diff --git a/PGPE_equality_test.ipynb b/PGPE_equality_test.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "aboriginal-clarity",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import cm\n",
+ "import math\n",
+ "import numpy as np\n",
+ "from benchmarks.functions import rastrigin\n",
+ "from black_box_optimizers.pgpe_spielplatz import SyS_PGPE_neweps, PGPE, RandomSearch, Learner, SyS_PGPE_MS_neweps\n",
+ "from black_box_optimizers.hillclimber import HillClimber, SmartHillClimber\n",
+ "from optimization.optimizer import OptimizeableParameterDict as OP\n",
+ "from scipy import stats\n",
+ "import pickle as cp\n",
+ "import datetime\n",
+ "from scipy.stats import levene"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00b3ddd1-0389-4682-a9b6-a63e2d6f50e4",
+ "metadata": {},
+ "source": [
+ "# Fragen\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3fc4808-a040-44d6-8a33-cd058ae3a684",
+ "metadata": {},
+ "source": [
+ "# PGPE Parameter initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "88d5bfcb-0a75-4486-a535-6f53c3bbc891",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shared parameters\n",
+ "\n",
+ "# Path to witch the results are saved\n",
+ "res_path = \"./benchmarks/bench_results/\"\n",
+ "# Path to witch the result-plots are saved\n",
+ "fig_path = \"./benchmarks/bench_plots/\"\n",
+ "# Experiment name (becomes part of the save file names)\n",
+ "exp_name = \"_fast\"\n",
+ "\n",
+ "# List of benchmark problems that will be conducted \n",
+ "problem_list = [\"rastrigin\"]\n",
+ "\n",
+ "# problem dimension to probe\n",
+ "dim_list = [2, 10, 100]\n",
+ "\n",
+ "# Number of evaluations per algorithm per problem allowed to be used\n",
+ "opt_iter = [3000, 6000, 16000] \n",
+ "\n",
+ "trainings = 100\n",
+ "\n",
+ "# standard pgpe parameters\n",
+ "mue_alpha_s = 0.1 \n",
+ "sigma_alpha_s = 0.05\n",
+ "\n",
+ "# norm pgpe parameters\n",
+ "mue_alpha_n = mue_alpha_s \n",
+ "sigma_alpha_n = sigma_alpha_s \n",
+ "\n",
+ "# new pgpge parameters\n",
+ "mue_alpha_ms = mue_alpha_s\n",
+ "sigma_alpha_ms = sigma_alpha_s\n",
+ "\n",
+ "# combined version parameters\n",
+ "mue_alpha_c = 0.1\n",
+ "sigma_alpha_c = 0.05\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2361909-20f2-4863-80b6-7d24c936bc8f",
+ "metadata": {},
+ "source": [
+ "## Functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "boring-stage",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Creating the Optimizable Parameter Dict\n",
+ "def get_para_dict(dimension):\n",
+ " para_dict = OP()\n",
+ " for i in range(dimension):\n",
+ " para_dict.add_parameter(\n",
+ " key=f\"var_{i}\",\n",
+ " min=-10.0,\n",
+ " max=10.0,\n",
+ " type=\"f\", # f=float, i=int, b=1-float\n",
+ " scale=\"linear\", # linear, exp=exponential, ixp=inverse exponential\n",
+ " init=8.0, # if not provided uses center of search intervall\n",
+ " id=i, # if not provided uses internal counter\n",
+ " )\n",
+ " return para_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "imperial-stadium",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_algo(algo_kind, para_dict, mue_alpha, sigma_alpha):\n",
+ " seed = np.array([para_dict.get_init_parameters_array()])\n",
+ " para_dict = para_dict.parameter_dict\n",
+ "\n",
+ " if algo_kind == \"sys_pgpe\" or algo_kind == \"sys_pgpe_norm\":\n",
+ " algorithm = SyS_PGPE_neweps(2,\n",
+ " paras=para_dict,\n",
+ " plot_paras = True,\n",
+ " seed=seed,\n",
+ " mue_alpha=mue_alpha,\n",
+ " sigma_alpha=sigma_alpha\n",
+ " )\n",
+ " elif algo_kind == \"sys_pgpe_ms\" or algo_kind == 'sys_pgpe_ms_norm':\n",
+ " algorithm = SyS_PGPE_MS_neweps(2,\n",
+ " paras=para_dict,\n",
+ " plot_paras = True,\n",
+ " seed=seed, \n",
+ " mue_alpha = mue_alpha,\n",
+ " sigma_alpha = sigma_alpha\n",
+ " )\n",
+ " \n",
+ " return algorithm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fluid-netherlands",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_problem(pop, problem):\n",
+ " if problem == 'rastrigin':\n",
+ " return - rastrigin(pop).reshape(-1,)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "7a3519fa-8725-4e32-8f75-8dc31593095c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_sample(optimizer, dim, trainings, opt_iter, mue_alpha, sigma_alpha, problem='rastrigin'): \n",
+ " #print(\"dim: \", dim)\n",
+ " samples = []\n",
+ " for n in range(trainings):\n",
+ " sample = [] # samples of specific dimension\n",
