bigcode/jupyter-parsed · Datasets at Hugging Face

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[ [ [ "# Interpreting Tree Models", "_____no_output_____" ], [ "You'll need to install the `treeinterpreter` library. ", "_____no_output_____" ] ], [ [ "# !pip install treeinterpreter", "_____no_output_____" ], [ "import sklearn\nimport tensorf...

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Jupyter Notebook

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[ [ [ "# Bayesian Ridge Regression Part 2", "_____no_output_____" ], [ "### Multiple Features", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# ya...

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Jupyter Notebook

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[ [ [ "from korea_news_crawler.articlecrawler import ArticleCrawler\n\nCrawler = ArticleCrawler() \nCrawler.set_category(\"정치\", \"IT과학\", \"economy\") \nCrawler.set_date_range(2017, 1, 2017, 2) \nCrawler.start()", "{'start_year': 2017, 'start_month': 1, 'end_year': 2018, 'end_month': 4}\n정치 PID: 1...

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[ [ [ "##Application of Linear Algebra in Data Science ", "_____no_output_____" ], [ "Here is the Python code to calculate and plot the MSE", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n", "_____no_output_____" ], [ "x = li...

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Jupyter Notebook

hard_PCR (WSC)/gpt2/src/gpt2_classification.ipynb

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2020-09-18T09:47:17.000Z

2021-11-04T02:55:39.000Z

hard_PCR (WSC)/gpt2/src/gpt2_classification.ipynb

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[ [ [ "# Load WSC dataset\n\nimport xml.etree.ElementTree as etree\nimport json\nimport numpy as np\n\nimport logging\nimport numpy\nimport os\n\n\ndef softmax(x):\n return np.exp(x)/sum(np.exp(x))", "_____no_output_____" ], [ "tree = etree.parse('WSCollection.xml')\nroot = tree.getro...

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Chapman/Ch2-Problem_2-15.ipynb

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2021-11-08T05:45:58.000Z

Chapman/Ch2-Problem_2-15.ipynb

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Chapman/Ch2-Problem_2-15.ipynb

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[ [ [ "# Excercises Electric Machinery Fundamentals\n## Chapter 2", "_____no_output_____" ], [ "## Problem 2-15", "_____no_output_____" ] ], [ [ "%pylab notebook", "Populating the interactive namespace from numpy and matplotlib\n" ] ], [ [ ...

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Jupyter Notebook

Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb

geochri/Udacity-DAND

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Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb

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[ [ [ "## Analyze A/B Test Results\n\nYou may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page. Either way assure that your code passes the project [RUBRIC](https://review.udacity.com/#!/projects/37e27304-ad47-4eb0-a1ab-8c12f60...

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[ [ [ "# Eurocode 8 - Chapter 3 - seismic_action\n\nraw functions", "_____no_output_____" ] ], [ [ "from streng.codes.eurocodes.ec8.raw.ch3.seismic_action import spectra", "_____no_output_____" ] ], [ [ "## spectra", "_____no_output_____" ], [ ...

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Jupyter Notebook

sandpit/standalone_vkdv_convergence.ipynb

mrayson/iwaves

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sandpit/standalone_vkdv_convergence.ipynb

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tests/standalone_vkdv_convergence.ipynb

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[ [ [ "# Standalone Convergence Checker for the numerical vKdV solver\n\nCopied from Standalone Convergence Checker for the numerical KdV solver - just add bathy\n\nDoes not save or require any input data", "_____no_output_____" ] ], [ [ "import xarray as xr\nfrom iwaves.kdv.kdvimex ...

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[ [ [ "## Random Forest Classification\n", "_____no_output_____" ], [ "### Random Forest\n#### The fundamental idea behind a random forest is to combine many decision trees into a single model. Individually, predictions made by decision trees (or humans) may not be accurate, but combined...

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RetinaNet_Video_Object_Detection.ipynb

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RetinaNet_Video_Object_Detection.ipynb

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HMM TaggerPart of Speech Tagging - HMM.ipynb

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HMM TaggerPart of Speech Tagging - HMM.ipynb

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[ [ [ "# Project: Part of Speech Tagging with Hidden Markov Models \n---\n### Introduction\n\nPart of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural language phrases because it can be done...

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[ [ [ "# [Bucles `for`](https://docs.python.org/3/tutorial/controlflow.html#for-statements)", "_____no_output_____" ], [ "## Iterando listas", "_____no_output_____" ] ], [ [ "mi_lista = [1, 2, 3, 4, 'Python', 'es', 'piola']\nfor item in mi_lista:\n print(item)"...

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[ [ [ "# Pos-Tagging & Feature Extraction\nFollowing normalisation, we can now proceed to the process of pos-tagging and feature extraction. Let's start with pos-tagging.", "_____no_output_____" ], [ "## POS-tagging\nPart-of-speech tagging is one of the most important text analysis tasks...

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examples/Interactive/Basic/test_plan_notebook.ipynb

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examples/Interactive/Basic/test_plan_notebook.ipynb

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examples/Interactive/Basic/test_plan_notebook.ipynb

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[ [ [ "import pprint\n\nfrom testplan import Testplan\nfrom testplan.common.utils.logger import TEST_INFO, DEBUG\nfrom my_tests.mtest import make_multitest", "_____no_output_____" ], [ "# Initialize a plan with interactive mode flag set.\nplan = Testplan(name='MyPlan',\n i...

