code stringlengths 2.5k 6.36M | kind stringclasses 2
values | parsed_code stringlengths 0 404k | quality_prob float64 0 0.98 | learning_prob float64 0.03 1 |
|---|---|---|---|---|
# Cross validation of the potential fit
The functions required to run the fit in a Jupyter notebook are imported from the source code, along with matplotlib, and glob.
```
import popoff.fitting_output as fit_out
import popoff.cross_validation as cv
import matplotlib.pyplot as plt
import glob
```
## Setup of fitting p... | github_jupyter | import popoff.fitting_output as fit_out
import popoff.cross_validation as cv
import matplotlib.pyplot as plt
import glob
params = {}
params['core_shell'] = { 'Li': False, 'Ni': False, 'O': True }
params['charges'] = {'Li': +1.0,
'Ni': +3.0,
'O': {'core': -2.0,
... | 0.363534 | 0.983407 |
```
import pandas as pd
import utils
import seaborn as sns
import matplotlib.pyplot as plt
import random
import plotly.express as px
random.seed(9000)
plt.style.use("seaborn-ticks")
plt.rcParams["image.cmap"] = "Set1"
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set1.colors)
%matplotlib inline
```
In th... | github_jupyter | import pandas as pd
import utils
import seaborn as sns
import matplotlib.pyplot as plt
import random
import plotly.express as px
random.seed(9000)
plt.style.use("seaborn-ticks")
plt.rcParams["image.cmap"] = "Set1"
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set1.colors)
%matplotlib inline
n_samples = 1... | 0.427994 | 0.749145 |
International Morse Code defines a standard encoding where each letter is mapped to a series of dots and dashes, as follows: "a" maps to ".-", "b" maps to "-...", "c" maps to "-.-.", and so on.
For convenience, the full table for the 26 letters of the English alphabet is given below:
```javascript
[".-","-...","-.-."... | github_jupyter | [".-","-...","-.-.","-..",".","..-.","--.","....","..",".---","-.-",".-..","--","-.","---",".--.","--.-",".-.","...","-","..-","...-",".--","-..-","-.--","--.."]
Input: words = ["gin", "zen", "gig", "msg"]
"gin" -> "--...-."
"zen" -> "--...-."
"gig" -> "--...--."
"msg" -> "--...--."
class Solution(object):
def u... | 0.314366 | 0.834744 |
# Support Vector Regression with RobustScaler
This Code template is for regression analysis using Support Vector Regressor(SVR) based on the Support Vector Machine algorithm and feature rescaling technique RobustScaler in a pipeline.
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
... | github_jupyter | import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from sklearn.svm import SVR
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.metrics import r2_score... | 0.331877 | 0.989531 |
# Root cause analysis (RCA) of latencies in a microservice architecture
In this case study, we identify the root causes of "unexpected" observed latencies in cloud services that empower an
online shop. We focus on the process of placing an order, which involves different services to make sure that
the placed order is ... | github_jupyter | from IPython.display import Image
Image('microservice-architecture-dependencies.png', width=500)
import pandas as pd
normal_data = pd.read_csv("rca_microservice_architecture_latencies.csv")
normal_data.head()
axes = pd.plotting.scatter_matrix(normal_data, figsize=(10, 10), c='#ff0d57', alpha=0.2, hist_kwds={'color':... | 0.658088 | 0.985594 |
## Identifiability Test of Linear VAE on Synthetic Dataset
```
%load_ext autoreload
%autoreload 2
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split
import ltcl
import numpy as np
from ltcl.datasets.sim_dataset import SimulationDatasetTSTwoSample
from ltcl.modules.srnn i... | github_jupyter | %load_ext autoreload
%autoreload 2
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split
import ltcl
import numpy as np
from ltcl.datasets.sim_dataset import SimulationDatasetTSTwoSample
from ltcl.modules.srnn import SRNNSynthetic
from ltcl.tools.utils import load_yaml
impor... | 0.584983 | 0.722356 |
# 5.3 – Open Systems and Enthalpy
---
## 5.3.0 – Learning Objectives
By the end of this section you should be able to:
1. Understand the definition of enthalpy.
2. Explain how enthalpy differs from internal energy
3. Look at the steps to solving an enthalpy problem.
---
## 5.3.1 – Introduction
... | github_jupyter | # 5.3 – Open Systems and Enthalpy
---
## 5.3.0 – Learning Objectives
By the end of this section you should be able to:
1. Understand the definition of enthalpy.
2. Explain how enthalpy differs from internal energy
3. Look at the steps to solving an enthalpy problem.
---
## 5.3.1 – Introduction
... | 0.806358 | 0.983723 |
# Ray RLlib Multi-Armed Bandits - A Simple Bandit Example
© 2019-2021, Anyscale. All Rights Reserved
Let's explore a very simple contextual bandit example with three arms. We'll run trials using RLlib and [Tune](http://tune.io), Ray's hyperparameter tuning li... | github_jupyter | import gym
from gym.spaces import Discrete, Box
import numpy as np
import random
import time
import ray
class SimpleContextualBandit (gym.Env):
def __init__ (self, config=None):
self.action_space = Discrete(3) # 3 arms
self.observation_space = Box(low=-1., high=1., shape=(2, ), dtype=np.float64... | 0.714927 | 0.983863 |
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from scipy import signal
from scipy.fftpack import fft, ifft
import seaborn as sns
from obspy.io.segy.segy import _read_segy
from las import LASReader
from tabulate import tabulate
from scipy.optimize import curve_fit
import ... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from scipy import signal
from scipy.fftpack import fft, ifft
import seaborn as sns
from obspy.io.segy.segy import _read_segy
from las import LASReader
from tabulate import tabulate
from scipy.optimize import curve_fit
import pand... | 0.356895 | 0.471162 |
# Data Preprocessing
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
# Importing the Datasets
```
dataset=pd.read_csv("E:\\Edu\\Data Science and ML\\Machinelearningaz\\Datasets\\Part 2 - Regression\\Section 6 - Polynomial Regression\\Position_Salaries.csv")
dataset.head()
dataset.plot(... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset=pd.read_csv("E:\\Edu\\Data Science and ML\\Machinelearningaz\\Datasets\\Part 2 - Regression\\Section 6 - Polynomial Regression\\Position_Salaries.csv")
dataset.head()
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, share... | 0.700383 | 0.977414 |
<h1> 2. Creating a sampled dataset </h1>
This notebook illustrates:
<ol>
<li> Sampling a BigQuery dataset to create datasets for ML
<li> Preprocessing with Pandas
</ol>
```
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.e... | github_jupyter | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb -l ${REGION} gs://${B... | 0.252384 | 0.934634 |
```
import csv, time, requests, json, datetime
import hmac
import hashlib
import sys
!{sys.executable} -m pip install websocket-client
import websocket
import ssl
webSocket ='wss://stream.binance.com:9443/ws/xvgbtc@ticker'
ws = websocket.WebSocket(sslopt={"cert_reqs": ssl.CERT_NONE})
connect = ws.connect(webSocket)
... | github_jupyter | import csv, time, requests, json, datetime
import hmac
import hashlib
import sys
!{sys.executable} -m pip install websocket-client
import websocket
import ssl
webSocket ='wss://stream.binance.com:9443/ws/xvgbtc@ticker'
ws = websocket.WebSocket(sslopt={"cert_reqs": ssl.CERT_NONE})
connect = ws.connect(webSocket)
binT... | 0.158304 | 0.122052 |
# Name
Data processing by creating a cluster in Cloud Dataproc
# Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
# Summary
A Kubeflow Pipeline component to create a cluster in Cloud Dataproc.
# Details
## Intended use
Use this component at the start of a Kubeflow Pipeline to create a tempora... | github_jupyter | component_op(...).apply(gcp.use_gcp_secret('user-gcp-sa'))
```
* Grant the following types of access to the Kubeflow user service account:
* Read access to the Cloud Storage buckets which contains initialization action files.
* The role, `roles/dataproc.editor` on the project.
## Detailed descrip... | 0.845017 | 0.944382 |
# 函数
- 函数可以用来定义可重复代码，组织和简化
- 一般来说一个函数在实际开发中为一个小功能
- 一个类为一个大功能
- 同样函数的长度不要超过一屏
Python中的所有函数实际上都是有返回值(return None),
如果你没有设置return,那么Python将不显示None.
如果你设置return,那么将返回出return这个值.