+ " para_dict = get_para_dict(dim) #initialize the parameters for the current problem dimension\n",
+ " # get the algorithm object\n",
+ " algorithm = get_algo(algo_kind=optimizer, \n",
+ " para_dict=para_dict,\n",
+ " mue_alpha = mue_alpha,\n",
+ " sigma_alpha = sigma_alpha\n",
+ " )\n",
+ " #print(\"algorithm: \", algorithm)\n",
+ " for i in range(opt_iter):\n",
+ " #äprint(f\"training: {n} iteration: {i}\")\n",
+ " #print(algorithm.baseline)\n",
+ " population = algorithm.ask() # gibt mir das Sample\n",
+ " #print(\"sample: \", population)\n",
+ " rewards = evaluate_problem(pop=population, \n",
+ " problem=problem\n",
+ " )\n",
+ " #print(\"rewards: \", rewards)\n",
+ " if \"norm\" in optimizer:\n",
+ " # tell with reward normalization \n",
+ " algorithm.tell_norm(pop=population, \n",
+ " rewards=rewards, \n",
+ " cfg=None,\n",
+ " logger=None\n",
+ " )\n",
+ " baseline_norm = algorithm.baseline_act\n",
+ " baseline = baseline_norm*(algorithm.max_tell-algorithm.min_tell)+algorithm.min_tell\n",
+ " sample.append(baseline)\n",
+ " else: \n",
+ " algorithm.tell(pop=population, \n",
+ " rewards=rewards, \n",
+ " cfg=None,\n",
+ " logger=None\n",
+ " )\n",
+ " sample.append(algorithm.baseline_act)\n",
+ " if \"norm\" in optimizer: \n",
+ " x = algorithm.baseline_act*(algorithm.max_tell-algorithm.min_tell)+algorithm.min_tell\n",
+ " else: \n",
+ " x = algorithm.baseline_act\n",
+ " samples.append(x)\n",
+ " \"\"\"\n",
+ " n = len(samples)\n",
+ " if n>10:\n",
+ " std = np.std(samples)\n",
+ " con = (2.576*std)/np.sqrt(n)\n",
+ " print(\"con: \", con)\n",
+ " if con <= 0.01*(sum(samples)/n):\n",
+ " break\n",
+ " \"\"\"\n",
+ " return np.array(samples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "70469bb7-caeb-40fc-8a77-c4ea5c75ff89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# T-Test oder Welch-Test for equality\n",
+ "def test_equality(sample1, sample2):\n",
+ " \n",
+ " # Levene Test for variance equality\n",
+ " stat, p = levene(sample1, sample2, center = 'mean')\n",
+ " if p>0.05:\n",
+ " result = stats.ttest_ind(sample1, \n",
+ " sample2, \n",
+ " equal_var=True, # t-Test\n",
+ " nan_policy='raise'\n",
+ " )\n",
+ " elif p<=0.05:\n",
+ " result = stats.ttest_ind(sample1, \n",
+ " sample2, \n",
+ " equal_var=False, # Welch Test\n",
+ " nan_policy='raise'\n",
+ " )\n",
+ " else:\n",
+ " print(\"bei levene stimmt was nicht\")\n",
+ " print(\"levene p: \", p)\n",
+ "\n",
+ " return result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "102e7342-7f46-436a-8797-8487d1236261",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_sample(sample, name):\n",
+ " # save samples\n",
+ " # Generate a timestamp\n",
+ " timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n",
+ "\n",
+ " # Define the filename with the timestamp\n",
+ " filename = f'samples/{name}_samples_{timestamp}.npy'\n",
+ "\n",
+ " # Save the array to a file with the timestamped filename\n",
+ " np.save(filename, sample)\n",
+ " return filename"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d55505f2-9564-4851-90a2-850ae09af7bd",
+ "metadata": {},
+ "source": [
+ "## Create samples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "62f08f9f-a7d8-42de-93ef-3624d35d9382",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_samples = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "0cb5a501-4c69-44f6-b1a5-78411317f60f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/student3/.conda/envs/ml2025/repositories/optimization/black_box_optimizers/pgpe_spielplatz.py:554: RuntimeWarning: invalid value encountered in double_scalars\n",
+ " mue_gradient = r_tilde / (self.best - self.rewards.mean() + eps)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dim: 2\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "scale < 0",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dim \u001b[38;5;129;01min\u001b[39;00m dim_list:\n\u001b[1;32m 3\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m----> 4\u001b[0m sys_pgpe_sample \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msys_pgpe\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt_iter\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmue_alpha_s\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigma_alpha_s\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdim: \u001b[39m\u001b[38;5;124m\"\u001b[39m, dim)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m#print(\"sys pgpe: \", sys_pgpe_sample)\u001b[39;00m\n",
