: \", round(time.time() - initial_time, 2), \" seconds\")\n",
        "    return unfolded_model, train_rates, validation_rates\n",
        "\n",
        "\n",
        "def run_PGA_Momentum(PGA_Mu_init, beta, pga_iters, H_valid, W, X, A, p_s, n_0):\n",
        "    \"\"\" Running the PGA + Momentum, with fixes Mu and Beta. Returns the list of calculated sum-rate for each iteration. \"\"\"\n",
        "    \"\"\" Used as a benchmark comparing the performance of the unfolded algorithm with the classical one. \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    # projecting W on the constraints\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W, D)\n",
        "    # moving the variables to \"device\":\n",
        "    H_valid,W,X,A = H_valid.to(device), W.to(device),X.to(device),A.to(device)\n",
        "    # setting the model and running the PGA:\n",
        "    pga_model_Momentum = PGAModelMomentum(Mu=PGA_Mu_init,beta=beta, pga_iters=pga_iters)\n",
        "    Rs_pga, W_2, X_2 = pga_model_Momentum.batch_rate(H_valid, W=W, X=X,A=A, p_s=p_s, n_0=n_0)\n",
        "    return Rs_pga, W_2, X_2\n",
        "\n",
        "\n",
        "def run_PGA(PGA_Mu_init, pga_iters, H_valid, W, X, A, p_s, n_0):\n",
        "    \"\"\" Running the classical PGA , with fixes Mu and Beta. Returns the list of calculated sum-rate for each iteration. \"\"\"\n",
        "    \"\"\" Not used in our code. \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W, D)\n",
        "    H_valid,W,X,A = H_valid.to(device), W.to(device),X.to(device),A.to(device)\n",
        "    pga_model = PGAModel(Mu=PGA_Mu_init, pga_iters=pga_iters)\n",
        "    Rs_pga, W_2, X_2 = pga_model.batch_rate(H_valid, W=W, X=X,A=A, p_s=p_s, n_0=n_0)\n",
        "    return Rs_pga, W_2, X_2\n",
        "\n",
        "def line_search(H_valid, X, W, A, p_s, n_0, grid, line_search_iters):\n",
        "    \"\"\" Iterates over every sample in the validation set, and finds the best step size for each sample. \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    # projecting W on the constraints:\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W, D)\n",
        "    H_valid, W, X, A = H_valid.to(device), W.to(device), X.to(device), A.to(device)\n",
        "    W_1 = W.detach()\n",
        "    X_1 = X.detach()\n",
        "    # Initializing the PGA + M object:\n",
        "    pga_mom_model = PGAModelMomentum(Mu=0,beta=0.9, pga_iters=line_search_iters)\n",
        "    best_steps = torch.zeros((H_valid.shape[0], line_search_iters), device=device)\n",
        "    best_Rs = torch.zeros((H_valid.shape[0], line_search_iters), device=device)\n",
        "    # Takes a single sample:\n",
        "    for sample_num in range(H_valid.shape[0]):\n",
        "        W_1 = W.detach()\n",
        "        X_1 = X.detach()\n",
        "        sample = H_valid[sample_num, :, :, :]\n",
        "        # Start moving over all the iterations in \"line_search_iters\" (Usually 50).\n",
        "        for iteration in range(line_search_iters):\n",
        "            if W_is_phase_only:\n",
        "                W_1 = project_onto_phases(W_1)\n",
        "            if W_is_block_diag:\n",
        "                W_1 = project_onto_block_diagonal(W_1, D)\n",
        "            # Calculates the gradient:\n",
        "            grad = pga_mom_model.calc_dR_dW(sample, W_1, X_1, A, p_s, n_0)\n",
        "            best_step = 0\n",
        "            best_R = -1\n",
        "            # Looking for the best step size with the calculated gradient:\n",
        "            for step in grid:\n",
        "                W_new = W_1 + step * grad  # Updating W\n",
        "                if W_is_phase_only:\n",
        "                    W_new = project_onto_phases(W_new)\n",
        "                if W_is_block_diag:\n",
        "                    W_new = project_onto_block_diagonal(W_new, D)\n",
        "                # Calculating the optional R.\n",
        "                R = pga_mom_model.calc_R(sample, W_new, X, A, p_s, n_0)\n",
        "                if abs(R) > abs(best_R): # If the calculated R is better than the best R so far, save the step size.\n",
        "                    best_R = R\n",
        "                    best_step = step\n",
        "            # Before the next iteration, update W with the best step chosen.\n",
        "            W_1 += best_step * grad\n",
        "            if W_is_phase_only:\n",
        "                W_1 = project_onto_phases(W_1)\n",
        "            if W_is_block_diag:\n",
        "                W_1 = project_onto_block_diagonal(W_1,D)\n",
        "            best_steps[sample_num,iteration] = best_step # saving the best step and the best R.\n",
        "            best_Rs[sample_num,iteration] = best_R\n",
        "    average_best_Rs = torch.mean(best_Rs,axis=0)  # Calculate the mean R\n",
        "    average_best_steps = torch.mean(best_steps,axis=0)  # Calculate the mean best step size, for comparison reasons.\n",
        "    return average_best_Rs, average_best_steps\n",
        "\n",
        "\n",
        "def train_momentum(H_train, H_valid, X, W, A,p_s,n_0, J, Mu_X_init, Mu_W_init,\n",
        "          optimizer_learning_rate, epochs, train_size, batch_size, betas_init):\n",