## 定义一个函数
def function_name(list of parameters):
do something
- 以前使用的random 或者range 或者print.. 其实都是函数或者类
```
... | github_jupyter | def uuu():#r任何函数值都有默认值，
print('hello')
b='uuu'
print(b)
import random
com=random.randint(0,5)
while 1:
com = eval(input(''))
if com
def num(x1,x2,x3):
if x1>x2 and x1>x3
result = x1
elif x2>x3 and x2>x1:
result = x2
elif x3>x1 and x3>x2:
result = x3
num(x1=4,x2=2,x3=10)
d... | 0.239083 | 0.761073 |
# HW3
### Samir Patel
### DATA 515a
### 4/20/2017
For this homework, you are a data scientist working for Pronto (before the end of their contract with the City of Seattle). Your job is to assist in determining how to do end-of-day adjustments in the number of bikes at stations so that all stations will have enough b... | github_jupyter | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
df = pd.read_csv("2015_trip_data.csv")
start_weekday = [pd.to_datetime(x).dayofweek for x in df.starttime]
stop_weekday = [pd.to_datetime(x).dayofweek for x in df.stoptime]
df['startweekday'] = start_weekday # Creates a new colu... | 0.479016 | 0.962883 |
```
from source_files import SNOW_DEPTH_DIR, CASI_COLORS
from plot_helpers import *
from raster_compare.plots import PlotBase
from raster_compare.base import RasterFile
import math
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
aso_snow_depth = RasterFile(
SNOW_DEPTH_... | github_jupyter | from source_files import SNOW_DEPTH_DIR, CASI_COLORS
from plot_helpers import *
from raster_compare.plots import PlotBase
from raster_compare.base import RasterFile
import math
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
aso_snow_depth = RasterFile(
SNOW_DEPTH_DIR ... | 0.60054 | 0.597079 |
### ODPi Egeria Hands-On Lab
# Welcome to the Understanding Cohort Configuration Lab
## Introduction
ODPi Egeria is an open source project that provides open standards and implementation libraries to connect to... | github_jupyter | %run ../common/environment-check.ipynb
print (" ")
print ('Cohort(s) for cocoMDS1 are [%s]' % ', '.join(map(str, queryServerCohorts(cocoMDS1Name, cocoMDS1PlatformName, cocoMDS1PlatformURL))))
print ('Cohort(s) for cocoMDS2 are [%s]' % ', '.join(map(str, queryServerCohorts(cocoMDS2Name, cocoMDS2PlatformName, cocoMDS2... | 0.099563 | 0.941061 |
```
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import seaborn as sns
# Matplotlib
sns.set()
W = 8.27
plt.rcParams.update({
'figure.figsize': (W, W/(4/3)),
'figure.dpi': 150,
'font.size' : 11,
'axes.labelsize': 11,
... | github_jupyter | import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import seaborn as sns
# Matplotlib
sns.set()
W = 8.27
plt.rcParams.update({
'figure.figsize': (W, W/(4/3)),
'figure.dpi': 150,
'font.size' : 11,
'axes.labelsize': 11,
'leg... | 0.764188 | 0.559771 |
# DSCI 525 - Web and Cloud Computing
Milestone 2: Your team is planning to migrate to the cloud. AWS gave 400$ (100$ each) to your team to support this. As part of this initiative, your team needs to set up a server in the cloud, a collaborative environment for your team, and later move your data to the cloud. After t... | github_jupyter | import re
import os
import glob
import zipfile
import requests
from urllib.request import urlretrieve
import json
import pandas as pd
# Necessary metadata
article_id = 14226968 # this is the unique identifier of the article on figshare
url = f"https://api.figshare.com/v2/articles/{article_id}"
headers = {"Content-Typ... | 0.238373 | 0.89774 |
# Overview of pMuTT's Core Functionality
Originally written for Version 1.2.1
Last Updated for Version 1.2.13
## Topics Covered
- Using constants and converting units using the ``constants`` module
- Initializing ``StatMech`` objects by specifying all modes and by using ``presets`` dictionary
- Initializing empirica... | github_jupyter | from pmutt import constants as c
1.987
print(c.R('kJ/mol/K'))
print('Some constants')
print('R (J/mol/K) = {}'.format(c.R('J/mol/K')))
print("Avogadro's number = {}\n".format(c.Na))
print('Unit conversions')
print('5 kJ/mol --> {} eV/molecule'.format(c.convert_unit(num=5., initial='kJ/mol', final='eV/molecule')))
pr... | 0.505859 | 0.852137 |
# TabNet: Attentive Interpretable Tabular Learning
## Preparation
```
%%capture
!pip install pytorch-tabnet
!pip install imblearn
!pip install catboost
!pip install tab-transformer-pytorch
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score... | github_jupyter | %%capture
!pip install pytorch-tabnet
!pip install imblearn
!pip install catboost
!pip install tab-transformer-pytorch
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, accuracy_score, f1_score, confusion_matrix
from sklearn.preprocessing ... | 0.776453 | 0.889769 |
# Human numbers
```
from fastai.text import *
bs=64
```
## Data
```
path = untar_data(URLs.HUMAN_NUMBERS)
path.ls()
def readnums(d): return [', '.join(o.strip() for o in open(path/d).readlines())]
train_txt = readnums('train.txt'); train_txt[0][:80]
valid_txt = readnums('valid.txt'); valid_txt[0][-80:]
train = TextL... | github_jupyter | from fastai.text import *
bs=64
path = untar_data(URLs.HUMAN_NUMBERS)
path.ls()
def readnums(d): return [', '.join(o.strip() for o in open(path/d).readlines())]
train_txt = readnums('train.txt'); train_txt[0][:80]
valid_txt = readnums('valid.txt'); valid_txt[0][-80:]
train = TextList(train_txt, path=path)
valid = Text... | 0.813387 | 0.770206 |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Vectors/us_epa_ecoregions.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_b... | github_jupyter | # Installs geemap package
import subprocess
try:
import geemap
except ImportError:
print('geemap package not installed. Installing ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])
# Checks whether this notebook is running on Google Colab
try:
import google.colab
import gee... | 0.526343 | 0.961929 |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Concat... | github_jupyter | import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Concatenat... | 0.508788 | 0.410166 |
# Question formulation Notebook
Use of toy dataset and notebook dependencies.