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Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb

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[ [ [ "from IPython.display import Image", "_____no_output_____" ] ], [ [ "# CNTK 201B: Hands On Labs Image Recognition", "_____no_output_____" ], [ "This hands-on lab shows how to implement image recognition task using [convolution network][] with CNTK v2 Python ...

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clairvoyant/Stocks

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clairvoyant/Stocks

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2019-09-09T13:02:39.000Z

2021-03-24T13:28:36.000Z

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[ [ [ "# Purpose: A basic object identification package for the lab to use", "_____no_output_____" ], [ "*Step 1: import packages*", "_____no_output_____" ] ], [ [ "import os.path as op\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n#...

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[ [ [ "## E4 Sensor Concatenation", "_____no_output_____" ], [ "This sensor concatenation file compiles all .csv files of subjects by sensor type. A column is added with the \"Subject_ID\" and arranges the data in order of ascending ID number. The output of this function is a csv file. "...

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[ [ [ "# Create Temporary Datasets for Analysis\n\nSimulate the proofcheck dataset until you get access to it", "_____no_output_____" ] ], [ [ "import pandas as pd\nmov_meta = pd.read_csv('movie_metadata.csv')", "_____no_output_____" ], [ "mov_meta.head()", ...

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[ [ [ "This notebook shows:\n* How to launch the [**StarGANv1**](https://arxiv.org/abs/1711.09020) model for inference\n* Example of results for both\n * attrubutes **detection**\n * new face **generation** with desired attributes\n\nHere I use [**PyTorch** implementation](https://github.com/yunjey/st...

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[ [ [ "import logging\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nfrom afsk.ax25 import UI\nfrom afsk.afsk import encode\nimport audiogen\nimport sys", "_____no_output_____" ], [ "packet = UI(\n\tdestination='APRS',\n\tsource='LU8AIE', \n\tinfo=':EM...

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[ [ [ "import pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nfrom sklearn.tree import export_text", "_____no_output_____" ] ], [ [ "This e...

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Jupyter Notebook

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[ [ [ "# Data Set-up and Cleaning", "_____no_output_____" ] ], [ [ "# Standard Library Imports\nimport pandas as pd\nimport numpy as np", "_____no_output_____" ] ], [ [ "For this section, I will be concatenating all the data sets into one large dataset.", ...

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[ [ [ "# Understanding the data\n\nIn this first part, we load the data and perform some initial exploration on it. The main goal of this step is to acquire some basic knowledge about the data, how the various features are distributed, if there are missing values in it and so on.", "_____no_output____...

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[ [ [ "# Finetuning of the pretrained Japanese BERT model\n\nFinetune the pretrained model to solve multi-class classification problems. \nThis notebook requires the following objects:\n- trained sentencepiece model (model and vocab files)\n- pretraiend Japanese BERT model\n\nDataset is livedoor ニュースコーパス i...

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[ [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom PIL import Image\nimport os\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow...

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[ [ [ "import json\nimport math\nimport numpy as np\nimport openrtdynamics2.lang as dy\nimport openrtdynamics2.targets as tg\n\nfrom vehicle_lib.vehicle_lib import *", "_____no_output_____" ], [ "# load track data\nwith open(\"track_data/simple_track.json\", \"r\") as read_file:\n tra...

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2020-09-23T19:26:32.000Z

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[ [ [ "# First steps with xmovie", "_____no_output_____" ] ], [ [ "import warnings\n\nimport matplotlib.pyplot as plt\nimport xarray as xr\nfrom shapely.errors import ShapelyDeprecationWarning\nfrom xmovie import Movie\n\nwarnings.filterwarnings(\n action='ignore',\n category=S...

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[ [ [ "<a href=\"https://colab.research.google.com/github/Sumi-Lee/testrepository/blob/main/math_symbol_prac.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "## 수학 기호 연습\n\n수식 기호들을 집...

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blab/flu-forecasting

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[ "MIT" ]

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2020-08-19T04:09:28.000Z

2021-07-05T02:32:04.000Z

analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb

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analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb

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[ [ [ "# Summarize titers and sequences by date\n\nCreate a single histogram on the same scale for number of titer measurements and number of genomic sequences per year to show the relative contribution of each data source.", "_____no_output_____" ] ], [ [ "import Bio\nimport Bio.Seq...

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examples/HSMfile_examples.ipynb

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[ [ [ "# HSMfile examples", "_____no_output_____" ], [ "The [hsmfile module](https://github.com/hadfieldnz/hsmfile) is modelled on my IDL mgh_san routines and provides user-customisable access to remote (slow-access) and local (fast-access) files.\n\nThis notebook exercises various aspe...

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[ [ [ "import re\nimport pprint\nimport json\nimport logging", "_____no_output_____" ], [ "# re.match(pattern, string, flags=0)\n\nprint(re.match('www', 'www.qwer.com').span()) # 在起始位置匹配\nprint(re.match('com', 'www.qwer.com')) # 不在起始位置匹配\n", "_____no_output_____" ], [ ...

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