        "    \"\"\" Trains the unfolded model + Momentum. Returns the trained model and the training and validation rates.\n",
        "     Using ADAM optimizer and maximizing the weighted sum rate function of each iteration.\"\"\"\n",
        "    # setting the device to be GPU to accalerate calculations.\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W,D)\n",
        "    # saving variables in device:\n",
        "    H_train, H_valid, X, W, A = H_train.to(device), H_valid.to(device), X.to(device), W.to(device), A.to(device)\n",
        "    Mu_W = torch.full((J, M, L * P), Mu_W_init, requires_grad=True, device=device)\n",
        "    Mu_X = torch.full((J, T, T), Mu_X_init, requires_grad=X_is_variable, device=device)\n",
        "    betas = torch.full((J, M, L * P), betas_init, requires_grad=True, device = device)\n",
        "    momentum_model = MomentumModel(Mu_W, Mu_X,betas, J).to(device)\n",
        "    optimizer = torch.optim.Adam([Mu_W, betas], lr=optimizer_learning_rate)\n",
        "    if W_is_phase_only:\n",
        "        W = project_onto_phases(W)\n",
        "    if W_is_block_diag:\n",
        "        W = project_onto_block_diagonal(W,D)\n",
        "    train_rates = [0]*(epochs*int(np.ceil(train_size/batch_size)))\n",
        "    validation_rates = [0]*(epochs)\n",
        "    i = 0\n",
        "    # Starting training procedure:\n",
        "    print(\"Momentum starts training\")\n",
        "    for epoch in range(epochs):\n",
        "        print(\"epoch: \", epoch + 1, \" of \", epochs)\n",
        "        initial_time = time.time()\n",
        "        H_train = H_train[torch.randperm(H_train.shape[0])]  # Shuffles the training data.\n",
        "        for iter in range(0, train_size, batch_size):\n",
        "            H = H_train[iter:iter + batch_size, :, :, :]  # dividing the data to batches.\n",
        "            Rs, W_1, X_1 = momentum_model.batch_rate(H, W=W, X=X, A=A, p_s=p_s, n_0=n_0)  # Rs is the list of the calculated rates in each iteration.\n",
        "            loss = -  torch.sum(Rs * torch.log(torch.arange(2, J + 2, device=device)))  # Negating the weighted loss since we want to maximize the rate.\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()  # Making the backward step.\n",
        "            optimizer.step()\n",
        "            train_rates[i] = Rs[-1]\n",
        "            i = i+1\n",
        "        # At the end of each epoch we calculate the validation rate:\n",
        "        validation_rate, _, _ = momentum_model.batch_rate(H_valid, W=W, X=X, A=A,p_s=p_s,n_0=n_0)\n",
        "        validation_rates[epoch] = validation_rate\n",
        "        print(\"epoch time: \", round(time.time() - initial_time, 2), \" seconds\")\n",
        "    return momentum_model, train_rates, validation_rates\n",
        "\n",
        "\n",
        "def constant_search(H_valid, W, X, A, p_s, n_0,constant_search_iters, grid):\n",
        "    \"\"\" A function that finds the best Mu from a given grid, for a classical PGA algorithm without momentum. Not used in our code. \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    H_valid, W, X, A = H_valid.to(device), W.to(device), X.to(device), A.to(device)\n",
        "    W_1 = W.detach()\n",
        "    X_1 = X.detach()\n",
        "    if W_is_phase_only:\n",
        "        W_1 = project_onto_phases(W_1)\n",
        "    if W_is_block_diag:\n",
        "        W_1 = project_onto_block_diagonal(W_1, D)\n",
        "    best_R = 0\n",
        "    best_step = 0\n",
        "    for step in grid:\n",
        "        pga_model = PGAModel(Mu=step, pga_iters=constant_search_iters)\n",
        "        R,_,_ = pga_model.batch_rate(H_valid, W_1, X_1, A, p_s, n_0)\n",
        "        if abs(R[-1]) > abs(best_R):\n",
        "            best_R = R[-1]\n",
        "            best_step = step\n",
        "    return best_step, best_R\n",
        "\n",
        "\n",
        "def beta_search(H_valid, W, X, A, p_s, n_0,constant_search_iters, grid, best_Mu):\n",
        "    \"\"\" A function that finds the best beta from a given grid, for a PGA + M , with a fixed Mu. Not used in our code. \"\"\"\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    H_valid, W, X, A = H_valid.to(device), W.to(device), X.to(device), A.to(device)\n",
        "    W_1 = W.detach()\n",
        "    X_1 = X.detach()\n",
        "    if W_is_phase_only:\n",
        "        W_1 = project_onto_phases(W_1)\n",
        "    if W_is_block_diag:\n",
        "        W_1 = project_onto_block_diagonal(W_1, D)\n",
        "    best_R = 0\n",
        "    best_step = 0\n",
        "    for step in grid:\n",
        "        pga_model_mom = PGAModelMomentum(Mu=best_Mu,beta=step, pga_iters=constant_search_iters)\n",
        "        R,_,_ = pga_model_mom.batch_rate(H_valid, W_1, X_1, A, p_s, n_0)\n",
        "        if abs(R[-1]) > abs(best_R):\n",
        "            best_R = R[-1]\n",
        "            best_step = step\n",
        "    return best_step, best_R\n",
        "\n",
        "\n",
        "def mu_and_beta_search(H_valid, W, X, A, p_s, n_0,constant_search_iters, grid_mu, grid_beta):\n",
        "    \"\"\" A function that finds the best Mu and beta combination from some given grids, for a classical PGA + M.\n",
