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Last active January 9, 2019 13:02
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"金子先生の[demo_opt_gtm_with_k3nerror.ipynb](https://github.com/hkaneko1985/gtm-generativetopographicmapping/blob/master/Python/demo_opt_gtm_with_k3nerror.ipynb)を参考に行なっています"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.figure as figure\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import numpy as np\n",
"from sklearn.datasets import load_boston\n",
"\n",
"from gtm import gtm\n",
"from k3nerror import k3nerror"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# settings\n",
"candidates_of_shape_of_map = np.arange(30, 31, dtype=int)\n",
"candidates_of_shape_of_rbf_centers = np.arange(2, 22, 2, dtype=int)\n",
"candidates_of_variance_of_rbfs = 2 ** np.arange(-5, 4, 2, dtype=float)\n",
"candidates_of_lambda_in_em_algorithm = 2 ** np.arange(-4, 0, dtype=float)\n",
"candidates_of_lambda_in_em_algorithm = np.append(0, candidates_of_lambda_in_em_algorithm)\n",
"number_of_iterations = 300\n",
"display_flag = 0\n",
"k_in_k3nerror = 10\n",
"\n",
"# load an iris dataset\n",
"boston = load_boston()\n",
"# input_dataset = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
"input_dataset = boston.data\n",
"color = boston.target\n",
"\n",
"# autoscaling\n",
"input_dataset = (input_dataset - input_dataset.mean(axis=0)) / input_dataset.std(axis=0, ddof=1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"CPU times: user 1h 8min 15s, sys: 3min 4s, total: 1h 11min 20s\n",
"Wall time: 13min 22s\n"
]
}
],
"source": [
"%%time\n",
"# grid search\n",
"parameters_and_k3nerror = []\n",
"all_calculation_numbers = len(candidates_of_shape_of_map) * len(candidates_of_shape_of_rbf_centers) * len(\n",
" candidates_of_variance_of_rbfs) * len(candidates_of_lambda_in_em_algorithm)\n",
"calculation_number = 0\n",
"for shape_of_map_grid in candidates_of_shape_of_map:\n",
" for shape_of_rbf_centers_grid in candidates_of_shape_of_rbf_centers:\n",
" for variance_of_rbfs_grid in candidates_of_variance_of_rbfs:\n",
" for lambda_in_em_algorithm_grid in candidates_of_lambda_in_em_algorithm:\n",
" calculation_number += 1\n",
" print([calculation_number, all_calculation_numbers])\n",
" # construct GTM model\n",
" model = gtm([shape_of_map_grid, shape_of_map_grid],\n",
" [shape_of_rbf_centers_grid, shape_of_rbf_centers_grid],\n",
" variance_of_rbfs_grid, lambda_in_em_algorithm_grid, number_of_iterations, display_flag)\n",
" model.fit(input_dataset)\n",
" if model.success_flag:\n",
" # calculate of responsibilities\n",
" responsibilities = model.responsibility(input_dataset)\n",
" # calculate the mean of responsibilities\n",
" means = responsibilities.dot(model.map_grids)\n",
" # calculate k3n-error\n",
" k3nerror_of_gtm = k3nerror(input_dataset, means, k_in_k3nerror)\n",
" else:\n",
" k3nerror_of_gtm = 10 ** 100\n",
" parameters_and_k3nerror.append(\n",
" [shape_of_map_grid, shape_of_rbf_centers_grid, variance_of_rbfs_grid, lambda_in_em_algorithm_grid,\n",
" k3nerror_of_gtm])\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# optimized GTM\n",
"parameters_and_k3nerror = np.array(parameters_and_k3nerror)\n",
"optimized_hyperparameter_number = \\\n",
" np.where(parameters_and_k3nerror[:, 4] == np.min(parameters_and_k3nerror[:, 4]))[0][0]\n",
"shape_of_map = [parameters_and_k3nerror[optimized_hyperparameter_number, 0],\n",
" parameters_and_k3nerror[optimized_hyperparameter_number, 0]]\n",
"shape_of_rbf_centers = [parameters_and_k3nerror[optimized_hyperparameter_number, 1],\n",
" parameters_and_k3nerror[optimized_hyperparameter_number, 1]]\n",
"variance_of_rbfs = parameters_and_k3nerror[optimized_hyperparameter_number, 2]\n",
"lambda_in_em_algorithm = parameters_and_k3nerror[optimized_hyperparameter_number, 3]\n",
"\n",
"# construct GTM model\n",
"model = gtm(shape_of_map, shape_of_rbf_centers, variance_of_rbfs, lambda_in_em_algorithm, number_of_iterations,\n",
" display_flag)\n",
"model.fit(input_dataset)\n",
"\n",
"# calculate of responsibilities\n",
"responsibilities = model.responsibility(input_dataset)\n",
"\n",
"# plot the mean of responsibilities\n",
"means = responsibilities.dot(model.map_grids)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6871224951356412"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k3nerror(input_dataset, means, 10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(5, 5))\n",
"plt.scatter(means[:, 0], means[:, 1], c=color)\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.xlim(-1.1, 1.1)\n",
"plt.xlabel(\"z1 (mean)\")\n",
"plt.ylabel(\"z2 (mean)\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters\n",
"Shape of map = 30\n",
"Shape of RBF centers = 4\n",
"Variance of RBFs = 0.125\n",
"Lambda in EM algorithm = 0.0625\n"
]
}
],
"source": [
"print(f'Best parameters')\n",
"print(f'Shape of map = {shape_of_map[0]}')\n",
"print(f'Shape of RBF centers = {shape_of_rbf_centers[0]}')\n",
"print(f'Variance of RBFs = {variance_of_rbfs}')\n",
"print(f'Lambda in EM algorithm = {lambda_in_em_algorithm}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EOF "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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