From 9c1cfb4316d931c7381498591b2671eeb0418393 Mon Sep 17 00:00:00 2001
From: Mikolaj Rybinski <mikolaj.rybinski@id.ethz.ch>
Date: Fri, 23 Aug 2019 15:01:11 +0200
Subject: [PATCH] copyright notice
---
02_classification.ipynb | 9 +++++-
03_overfitting_and_cross_validation.ipynb | 9 +++++-
04_measuring_quality_of_a_classifier.ipynb | 9 +++++-
...ines_and_hyperparameter_optimization.ipynb | 2 +-
06_classifiers_overview.ipynb | 2 +-
07_regression.ipynb | 10 +++----
08_neural_networks.ipynb | 30 +++++++++++--------
7 files changed, 49 insertions(+), 22 deletions(-)
diff --git a/02_classification.ipynb b/02_classification.ipynb
index 77fa998..ac272ba 100644
--- a/02_classification.ipynb
+++ b/02_classification.ipynb
@@ -2014,6 +2014,13 @@
" In <code>scikit-learn</code> many classifiers support multi-class problems out of the box and also offer functionalities to implement <strong>one-vs-all</strong> or <strong>one-vs-one</strong> in some cases (cf. <a href=\"https://scikit-learn.org/stable/modules/multiclass.html\"><code>scikit-learn</code> multiclass and multilabel algorithms</a>).\n",
"</p></div>"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Copyright (C) 2019 ETH Zurich, SIS ID"
+ ]
}
],
"metadata": {
@@ -2033,7 +2040,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/03_overfitting_and_cross_validation.ipynb b/03_overfitting_and_cross_validation.ipynb
index e84f772..e2159a7 100644
--- a/03_overfitting_and_cross_validation.ipynb
+++ b/03_overfitting_and_cross_validation.ipynb
@@ -1395,6 +1395,13 @@
"print(\"BEST RESULT CROSS VALIDATION\")\n",
"print(\"score = {:.3f} C={:.1f} gamma={:.1f}\".format(best_score, C, gamma))"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Copyright (C) 2019 ETH Zurich, SIS ID"
+ ]
}
],
"metadata": {
@@ -1414,7 +1421,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/04_measuring_quality_of_a_classifier.ipynb b/04_measuring_quality_of_a_classifier.ipynb
index ff5da77..1917213 100644
--- a/04_measuring_quality_of_a_classifier.ipynb
+++ b/04_measuring_quality_of_a_classifier.ipynb
@@ -808,6 +808,13 @@
"classifier.fit(features, labels)\n",
"predicted = classifier.predict(eval_features)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Copyright (C) 2019 ETH Zurich, SIS ID"
+ ]
}
],
"metadata": {
@@ -827,7 +834,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb b/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
index 8c0d904..a169eb0 100644
--- a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
+++ b/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
@@ -1358,7 +1358,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/06_classifiers_overview.ipynb b/06_classifiers_overview.ipynb
index c4965e4..697b49c 100644
--- a/06_classifiers_overview.ipynb
+++ b/06_classifiers_overview.ipynb
@@ -3127,7 +3127,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/07_regression.ipynb b/07_regression.ipynb
index d575c45..febedf6 100644
--- a/07_regression.ipynb
+++ b/07_regression.ipynb
@@ -1395,11 +1395,11 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": []
+ "source": [
+ "Copyright (C) 2019 ETH Zurich, SIS ID"
+ ]
}
],
"metadata": {
@@ -1419,7 +1419,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
diff --git a/08_neural_networks.ipynb b/08_neural_networks.ipynb
index 343143b..5702360 100644
--- a/08_neural_networks.ipynb
+++ b/08_neural_networks.ipynb
@@ -3564,18 +3564,11 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": []
+ "source": [
+ "Copyright (C) 2019 ETH Zurich, SIS ID"
+ ]
}
],
"metadata": {
@@ -3594,7 +3587,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.2"
+ "version": "3.7.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
@@ -3613,6 +3606,19 @@
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": true,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
}
},
"nbformat": 4,
--
GitLab