        "     Used in order to find the optimal values of PGA + M as benchmarks.\n",
        "     The unfolded model is initiated with the values found here. \"\"\"\n",
        "\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    H_valid, W, X, A = H_valid.to(device), W.to(device), X.to(device), A.to(device)\n",
        "    W_1 = W.detach()\n",
        "    X_1 = X.detach()\n",
        "    if W_is_phase_only:\n",
        "        W_1 = project_onto_phases(W_1)\n",
        "    if W_is_block_diag:\n",
        "        W_1 = project_onto_block_diagonal(W_1, D)\n",
        "    best_R = 0\n",
        "    best_mu = 0\n",
        "    best_beta = 0\n",
        "    pga_model_mom = PGAModelMomentum(Mu=0, beta=0, pga_iters=constant_search_iters)\n",
        "    for mu in grid_mu:\n",
        "        pga_model_mom.Mu = mu  # Changing the model hyperparameter\n",
        "        for beta in grid_beta:\n",
        "            pga_model_mom.beta = beta  # Changing the model hyperparameter\n",
        "            print(\"mu:\",round(mu.item(),2),\"beta:\",round(beta.item(),2))\n",
        "            # for a given combination of Mu and Beta, we run the PGA + M algorithm to find what combination brings the best R.\n",
        "            R,_,_ = pga_model_mom.batch_rate(H_valid, W_1, X_1, A, p_s, n_0)\n",
        "            if abs(R[-1]) > abs(best_R):  # Checking if the last R is better than the best R:\n",
        "                best_R = R[-1]\n",
        "                best_mu = mu\n",
        "                best_beta = beta\n",
        "    return best_mu,best_beta, best_R\n",
        "\n",
        "\n",
        "def plot_learning_curve(train_rates, validation_rates):\n",
        "    \"\"\" Plotting the improvment of the sum-rate after training the model for each epoch. Helps us avoid overfitting, tune the learning rate, etc.\n",
        "     Used for internal analysis. \"\"\"\n",
        "    iters_per_epoch = np.ceil(train_size / batch_size)\n",
        "    plt.figure()\n",
        "    y_t = [r.cpu().detach().numpy() for r in train_rates]\n",
        "    x_t = np.array(list(range(len(train_rates)))) / iters_per_epoch\n",
        "    y_v = [r[-1].cpu().detach().numpy() for r in validation_rates]\n",
        "    x_v = np.array(list(range(len(validation_rates)))) + 1\n",
        "    plt.plot(x_t, y_t, 'o', label='Train')\n",
        "    plt.plot(x_v, y_v, '*', label='Valid')\n",
        "    plt.grid()\n",
        "    title = 'Channel Rate After ' + str(J) + ' Iterations Unfolded PGA, After training ' + str(epochs) + ' Epochs \\n ' + \\\n",
        "            W_is_block_diag * 'W is Block Diagonal' + 'W is not Block Diagonal' * (not W_is_block_diag) + \\\n",
        "            ', W is Only Phases' * W_is_phase_only + ', W is not Only Phases' * (not W_is_phase_only) + \\\n",
        "            ', X is variable' * X_is_variable + ', X is fixed' * (not X_is_variable)\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(\"Bits per channel use\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_comparison(J, validation_rate, pga_iters, Rs_pga, labels = ['Unfolded PGA + M','PGA + M']):\n",
        "    \"\"\" Plotting the calculted rate after each iteration, only for the first J iterations (usually, J=10).\"\"\"\n",
        "    plt.figure()\n",
        "    plt.plot(list(range(1,J+1)) , validation_rate.detach().cpu().numpy(), label=labels[0])\n",
        "    plt.plot(list(range(1,pga_iters+1)) , Rs_pga.detach().cpu().numpy(), label=labels[1])\n",
        "    plt.grid()\n",
        "    plt.title('Channel Rate After ' + str(J) +\n",
        "              ' Iterations Unfolded PGA, After training ' + str(epochs) + ' Epochs \\n' +\n",
        "              'with K = ' + str(K))\n",
        "    plt.xlabel('Iteration')\n",
        "    plt.ylabel(\"Bits per channel use per user\")\n",
        "    plt.axhline(y=max(abs(Rs_pga)).detach().cpu().numpy(), color='black', linestyle='--', label='Max rate after {} iters'.format(pga_iters))\n",
        "    plt.legend()\n",
        "    plt.xlim(1,J)\n",
        "    # plt.show()\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_comparison_full(J, validation_rate, pga_iters, Rs_pga, labels = ['Unfolded PGA + M','PGA + M']):\n",
        "    \"\"\" Plotting the calculted rate after each iteration, For the whole range of iterations (usually, 400).\"\"\"\n",
        "    plt.figure()\n",
        "    plt.plot(list(range(1,J+1)) , validation_rate.detach().cpu().numpy(), label=labels[0])\n",
        "    plt.plot(list(range(1,pga_iters+1)) , Rs_pga.detach().cpu().numpy(), label=labels[1])\n",
        "    plt.grid()\n",
        "    plt.title('Channel Rate After ' + str(J) +\n",
        "              ' Iterations Unfolded PGA, After training ' + str(epochs) + ' Epochs \\n' +\n",
        "              'with K = ' + str(K))\n",
        "    plt.xlabel('Iteration')\n",
        "    plt.ylabel(\"Bits per channel use per user\")\n",
        "    plt.axhline(y=max(abs(Rs_pga)).detach().cpu().numpy(), color='black', linestyle='--', label='Max rate after {} iters'.format(pga_iters))\n",
        "    plt.legend()\n",
        "    # plt.show()\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_Mu_W(Mu_W, J):\n",
