| @import url("https://unpkg.com/primer/build/build.css");.highlight{width:100%;overflow:auto;background:#ffffff}.highlight .c{color:#999988;font-style:italic}.highlight .err{color:#a61717;background-color:#e3d2d2}.highlight .k{font-weight:bold}.highlight .o{font-weight:bold}.highlight .cm{color:#999988;font-style:italic}.highlight .cp{color:#999999;font-weight:bold}.highlight .c1{color:#999988;font-style:italic}.highlight .cs{color:#999999;font-weight:bold;font-style:italic}.highlight .gd{color:#000000;background-color:#fdd}.highlight .gd .x{color:#000000;background-color:#faa}.highlight .ge{font-style:italic}.highlight .gr{color:#a00}.highlight .gh{color:#999}.highlight .gi{color:#000000;background-color:#dfd}.highlight .gi .x{color:#000000;background-color:#afa}.highlight .go{color:#888}.highlight .gp{color:#555}.highlight .gs{font-weight:bold}.highlight .gu{color:#aaa}.highlight .gt{color:#a00}.highlight .kc{font-weight:bold}.highlight .kd{font-weight:bold}.highlight .kp{font-weight:bold}.highlight .kr{font |
| { | |
| "$id": "https://github.com/microsoft/terminal/blob/master/doc/cascadia/profiles.schema.json", | |
| "$schema": "http://json-schema.org/draft-07/schema#", | |
| "title": "Microsoft's Windows Terminal Settings Profile Schema", | |
| "definitions": { | |
| "KeyChordSegment": { | |
| "pattern": "^(?<modifier>(ctrl|alt|shift)(?:\\+(ctrl|alt|shift)(?<!\\2))?(?:\\+(ctrl|alt|shift)(?<!\\2|\\3))?\\+)?(?<key>[^\\s+]|backspace|tab|enter|esc|escape|space|pgup|pageup|pgdn|pagedown|end|home|left|up|right|down|insert|delete|(?<!shift.+)(?:numpad_?[0-9]|numpad_(?:period|decimal))|numpad_(?:multiply|plus|add|minus|subtract|divide)|f[1-9]|f1[0-9]|f2[0-4]|plus)$", | |
| "type": "string", | |
| "description": "The string should fit the format \"[ctrl+][alt+][shift+]<keyName>\", where each modifier is optional, separated by + symbols, and keyName is either one of the names listed in the table below, or any single key character. The string should be written in full lowercase.\nbackspace\tBACKSPACE key\ntab\tTAB key\nenter\tENTER key\nesc, e |
| # Read all the weights/outputs | |
| # Specify weights for all models (such that they equal to 1) | |
| # Finally, tweak them to create a perfect blend | |
| ### Ref | |
| ### https://www.kaggle.com/muhakabartay/simple-public-blender-0-936 |
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | target | |
|---|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa | |
| 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa | |
| 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa | |
| 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa | |
| 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa | |
| 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa | |
| 4.6 | 3.4 | 1.4 | 0.3 | Iris-setosa | |
| 5.0 | 3.4 | 1.5 | 0.2 | Iris-setosa | |
| 4.4 | 2.9 | 1.4 | 0.2 | Iris-setosa |
| 0.78051 | -0.063669 | 1 | |
|---|---|---|---|
| 0.28774 | 0.29139 | 1 | |
| 0.40714 | 0.17878 | 1 | |
| 0.2923 | 0.4217 | 1 | |
| 0.50922 | 0.35256 | 1 | |
| 0.27785 | 0.10802 | 1 | |
| 0.27527 | 0.33223 | 1 | |
| 0.43999 | 0.31245 | 1 | |
| 0.33557 | 0.42984 | 1 | |
| 0.23448 | 0.24986 | 1 |
| -----BEGIN RSA PRIVATE KEY----- | |
| MIIEowIBAAKCAQEA0HOsVMA7RPjHPbB2hWQ+KZscScyeMVUSXy9e+m7KJcXgJfG/A4w0iQr2rUUE | |
| Hp0uy/RUky4TPN/uLuIGQZlooiqiQ3M4nd3KckS7vK7aiAt5gUL3y2jCSkI2WJyfN8jetLBwOV3j | |
| 5jlyOSN74wGDxtKhQ8iMRku5J9rgxrtIrHOW8xlsRPZVuMjlX446TkIjQpojb82bK2jY6id7tiS3 | |
| ZHsBZhz70b9URrp/GhuSIsufpbPS6U+zfeiDUmnVkrkxu3eR5xbApM0CS1IlIvKplGkNc4KzaE2m | |
| 82f7HL6KrRLJAXSXXGZ/BTFtiDZz3JnWJdaxCvjxjnFHY2CwRM+i1wIDAQABAoIBAQDNhO6o1Krb | |
| EskmGmCQncfoL4URgguKKg0mCceD9E2cNl8MKjAhKE/3ufaB2ST5phGohuoH/op7H3FvieuPK9iM | |
| 4qn/BJASncTnKrtKib7uGRogBs3XIB9HJcb9UNZuIr7ouXi8JsT2jTb//Os9YpXxJmRXN1iOSkRw | |
| dg3eAnLhioF1v4rET2JrWA87DY05gsymeS/uAjerptWS4KWLiI1pgxAE3nYowYbUFxv7jcs4AttY | |
| P74SraFrGxGs18n+Wyo0B8takjlwRDuGR+psKdLAq7CoHJiGz1OlKKppAPTzlDsY5pdGdAU6BwC7 |
| !pip install -q "fastai==0.7.0" Pillow==4.1.1 torchtext==0.2.3 | |
| !apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python | |
| from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag | |
| platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag()) | |
| # !apt update -q | |
| !apt install -y libsm6 libxext6 | |
| from os import path | |
| accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu' |
| # !!! RUN THIS CELL ONLY ON GOOGLE COLAB !!! | |
| ! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py | |
| import setup_google_colab | |
| # run tensorboard in background | |
| import os | |
| os.system("tensorboard --logdir=./logs --host 0.0.0.0 --port 6006 &") | |
| # expose port and show the link | |
| setup_google_colab.expose_port_on_colab(6006) |
| """ | |
| A demo of creating a new database via SQL Alchemy. | |
| Under MIT License from sprin (https://gist.github.com/sprin/5846464/) | |
| This module takes the form of a nosetest with three steps: | |
| - Set up the new database. | |
| - Create a table in the new database. | |
| - Teardown the new database. | |
| """ |
By Emily Gill and Amber Rivera
The Pipeline constructor from sklearn allows you to chain transformers and estimators together into a sequence that functions as one cohesive unit. For example, if your model involves feature selection, standardization, and then regression, those three steps, each as it's own class, could be encapsulated together via Pipeline.