+ "Cell \u001b[0;32mIn[6], line 17\u001b[0m, in \u001b[0;36mcreate_sample\u001b[0;34m(optimizer, dim, trainings, opt_iter, mue_alpha, sigma_alpha, problem)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m#print(\"algorithm: \", algorithm)\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(opt_iter):\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#äprint(f\"training: {n} iteration: {i}\")\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m#print(algorithm.baseline)\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m population \u001b[38;5;241m=\u001b[39m \u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# gibt mir das Sample\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m#print(\"sample: \", population)\u001b[39;00m\n\u001b[1;32m 19\u001b[0m rewards \u001b[38;5;241m=\u001b[39m evaluate_problem(pop\u001b[38;5;241m=\u001b[39mpopulation, \n\u001b[1;32m 20\u001b[0m problem\u001b[38;5;241m=\u001b[39mproblem\n\u001b[1;32m 21\u001b[0m )\n",
+ "File \u001b[0;32m~/.conda/envs/ml2025/repositories/optimization/black_box_optimizers/pgpe_spielplatz.py:265\u001b[0m, in \u001b[0;36mLearner.ask\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mask\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 258\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;124;03m Ask for a set of individuals to be evaluated.\u001b[39;00m\n\u001b[1;32m 260\u001b[0m \n\u001b[1;32m 261\u001b[0m \u001b[38;5;124;03m Returns:\u001b[39;00m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;124;03m - numpy.ndarray: List of individuals, ready to be evaluated.\u001b[39;00m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mclip(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_min, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_max)\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# print(\"pop: \", self.pop)\u001b[39;00m\n",
+ "File \u001b[0;32m~/.conda/envs/ml2025/repositories/optimization/black_box_optimizers/pgpe_spielplatz.py:543\u001b[0m, in \u001b[0;36mSyS_PGPE_neweps._sample\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_sample\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 542\u001b[0m \u001b[38;5;66;03m# generates a difference vector with the given standard deviations\u001b[39;00m\n\u001b[0;32m--> 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeltas \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msigma\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# drawing the perturbation from a normal distribution with corresponding sigmas\u001b[39;00m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop_size, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmue\u001b[38;5;241m.\u001b[39msize), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpop[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmue \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeltas \u001b[38;5;66;03m# Adding perturbation to cur. hypo. for first sample\u001b[39;00m\n",
+ "File \u001b[0;32mmtrand.pyx:1510\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.normal\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m_common.pyx:600\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m_common.pyx:505\u001b[0m, in \u001b[0;36mnumpy.random._common.cont_broadcast_2\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m_common.pyx:384\u001b[0m, in \u001b[0;36mnumpy.random._common.check_array_constraint\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: scale < 0"
+ ]
+ }
+ ],
+ "source": [
+ "sys_samples = []\n",
+ "for dim in dim_list:\n",
+ " i = 1\n",
+ " sys_pgpe_sample = create_sample('sys_pgpe', dim, trainings, opt_iter[i], mue_alpha_s, sigma_alpha_s)\n",
+ " print(\"dim: \", dim)\n",
+ " #print(\"sys pgpe: \", sys_pgpe_sample)\n",
+ " sys_samples.append(sys_pgpe_sample)\n",
+ " i += 1\n",
+ "sys_filename = save_sample(sys_samples, \"sys_pgpe_1000_trainings_3000_iterations_2_10_100_dim\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3233ea5d-2268-46b3-8b98-4f41ea91c336",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sys_norm_samples = []\n",
+ "for dim in dim_list:\n",
+ " i = 1\n",
+ " sys_pgpe_norm_sample = create_sample('sys_pgpe_norm', dim, trainings, opt_iter[i], mue_alpha_n, sigma_alpha_n)\n",
+ " print(\"dim: \", dim)\n",
+ " #print(\"pgpe normalized rewards: \", sys_pgpe_norm_sample)\n",
+ " sys_norm_samples.append(sys_pgpe_norm_sample)\n",
+ " i += 1\n",