        "    \"\"\" Plotting the Mu_W matrix, the step size in each iteration.\n",
        "     The functions is used for internal analysis. \"\"\"\n",
        "    fig1, axs1 = plt.subplots(1, J, figsize=(18, 4.5))\n",
        "    # Plotting each slice of Mu_W with the same colorbar range :\n",
        "    global_min = np.min(np.real(Mu_W[:, :, :].cpu().detach().numpy()))\n",
        "    global_max = np.max(np.real(Mu_W[:, :, :].cpu().detach().numpy()))\n",
        "    for j in range(J):\n",
        "        im1 = axs1[j].imshow(np.real(Mu_W[j, :, :].cpu().detach().numpy()), vmin=global_min, vmax=global_max)\n",
        "        axs1[j].set_title('j=' + str(j), fontsize=14)\n",
        "    # Addding a global colorbar for all the plots:\n",
        "    fig1.colorbar(im1, ax=axs1[:], location='bottom')\n",
        "    plt.suptitle('Mu_W', fontsize=28)\n",
        "    plt.show()\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_betas(betas, J):\n",
        "    \"\"\" Plotting the Betas matrix, used for the momentum step.\n",
        "     The function is used for internal analysis. \"\"\"\n",
        "    fig1, axs1 = plt.subplots(1, J, figsize=(18, 4.5))\n",
        "    global_min = np.min(np.real(betas[:, :, :].cpu().detach().numpy()))\n",
        "    global_max = np.max(np.real(betas[:, :, :].cpu().detach().numpy()))\n",
        "    # Plot each slice of Mu_W with the same colorbar range\n",
        "    for j in range(J):\n",
        "        im1 = axs1[j].imshow(np.real(betas[j, :, :].cpu().detach().numpy()), vmin=global_min, vmax=global_max)\n",
        "        axs1[j].set_title('j=' + str(j), fontsize=14)\n",
        "    # Add a global colorbar for all the plots\n",
        "    fig1.colorbar(im1, ax=axs1[:], location='bottom')\n",
        "    plt.suptitle('Momentum Coeffcients', fontsize=28)\n",
        "    plt.show()\n",
        "    return\n",
        "\n",
        "\n",
        "def plot_all(train_rates, validation_rates, Rs_pga, Mu_W, J, pga_iters, to_plot_betas = False, betas = None):\n",
        "    \"\"\" Plotting all the relevant graphs for the unfolded PGA algorithm. \"\"\"\n",
        "    plot_learning_curve(train_rates, validation_rates)\n",
        "    plot_comparison(J,validation_rates[-1], pga_iters, Rs_pga)\n",
        "    plot_comparison_full(J,validation_rates[-1], pga_iters, Rs_pga)\n",
        "    plot_Mu_W(Mu_W, J)\n",
        "    if to_plot_betas:\n",
        "        plot_betas(betas, J)\n",
        "    return\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNuG0pgISOwd"
      },
      "source": [
        "#Simulations"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##T=5,K=20,P=2,L=4,N=8,B=4"
      ],
      "metadata": {
        "id": "t31ypl9DQH1M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###valid_size = 100, pga_iters = 400"
      ],
      "metadata": {
        "id": "IGt1BkeXVp2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "range_snr = range(0, 6) # SNR range to iterate over.\n",
        "line_search_max = [0 for _ in range_snr]\n",
        "pga_400_max = [0 for _ in range_snr]\n",
        "pga_10_max = [0 for _ in range_snr]\n",
        "unfolded_max = [0 for _ in range_snr]\n",
        "momentum_max = [0 for _ in range_snr]\n",
        "for SNR in range_snr:\n",
        "\n",
        "    init_time = time.time()\n",
        "    print(\"SNR =\",SNR)\n",
        "\n",
        "    # Setting parameters and variables:\n",
        "    device, n_0, p_s, B, K, P, N, M, L, T, snr, T_opt, A, Ik, Im,D, J,\\\n",
        "     epochs, W_is_block_diag, W_is_phase_only, X_is_variable,\\\n",
        "        train_size, batch_size, pga_iters,\\\n",
        "         optimizer_learning_rate= set_general_params(J=10, epochs=5,\n",
        "            batch_size=20,T=5,K=20,P=2,L=4,N=8,B=4,train_size = 2000,\n",
        "              pga_iters = 400, optimizer_learning_rate=0.09, n_0 = 10**(-0.1*SNR))\n",
        "\n",
        "    H_train,H_valid,X,W = gen_data(seed = 1, train_size = train_size, valid_size = 100)\n",
        "    H_train,H_valid,X,W = H_train.to(device),H_valid.to(device),X.to(device),W.to(device)\n",
        "    grid_line = torch.cat((torch.linspace(0.1, 1, 3),torch.linspace(1.5, 9.5, 17))).to(device)\n",
        "    grid_mu =  torch.cat((torch.linspace(0.05, 1, 4),torch.linspace(1.5, 7.5, 4))).to(device)\n",
        "    grid_beta = torch.linspace(0.9, 0.99, 3).to(device)\n",
        "\n",
        "    # Running benchmarks:\n",
        "    # Linesearch:\n",
        "    Rs_line_search, _ = line_search(H_valid, X, W, A, p_s, n_0, grid_line, line_search_iters = 50)\n",
        "\n",
        "    # Classical PGA + M :\n",
        "    best_mu, best_beta, best_R_mom = mu_and_beta_search(H_valid, W, X, A, p_s, n_0, constant_search_iters=pga_iters,\n",
        "                                                        grid_mu=grid_mu, grid_beta=grid_beta)\n",
        "    # result after 400 iterations:\n",
        "    Rs_pga_400, _,_ = run_PGA_Momentum(PGA_Mu_init=best_mu, beta=best_beta, pga_iters=pga_iters,H_valid = H_valid,W=W,X=X,A=A, p_s= p_s, n_0=n_0)\n",
        "    # result after 10 iterations:\n",
        "    Rs_pga_10,_,_ = run_PGA_Momentum(PGA_Mu_init=best_mu,beta=best_beta, pga_iters=J,H_valid = H_valid,W=W,X=X,A=A, p_s= p_s, n_0=n_0)\n",
        "\n",
