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rasbtrasbt
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small cosmetic changes
1 parent e0e87da commit 4052010

27 files changed

Lines changed: 143 additions & 113 deletions

‎ch01/ch01.ipynb‎

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{
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"cell_type": "markdown",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [

‎ch02/ch02.ipynb‎

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‎ch02/ch02.py‎

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class Perceptron():
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class Perceptron:
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"""Perceptron classifier.
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Parameters
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class AdalineGD():
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class AdalineGD:
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"""ADAptive LInear NEuron classifier.
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Parameters
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Attributes
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-----------
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w_ : 1d-array
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Weights after fitting.
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b_ : Scalar
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Bias unit after fitting.
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losses_ : list
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class AdalineSGD():
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class AdalineSGD:
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"""ADAptive LInear NEuron classifier.
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Parameters
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plt.legend(loc='upper left')
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plt.tight_layout()
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#plt.savefig('figures/02_15_1.png', dpi=300)
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plt.savefig('figures/02_15_1.png', dpi=300)
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plt.show()
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plt.plot(range(1, len(ada_sgd.losses_) + 1), ada_sgd.losses_, marker='o')
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plt.xlabel('Epochs')
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plt.ylabel('Average loss')
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#plt.savefig('figures/02_15_2.png', dpi=300)
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plt.savefig('figures/02_15_2.png', dpi=300)
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plt.show()
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‎ch02/figures/02_15_1.png‎

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‎ch02/figures/02_15_2.png‎

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‎ch05/ch05.ipynb‎

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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"source": [
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"## Package version checks"
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]
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"text": [
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"[OK] Your Python version is 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:35:11) \n",
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"[Clang 11.1.0 ]\n",
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"[OK] numpy 1.21.4\n",
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"[OK] numpy 1.21.5\n",
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"[OK] matplotlib 3.5.1\n",
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"[OK] sklearn 1.0.1\n",
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"[OK] pandas 1.3.5\n"
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"text": [
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"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n",
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"[NbConvertApp] Converting notebook ch05.ipynb to script\n",
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"[NbConvertApp] Writing 20297 bytes to ch05.py\n"
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"[NbConvertApp] Writing 20329 bytes to ch05.py\n"
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]
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}
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],

‎ch05/ch05.py‎

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plt.xlabel('Principal component index')
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plt.legend(loc='best')
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plt.tight_layout()
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# plt.savefig('images/05_02.png', dpi=300)
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# plt.savefig('figures/05_02.png', dpi=300)
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plt.show()
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plt.ylabel('PC 2')
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plt.legend(loc='lower left')
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plt.tight_layout()
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# plt.savefig('images/05_03.png', dpi=300)
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# plt.savefig('figures/05_03.png', dpi=300)
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plt.show()
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plt.ylabel('PC 2')
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plt.legend(loc='lower left')
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plt.tight_layout()
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# plt.savefig('images/05_04.png', dpi=300)
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# plt.savefig('figures/05_04.png', dpi=300)
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plt.show()
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plt.ylabel('PC 2')
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plt.legend(loc='lower left')
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plt.tight_layout()
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# plt.savefig('images/05_05.png', dpi=300)
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# plt.savefig('figures/05_05.png', dpi=300)
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plt.show()
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plt.ylim([-1, 1])
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plt.tight_layout()
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plt.savefig('images/05_05_02.png', dpi=300)
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plt.savefig('figures/05_05_02.png', dpi=300)
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plt.show()
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plt.ylim([-1, 1])
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plt.tight_layout()
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plt.savefig('images/05_05_03.png', dpi=300)
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plt.savefig('figures/05_05_03.png', dpi=300)
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plt.show()
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plt.ylim([-0.1, 1.1])
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plt.legend(loc='best')
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plt.tight_layout()
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#plt.savefig('images/05_07.png', dpi=300)
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#plt.savefig('figures/05_07.png', dpi=300)
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plt.show()
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plt.ylabel('LD 2')
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plt.legend(loc='lower right')
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plt.tight_layout()
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plt.savefig('images/05_08.png', dpi=300)
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plt.savefig('figures/05_08.png', dpi=300)
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plt.show()
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plt.ylabel('LD 2')
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plt.legend(loc='lower left')
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plt.tight_layout()
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# plt.savefig('images/05_09.png', dpi=300)
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# plt.savefig('figures/05_09.png', dpi=300)
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plt.show()
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plt.ylabel('LD 2')
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plt.legend(loc='lower left')
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plt.tight_layout()
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# plt.savefig('images/05_10.png', dpi=300)
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# plt.savefig('figures/05_10.png', dpi=300)
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plt.show()
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for i in range(4):
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ax[i].imshow(digits.images[i], cmap='Greys')
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# plt.savefig('images/05_12.png', dpi=300)
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# plt.savefig('figures/05_12.png', dpi=300)
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plt.show()
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PathEffects.Normal()])
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plot_projection(X_digits_tsne, y_digits)
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# plt.savefig('images/05_13.png', dpi=300)
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# plt.savefig('figures/05_13.png', dpi=300)
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plt.show()
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‎ch06/ch06.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"source": [
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"# Machine Learning with PyTorch and Scikit-Learn \n",
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"# -- Code Examples"
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",

‎ch07/ch07.ipynb‎

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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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},
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"tags": []
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},
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"outputs": [
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" def get_params(self, deep=True):\n",
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" \"\"\" Get classifier parameter names for GridSearch\"\"\"\n",
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" if not deep:\n",
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" return super(MajorityVoteClassifier, self).get_params(deep=False)\n",
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" return super().get_params(deep=False)\n",
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" else:\n",
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" out = self.named_classifiers.copy()\n",
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" for name, step in self.named_classifiers.items():\n",
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"text": [
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"[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n",
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"[NbConvertApp] Converting notebook ch07.ipynb to script\n",
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"[NbConvertApp] Writing 24238 bytes to ch07.py\n"
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"[NbConvertApp] Writing 24252 bytes to ch07.py\n"
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]
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}
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],
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"source": [
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"! python ../.convert_notebook_to_script.py --input ch07.ipynb --output ch07.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎ch07/ch07.py‎

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'scipy': '1.7.0',
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'matplotlib': '3.4.3',
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'sklearn': '1.0',
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'pandas': '1.3.2'
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'pandas': '1.3.2',
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'xgboost': '1.5.0',
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check_packages(d)
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#plt.savefig('images/07_03.png', dpi=300)
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#plt.savefig('figures/07_03.png', dpi=300)
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def get_params(self, deep=True):
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#plt.savefig('figures/07_04', dpi=300)
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plt.show()
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#plt.savefig('figures/07_05', dpi=300)
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plt.show()
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#plt.savefig('figures/07_08.png', dpi=300, bbox_inches='tight')
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#plt.savefig('images/07_11.png', dpi=300, bbox_inches='tight')
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#plt.savefig('figures/07_11.png', dpi=300, bbox_inches='tight')
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