+ "sys_norm_filename = save_sample(sys_norm_samples, \"sys_pgpe_norm_1000_trainings_3000_iterations_2_10_100_dim\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6771ea52-fd6e-4718-ae54-a8861f54cd2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sys_ms_samples = []\n",
+ "for dim in dim_list:\n",
+ " i = 1\n",
+ " sys_pgpe_ms_sample = create_sample('sys_pgpe_ms', dim, trainings, opt_iter[i], mue_alpha_ms, sigma_alpha_ms)\n",
+ " print(\"dim: \", dim)\n",
+ " #print(\"pgpe new update rule: \", sys_pgpe_ms_sample)\n",
+ " sys_ms_samples.append(sys_pgpe_ms_sample)\n",
+ " i += 1\n",
+ "sys_ms_filename = save_sample(sys_ms_samples, \"sys_ms_pgpe_1000_trainings_3000_iterations_2_10_100_dim\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f39f1029-a2a5-4eb5-b958-84c358f92cd8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sys_ms_norm_samples = []\n",
+ "for dim in dim_list:\n",
+ " i = 1\n",
+ " sys_pgpe_ms_norm_sample = create_sample('sys_pgpe_ms_norm', dim, trainings, opt_iter[i], mue_alpha_ms, sigma_alpha_ms)\n",
+ " print(\"dim: \", dim)\n",
+ " #print(\"pgpe new update rule: \", sys_pgpe_ms_sample)\n",
+ " sys_ms_norm_samples.append(sys_pgpe_ms_norm_sample)\n",
+ " i += 1\n",
+ "sys_ms_norm_filename = save_sample(sys_ms_norm_samples, \"sys_ms_norm_pgpe_1000_trainings_3000_iterations_2_10_100_dim\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc068c17-4187-4e22-ba2c-9f1334ac2e5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the saved array\n",
+ "sys_samples = np.load(sys_filename, allow_pickle=True)\n",
+ "sys_norm_samples = np.load(sys_norm_filename, allow_pickle=True)\n",
+ "sys_ms_samples = np.load(sys_ms_filename, allow_pickle=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b08c41b-5bc1-4685-ab11-3bcbdf3ea108",
+ "metadata": {},
+ "source": [
+ "## Test samples for equality"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9ae75e4-eef1-44c1-85c4-969ecd0dc388",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test equality to norm for 2 dimensions\n",
+ "sys_norm_result_2d = test_equality(sys_samples[0], sys_norm_samples[0])\n",
+ "print(\"sys norm result 2d: \", sys_norm_result_2d)\n",
+ "# test equality to norm for 10 dimensions\n",
+ "sys_norm_result_10d = test_equality(sys_samples[1], sys_norm_samples[1])\n",
+ "print(\"sys norm result 10d: \", sys_norm_result_10d)\n",
+ "# test equality to norm for 100 dimensions\n",
+ "sys_norm_result_100d = test_equality(sys_samples[2], sys_norm_samples[2])\n",
+ "print(\"sys norm result 100d: \", sys_norm_result_100d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c49cffe-4bf1-4222-8ac9-24849128b54f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test equality to ms for 2 dimensions\n",
+ "sys_ms_result_2d = test_equality(sys_samples[0], sys_ms_samples[0])\n",
+ "print(\"sys ms result 2d: \", sys_ms_result_2d)\n",
+ "# test equality to ms for 10 dimensions\n",
+ "sys_ms_result_10d = test_equality(sys_samples[1], sys_ms_samples[1])\n",
+ "print(\"sys ms result 10d: \", sys_ms_result_10d)\n",
+ "# test equality to ms for 10 dimensions\n",
+ "sys_ms_result_100d = test_equality(sys_samples[2], sys_ms_samples[2])\n",
+ "print(\"sys ms result 100d: \", sys_ms_result_100d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0e9c3ca-ac3b-4467-b977-b2a6d53c8125",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f069f342-1e4b-4b78-9d62-75adf7b6bab3",
+ "metadata": {},
+ "source": [
+ "# T-Tests\n",
+ "## Paratmeter\n",
+ "- 100 Trainings\n",
+ "- 2D a 3000 Iterationen\n",
+ "- 10D a 6000 Iterationen\n",
+ "- 100D a 16.000 Iterationen\n",
+ "- mue_alpha = 0.1 \n",
+ "- sigma_alpha = 0.05\n",
+ "\n",
+ "## Ergebnisse\n",
+ "- result_norm_2d: Ttest_indResult(statistic=1.000015377088226, pvalue=0.3197410700120563)\n",
+ "- result_norm_10d: Ttest_indResult(statistic=-0.13399941843316795, pvalue=0.8935391374922611)\n",
+ "- result_norm_100d: Ttest_indResult(statistic=1.1873941552147524, pvalue=0.23649471653035284)\n",
+ "\n",
+ "- result_new_2d: Ttest_indResult(statistic=-0.9692005435684301, pvalue=0.3336275551349407)\n",
+ "- result_new_10d: Ttest_indResult(statistic=0.7816646887048703, pvalue=0.4353453428317401)\n",
+ "- result_new_100d: Ttest_indResult(statistic=-0.8944711470444833, pvalue=0.3721558659216999)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f23b2ed4-0477-4a2e-9e01-772096c1b596",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}