        "    # Running and training the unfolded model:\n",
        "    momentum_model, mom_train_rates,mom_validation_rates = train_momentum(H_train,H_valid, X,\n",
        "                                                      W, A, p_s=p_s, n_0=n_0, J=J,Mu_X_init=1.5, Mu_W_init=best_mu,\n",
        "                                                      optimizer_learning_rate=optimizer_learning_rate,\n",
        "                                                      epochs=epochs,train_size = train_size,\n",
        "                                                      batch_size = batch_size, betas_init = best_beta)\n",
        "\n",
        "    # printing the results when SNR = 0[dB] for sanity check.\n",
        "    line_search_max[SNR] = Rs_line_search[-1]\n",
        "    pga_400_max[SNR] = Rs_pga_400[-1]\n",
        "    pga_10_max[SNR] = Rs_pga_10[-1]\n",
        "    momentum_max[SNR] = mom_validation_rates[-1][-1]\n",
        "    if SNR==0:\n",
        "        plot_learning_curve(mom_train_rates, mom_validation_rates)\n",
        "        plot_comparison(J, mom_validation_rates[-1], pga_iters, Rs_pga_400)\n",
        "        plt.plot(range(1,51), Rs_line_search.cpu().detach().numpy(), label='Line Search')\n",
        "        plt.xlim(1,J)\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "        plot_comparison_full(J, mom_validation_rates[-1], pga_iters, Rs_pga_400)\n",
        "        plt.plot(range(1,51), Rs_line_search.cpu().detach().numpy(), label='Line Search')\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "        print(\"momentum:\", mom_validation_rates[-1])\n",
        "        print(\"line search:\", Rs_line_search)\n",
        "        print(\"pga 400:\", Rs_pga_400)\n",
        "    print(\"SNR time:\", round(time.time()-init_time,2),\"seconds\")\n",
        "\n",
        "\n",
        "# printing and plotting the other results:\n",
        "plt.figure()\n",
        "plt.plot(range_snr,torch.tensor(pga_10_max).cpu(), '--*',color=\"purple\",label= 'PGA + M - 10 Iters')\n",
        "plt.plot(range_snr,torch.tensor(line_search_max).cpu(), 'g:x',label= 'Line Search - 50 Iters', linewidth=2.5)\n",
        "plt.plot(range_snr,torch.tensor(pga_400_max).cpu(),'--*' ,color=\"orange\",label= 'PGA + M - 400 Iters')\n",
        "plt.plot(range_snr,torch.tensor(momentum_max).cpu(), 'b--s',label= 'Unfolded PGA + M - 10 Iters')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.xlabel('SNR [dB]')\n",
        "plt.ylabel(\"Bits per channel use\")\n",
        "plt.show()\n",
        "print(\"line search max: \", line_search_max)\n",
        "print(\"pga 400 max: \", pga_400_max)\n",
        "print(\"pga 10 max: \", pga_10_max)\n",
        "print(\"momentum max: \", momentum_max)\n",
        "pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iEymWN3IWKr5",
        "outputId": "870e06bd-3bf5-4791-8f51-c5848eb845f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SNR = 0\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-1-c05d309e312c>:620: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at ../aten/src/ATen/native/Copy.cpp:276.)\n",
            "  best_Rs[sample_num,iteration] = best_R\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  135.41  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  136.83  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  135.1  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  135.77  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  137.64  seconds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py:1335: ComplexWarning: Casting complex values to real discards the imaginary part\n",
            "  return np.asarray(x, float)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "momentum: tensor([14.6806-5.0078e-08j, 15.3371-1.5675e-08j, 15.8561+2.0185e-08j,\n",
            "        16.1240+1.0555e-07j, 16.2991-4.5777e-08j, 16.4054+2.9743e-08j,\n",
            "        16.4766+8.7832e-08j, 16.5255+5.1113e-08j, 16.5556-1.7021e-08j,\n",
            "        16.5777-2.3123e-08j], grad_fn=<DivBackward0>)\n",
            "line search: tensor([15.4744, 15.8836, 16.1010, 16.2316, 16.3175, 16.3771, 16.4220, 16.4568,\n",
            "        16.4841, 16.5054, 16.5230, 16.5375, 16.5499, 16.5605, 16.5698, 16.5780,\n",
            "        16.5856, 16.5923, 16.5984, 16.6036, 16.6085, 16.6129, 16.6172, 16.6211,\n",
            "        16.6246, 16.6280, 16.6310, 16.6339, 16.6367, 16.6393, 16.6418, 16.6441,\n",
            "        16.6462, 16.6483, 16.6502, 16.6521, 16.6537, 16.6554, 16.6571, 16.6588,\n",
            "        16.6603, 16.6618, 16.6631, 16.6643, 16.6655, 16.6667, 16.6677, 16.6688,\n",
            "        16.6698, 16.6707])\n",
            "pga 400: tensor([14.6365+6.1302e-08j, 14.7954-1.1418e-08j, 15.0325+3.3856e-08j,\n",
            "        15.2924-4.3245e-08j, 15.5155-2.4612e-08j, 15.6730+1.8224e-09j,\n",
            "        15.7679-1.2921e-08j, 15.8174+1.3872e-08j, 15.8418+1.2402e-07j,\n",
            "        15.8584-9.8430e-08j, 15.8773-2.5466e-08j, 15.9009+4.3499e-08j,\n",
            "        15.9280+3.1701e-08j, 15.9570-6.1542e-08j, 15.9865-6.5969e-08j,\n",
            "        16.0153-4.1714e-08j, 16.0424-5.1541e-08j, 16.0673+1.1631e-08j,\n",
            "        16.0906+1.8530e-08j, 16.1133-2.0691e-08j, 16.1355+3.7117e-08j,\n",
            "        16.1558-3.9571e-08j, 16.1740+7.8756e-08j, 16.1913+9.1926e-08j,\n",
            "        16.2087+1.9508e-08j, 16.2270+6.7871e-08j, 16.2456-5.8990e-09j,\n",
            "        16.2625+2.6674e-08j, 16.2762-2.2496e-09j, 16.2869+6.9083e-09j,\n",
            "        16.2961+4.7456e-09j, 16.3055-2.7846e-08j, 16.3171-4.8886e-08j,\n",
            "        16.3310+6.0848e-09j, 16.3444+4.4835e-08j, 16.3554-6.4823e-08j,\n",
            "        16.3643+2.6719e-08j, 16.3726-1.2297e-08j, 16.3813+4.0161e-08j,\n",
            "        16.3900-3.1267e-08j, 16.3982-8.8925e-08j, 16.4064+6.7184e-08j,\n",
            "        16.4149+7.0056e-08j, 16.4228-4.3521e-08j, 16.4302+3.1617e-08j,\n",
            "        16.4379+4.0720e-08j, 16.4456+3.9533e-08j, 16.4522-6.9512e-08j,\n",
            "        16.4572+6.7516e-08j, 16.4617+2.0942e-08j, 16.4675+5.7806e-08j,\n",
            "        16.4750+3.0969e-08j, 16.4826-6.1194e-09j, 16.4886-1.5103e-08j,\n",
            "        16.4935-3.9573e-08j, 16.4982-5.9093e-08j, 16.5028+5.4477e-08j,\n",
            "        16.5074+4.7215e-09j, 16.5124-1.6614e-08j, 16.5177+2.3429e-08j,\n",
            "        16.5231-3.1635e-08j, 16.5281+1.6052e-08j, 16.5324+9.3701e-09j,\n",
            "        16.5360-3.4986e-09j, 16.5393+5.1498e-08j, 16.5425+3.4187e-08j,\n",
            "        16.5459+3.9722e-08j, 16.5497+8.4470e-08j, 16.5535-1.1772e-08j,\n",
            "        16.5574+2.0692e-09j, 16.5614-7.7580e-09j, 16.5653-2.5799e-08j,\n",
            "        16.5687-7.3813e-08j, 16.5719+5.7525e-08j, 16.5754+1.3400e-08j,\n",
            "        16.5784-6.0500e-09j, 16.5813+6.8152e-08j, 16.5845-1.6629e-08j,\n",
            "        16.5878+1.4426e-08j, 16.5906-9.1843e-10j, 16.5930-3.0499e-08j,\n",
            "        16.5954+3.9079e-08j, 16.5983-4.5353e-09j, 16.6013+2.9311e-08j,\n",
            "        16.6041-7.8446e-08j, 16.6063-5.4044e-08j, 16.6083-1.1496e-08j,\n",
            "        16.6103-5.5589e-09j, 16.6125-1.2817e-08j, 16.6148-4.9105e-10j,\n",
            "        16.6168+1.9534e-08j, 16.6188+6.0344e-08j, 16.6208+8.2361e-08j,\n",
            "        16.6231+5.6697e-08j, 16.6256-4.8218e-08j, 16.6280+5.3495e-08j,\n",
            "        16.6306+1.1118e-09j, 16.6331-4.0064e-08j, 16.6349-6.6808e-09j,\n",
            "        16.6366+2.6310e-08j, 16.6384-8.2820e-09j, 16.6405+3.6610e-08j,\n",
            "        16.6431+2.8571e-08j, 16.6458+3.7296e-08j, 16.6473+6.7815e-09j,\n",
            "        16.6476+8.0070e-09j, 16.6479+6.0200e-08j, 16.6490+2.8984e-08j,\n",
            "        16.6506+1.9284e-08j, 16.6525+6.0010e-08j, 16.6546+7.4823e-10j,\n",
            "        16.6566+3.2992e-08j, 16.6582+1.1783e-09j, 16.6597-6.0037e-08j,\n",
            "        16.6609+1.1523e-08j, 16.6622-4.9931e-09j, 16.6641-7.3118e-08j,\n",
            "        16.6661+2.2192e-09j, 16.6674+6.7004e-08j, 16.6683+3.6833e-08j,\n",
            "        16.6694-3.7684e-09j, 16.6710+3.5756e-08j, 16.6726-1.0599e-08j,\n",
            "        16.6738+1.9073e-09j, 16.6747+3.9583e-09j, 16.6755+6.0522e-08j,\n",
            "        16.6765-4.5904e-08j, 16.6778-9.1961e-09j, 16.6793-4.8708e-08j,\n",
            "        16.6808+2.5701e-08j, 16.6823+7.7329e-08j, 16.6837+7.3973e-08j,\n",
            "        16.6847+5.6061e-08j, 16.6853-1.9700e-08j, 16.6857-3.6140e-08j,\n",
            "        16.6864-1.8175e-08j, 16.6873-6.8693e-08j, 16.6883-3.2379e-09j,\n",
            "        16.6900-1.3733e-08j, 16.6918-5.1932e-08j, 16.6933+2.2775e-09j,\n",
            "        16.6940+4.8191e-08j, 16.6940-1.3651e-08j, 16.6942-2.8451e-08j,\n",
            "        16.6952-5.1587e-08j, 16.6969-7.2001e-10j, 16.6986-2.9639e-08j,\n",
            "        16.6997-1.9040e-08j, 16.7000+5.3473e-08j, 16.7003-3.6914e-08j,\n",
            "        16.7008-2.8934e-08j, 16.7015+8.9906e-08j, 16.7019+4.4319e-08j,\n",
            "        16.7026-4.9600e-08j, 16.7037-4.1372e-08j, 16.7053-3.4123e-08j,\n",
            "        16.7067-6.3664e-08j, 16.7071-6.9548e-10j, 16.7070+2.1053e-08j,\n",
            "        16.7075-4.1803e-08j, 16.7086+3.9793e-08j, 16.7101+4.6752e-08j,\n",
            "        16.7114-2.0020e-08j, 16.7119+2.0613e-08j, 16.7119-7.3070e-08j,\n",
            "        16.7123-1.8400e-08j, 16.7128-2.3819e-08j, 16.7135-6.5695e-08j,\n",
            "        16.7144+4.1988e-08j, 16.7152-2.9271e-08j, 16.7158-3.1892e-08j,\n",
            "        16.7165-1.0284e-07j, 16.7177+7.1104e-10j, 16.7182-1.8613e-08j,\n",
            "        16.7185-4.7378e-08j, 16.7189+6.4468e-08j, 16.7195-4.7053e-08j,\n",
            "        16.7204+7.0786e-08j, 16.7211+6.2105e-08j, 16.7215-4.5310e-09j,\n",
            "        16.7219-1.9616e-08j, 16.7221+4.0293e-09j, 16.7224-5.6907e-08j,\n",
            "        16.7233-8.3780e-09j, 16.7243+3.1318e-08j, 16.7250-1.0327e-07j,\n",
            "        16.7253+1.2139e-07j, 16.7255-4.2965e-08j, 16.7260+6.4836e-08j,\n",
            "        16.7264+5.6405e-08j, 16.7266-7.0709e-08j, 16.7274-3.3865e-09j,\n",
            "        16.7280+1.2298e-07j, 16.7283-4.9993e-08j, 16.7286-1.0020e-07j,\n",
            "        16.7294-5.6278e-08j, 16.7301+5.4029e-08j, 16.7308-5.6996e-09j,\n",
            "        16.7310+7.5482e-08j, 16.7314+4.1892e-08j, 16.7321+1.9125e-08j,\n",
            "        16.7331+1.7278e-08j, 16.7336+4.7009e-08j, 16.7340+5.4675e-08j,\n",
            "        16.7341+3.9742e-08j, 16.7344-5.0888e-09j, 16.7348-4.5869e-08j,\n",
            "        16.7356-3.2496e-08j, 16.7361+7.1521e-08j, 16.7361-3.6767e-09j,\n",
            "        16.7362+3.8530e-08j, 16.7365+1.6060e-08j, 16.7371-1.1392e-07j,\n",
            "        16.7379-5.7269e-08j, 16.7382+1.1550e-08j, 16.7384+1.4949e-08j,\n",
            "        16.7385-6.4248e-08j, 16.7387+1.1364e-07j, 16.7388+4.9488e-08j,\n",
            "        16.7392-4.2152e-08j, 16.7399-7.2411e-09j, 16.7408+2.8729e-08j,\n",
            "        16.7416-1.6920e-09j, 16.7420+8.0892e-08j, 16.7423+9.6414e-08j,\n",
            "        16.7426-8.7668e-08j, 16.7428+9.9428e-09j, 16.7428-1.6222e-08j,\n",
            "        16.7428+4.4884e-08j, 16.7430+9.2179e-09j, 16.7437-3.7532e-08j,\n",
            "        16.7444-2.6881e-08j, 16.7451-9.8759e-09j, 16.7455-5.2558e-08j,\n",
            "        16.7455-4.9894e-09j, 16.7455+2.0607e-08j, 16.7458-3.4731e-08j,\n",
            "        16.7465-8.7900e-09j, 16.7469+1.5641e-08j, 16.7471-2.9690e-08j,\n",
            "        16.7473-2.8175e-08j, 16.7476+5.6238e-08j, 16.7479+5.3686e-08j,\n",
            "        16.7482-5.0720e-08j, 16.7485-1.5157e-08j, 16.7486+5.5748e-09j,\n",
            "        16.7492-2.7551e-08j, 16.7496+4.3529e-08j, 16.7499-2.7797e-08j,\n",
            "        16.7503-5.0207e-09j, 16.7502-4.2713e-08j, 16.7499+7.7868e-08j,\n",
            "        16.7503+6.4604e-08j, 16.7509+5.5478e-08j, 16.7514+7.3337e-08j,\n",
            "        16.7518-1.3732e-08j, 16.7520-1.2914e-09j, 16.7522+8.2770e-08j,\n",
            "        16.7526+4.4217e-08j, 16.7528-1.2518e-07j, 16.7531+5.7071e-08j,\n",
            "        16.7532+1.7756e-09j, 16.7532+8.7887e-08j, 16.7533+1.5824e-08j,\n",
            "        16.7536+3.4673e-09j, 16.7538+3.0100e-08j, 16.7540+5.9430e-08j,\n",
            "        16.7542-1.1634e-09j, 16.7547-1.9501e-08j, 16.7549-4.1552e-08j,\n",
            "        16.7552+4.6227e-09j, 16.7557-2.9319e-08j, 16.7560-9.6857e-09j,\n",
            "        16.7559+6.5907e-08j, 16.7560+8.1055e-08j, 16.7562-1.5554e-08j,\n",
            "        16.7564+1.2752e-08j, 16.7565+1.2556e-07j, 16.7566-8.1184e-08j,\n",
            "        16.7567+6.0860e-10j, 16.7569+8.9054e-09j, 16.7573+3.8235e-08j,\n",
            "        16.7574+1.0737e-07j, 16.7580-6.0624e-08j, 16.7581+2.7850e-08j,\n",
            "        16.7584-2.6912e-09j, 16.7584-6.1747e-08j, 16.7584-3.1744e-08j,\n",
            "        16.7587-1.0553e-07j, 16.7587-3.6713e-08j, 16.7589+3.2208e-08j,\n",
            "        16.7592+6.1769e-08j, 16.7593-2.1660e-08j, 16.7594-5.9964e-08j,\n",
            "        16.7597+1.2650e-08j, 16.7600+4.0210e-08j, 16.7603-3.3916e-08j,\n",
            "        16.7605-2.0129e-08j, 16.7605+8.3293e-08j, 16.7608-5.1374e-08j,\n",
            "        16.7609+8.8484e-08j, 16.7611-4.4913e-08j, 16.7613-3.2644e-08j,\n",
            "        16.7614-2.9939e-08j, 16.7613+4.2669e-08j, 16.7614-5.0473e-08j,\n",
            "        16.7614-8.0511e-08j, 16.7617-1.7622e-08j, 16.7621-3.1679e-08j,\n",
            "        16.7624+1.6291e-08j, 16.7628-8.5028e-08j, 16.7630-2.0779e-08j,\n",
            "        16.7630+2.0935e-08j, 16.7632+1.8299e-08j, 16.7636-2.4448e-08j,\n",
            "        16.7635-5.5778e-09j, 16.7637-1.6845e-08j, 16.7635-2.2234e-08j,\n",
            "        16.7634-5.6931e-08j, 16.7628+3.2351e-08j, 16.7636+6.4143e-08j,\n",
            "        16.7636-8.2844e-09j, 16.7634+1.1176e-08j, 16.7637-2.9836e-08j,\n",
            "        16.7633+3.7185e-08j, 16.7641+1.6597e-09j, 16.7640+6.9369e-08j,\n",
            "        16.7643-1.3974e-07j, 16.7642-7.5980e-08j, 16.7647-1.0818e-07j,\n",
            "        16.7645+5.8410e-08j, 16.7647+1.4959e-07j, 16.7649+3.3530e-08j,\n",
            "        16.7649+1.0297e-07j, 16.7652+1.3651e-08j, 16.7652+1.3647e-08j,\n",
            "        16.7654-6.0899e-08j, 16.7652-1.3743e-07j, 16.7656-2.9995e-08j,\n",
            "        16.7657-8.9317e-09j, 16.7661-9.0838e-09j, 16.7664+4.1492e-08j,\n",
            "        16.7662-6.0748e-08j, 16.7666+2.4736e-08j, 16.7666-2.8198e-08j,\n",
            "        16.7668+8.1228e-08j, 16.7665-3.8479e-08j, 16.7669-3.6435e-08j,\n",
            "        16.7670-1.2203e-07j, 16.7668+5.5421e-09j, 16.7670-5.0905e-09j,\n",
            "        16.7669-4.7821e-08j, 16.7672+6.2534e-08j, 16.7675-5.7988e-08j,\n",
            "        16.7674+3.4760e-09j, 16.7672-4.9317e-08j, 16.7678+9.0176e-08j,\n",
            "        16.7675+5.7464e-08j, 16.7678+1.0689e-08j, 16.7682-3.2751e-08j,\n",
            "        16.7681+6.9418e-08j, 16.7681+1.3218e-07j, 16.7683-7.3738e-08j,\n",
            "        16.7682+5.6080e-08j, 16.7684+7.1338e-08j, 16.7686-1.1266e-08j,\n",
            "        16.7682-7.8923e-08j, 16.7685-4.6765e-08j, 16.7689-3.3881e-08j,\n",
            "        16.7686-8.8479e-08j, 16.7685+1.3434e-08j, 16.7689+5.2745e-08j,\n",
            "        16.7690+9.2437e-08j, 16.7689+2.5601e-08j, 16.7693+4.3370e-08j,\n",
            "        16.7694+1.0213e-07j, 16.7691-5.0587e-08j, 16.7694+5.1661e-08j,\n",
            "        16.7693-9.9565e-09j, 16.7694+1.1977e-08j, 16.7694-1.3596e-07j,\n",
            "        16.7696-2.1403e-08j, 16.7697-1.5304e-08j, 16.7696-2.3331e-09j,\n",
            "        16.7698-1.3442e-08j, 16.7700+6.5295e-08j, 16.7702-1.8891e-08j,\n",
            "        16.7701+1.1297e-07j, 16.7702-1.8305e-08j, 16.7703-2.3452e-09j,\n",
            "        16.7703-3.6265e-08j, 16.7704+1.0858e-07j, 16.7703+8.8867e-08j,\n",
            "        16.7704+5.8026e-08j, 16.7704+9.0987e-08j, 16.7705-1.8170e-08j,\n",
            "        16.7706-7.8012e-08j, 16.7706-7.8012e-08j, 16.7707+4.5877e-08j,\n",
            "        16.7706+2.1134e-09j])\n",
            "SNR time: 2778.96 seconds\n",
            "SNR = 1\n",
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  135.89  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  136.36  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  134.31  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  134.89  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  137.9  seconds\n",
            "SNR time: 2778.27 seconds\n",
            "SNR = 2\n",
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  143.89  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  147.57  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  149.24  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  149.91  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  145.34  seconds\n",
            "SNR time: 3140.84 seconds\n",
            "SNR = 3\n",
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  138.55  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  133.26  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  134.28  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  132.48  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  146.23  seconds\n",
            "SNR time: 3146.29 seconds\n",
            "SNR = 4\n",
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  141.85  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  141.25  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  138.92  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  140.07  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  138.18  seconds\n",
            "SNR time: 3168.56 seconds\n",
            "SNR = 5\n",
            "mu: 0.05 beta: 0.9\n",
            "mu: 0.05 beta: 0.94\n",
            "mu: 0.05 beta: 0.99\n",
            "mu: 0.37 beta: 0.9\n",
            "mu: 0.37 beta: 0.94\n",
            "mu: 0.37 beta: 0.99\n",
            "mu: 0.68 beta: 0.9\n",
            "mu: 0.68 beta: 0.94\n",
            "mu: 0.68 beta: 0.99\n",
            "mu: 1.0 beta: 0.9\n",
            "mu: 1.0 beta: 0.94\n",
            "mu: 1.0 beta: 0.99\n",
            "mu: 1.5 beta: 0.9\n",
            "mu: 1.5 beta: 0.94\n",
            "mu: 1.5 beta: 0.99\n",
            "mu: 3.5 beta: 0.9\n",
            "mu: 3.5 beta: 0.94\n",
            "mu: 3.5 beta: 0.99\n",
            "mu: 5.5 beta: 0.9\n",
            "mu: 5.5 beta: 0.94\n",
            "mu: 5.5 beta: 0.99\n",
            "mu: 7.5 beta: 0.9\n",
            "mu: 7.5 beta: 0.94\n",
            "mu: 7.5 beta: 0.99\n",
            "Momentum starts training\n",
            "epoch:  1  of  5\n",
            "epoch time:  156.08  seconds\n",
            "epoch:  2  of  5\n",
            "epoch time:  140.52  seconds\n",
            "epoch:  3  of  5\n",
            "epoch time:  140.37  seconds\n",
            "epoch:  4  of  5\n",
            "epoch time:  141.14  seconds\n",
            "epoch:  5  of  5\n",
            "epoch time:  139.7  seconds\n",
            "SNR time: 3220.5 seconds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_tensor.py:972: ComplexWarning: Casting complex values to real discards the imaginary part\n",
            "  return self.numpy().astype(dtype, copy=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "line search max:  [tensor(16.6707), tensor(17.7822), tensor(18.9000), tensor(20.0280), tensor(21.1596), tensor(22.2956)]\n",
            "pga 400 max:  [tensor(16.7706+2.1134e-09j), tensor(17.8812-3.0031e-08j), tensor(19.0027-1.6587e-08j), tensor(20.1286+1.8045e-07j), tensor(21.2626-1.4352e-07j), tensor(22.3985-3.3608e-08j)]\n",
            "pga 10 max:  [tensor(15.8584-9.8430e-08j), tensor(17.0362-4.6594e-08j), tensor(18.0781-6.0298e-08j), tensor(19.1999-1.2746e-07j), tensor(20.3276+1.1349e-08j), tensor(21.5382-3.7578e-08j)]\n",
            "momentum max:  [tensor(16.5777-2.3123e-08j, grad_fn=<SelectBackward0>), tensor(17.6926-5.3908e-09j, grad_fn=<SelectBackward0>), tensor(18.8081-9.1880e-09j, grad_fn=<SelectBackward0>), tensor(19.9395-3.1018e-08j, grad_fn=<SelectBackward0>), tensor(21.0724-1.0050e-08j, grad_fn=<SelectBackward0>), tensor(22.2193-1.3544e-07j, grad_fn=<SelectBackward0>)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_Mu_W(momentum_model.Mu_W,J)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Z0vD4LmmzTpE",
        "outputId": "bfbeca1d-b1a3-43d2-b606-1315cb9f3655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x450 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_betas(momentum_model.betas,J)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "vmoAMO45_gLR",
        "outputId": "671b1a5e-c0eb-4fc4-da88-b7fb3550671d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x450 with 11 Axes>"
            ],
            "image/png": 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