### Notebook Set-up:
```
import re
import ast
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from os import path
from pyspark.sql import functions as F
from pyspark.... | github_jupyter | import re
import ast
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from os import path
from pyspark.sql import functions as F
from pyspark.sql.types import IntegerType
warnings.filterwarnings('ignore')
# warnings.resetwarnings()
PWD = !pwd
PWD =... | 0.497803 | 0.859664 |
```
# Import the dependencies
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from citipy import citipy
import requests
from config import weather_api_key
import sys
from datetime import datetime
# Create a set of random latitute and longitude combinations.
lats = np.random.uniform(low = -90.000... | github_jupyter | # Import the dependencies
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from citipy import citipy
import requests
from config import weather_api_key
import sys
from datetime import datetime
# Create a set of random latitute and longitude combinations.
lats = np.random.uniform(low = -90.000, hi... | 0.470007 | 0.495117 |
```
!pip install -U kaggle-cli
!kg download -u <username> -p <password> -c 'plant-seedlings-classification' -f 'test.zip'
!kg download -u <username> -p <password> -c 'plant-seedlings-classification' -f 'train.zip
!unzip test.zip -d data
!unzip train.zip -d data
import os
print(os.listdir('data/train/'))
import fnmatch... | github_jupyter | !pip install -U kaggle-cli
!kg download -u <username> -p <password> -c 'plant-seedlings-classification' -f 'test.zip'
!kg download -u <username> -p <password> -c 'plant-seedlings-classification' -f 'train.zip
!unzip test.zip -d data
!unzip train.zip -d data
import os
print(os.listdir('data/train/'))
import fnmatch
imp... | 0.387343 | 0.308255 |
## Descrição:
As a data scientist working for an investment firm, you will extract the revenue data for Tesla and GameStop and build a dashboard to compare the price of the stock vs the revenue.
## Tarefas:
- Question 1 - Extracting Tesla Stock Data Using yfinance - 2 Points
- Question 2 - Extracting Tesla ... | github_jupyter | # installing dependencies
!pip install yfinance pandas bs4
# importing modules
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
# needed to cast datetime values
from datetime import datetime
# making Ticker object
tesla_ticker = yf.Ticker("TSLA")
# creating a dataframe with hi... | 0.335133 | 0.941493 |
# Info
Name: Seyed Ali Mirferdos
Student ID: 99201465
# 0. Importing the necessary modules
```
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklear... | github_jupyter | import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.svm import SVC
!gdown --id 1oRmkmOMD5t_vA35N8IBmFcQM-WfE0_7x
df = pd.read_csv('bill_authentica... | 0.555918 | 0.935169 |
```
import youtube_dl
import re
import os
from tqdm import tqdm
import pandas as pd
import numpy as np
WAV_DIR = 'wav_files/'
genre_dict = {
'/m/064t9': 'Pop_music',
'/m/0glt670': 'Hip_hop_music',
'/m/0y4f8': 'Vocal',
'/m/06cqb': 'Reggae',
}
genre_set = set(... | github_jupyter | import youtube_dl
import re
import os
from tqdm import tqdm
import pandas as pd
import numpy as np
WAV_DIR = 'wav_files/'
genre_dict = {
'/m/064t9': 'Pop_music',
'/m/0glt670': 'Hip_hop_music',
'/m/0y4f8': 'Vocal',
'/m/06cqb': 'Reggae',
}
genre_set = set(genr... | 0.233969 | 0.280339 |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/JavaScripts/Image/Hillshade.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" ... | github_jupyter | # Installs geemap package
import subprocess
try:
import geemap
except ImportError:
print('geemap package not installed. Installing ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])
# Checks whether this notebook is running on Google Colab
try:
import google.colab
import gee... | 0.524882 | 0.946646 |
<img src='./img/egu_2020.png' alt='Logo EU Copernicus EUMETSAT' align='left' width='30%'></img><img src='./img/atmos_logos.png' alt='Logo EU Copernicus EUMETSAT' align='right' width='60%'></img></span>
<br>
<a href="./12_AC_SAF_GOME-2_L2_preprocess.ipynb"><< 12 - AC SAF GOME-2 Level 2 - preprocess </a><span style="fl... | github_jupyter | %matplotlib inline
import os
import xarray as xr
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from matplotlib.axes import Axes
from cartopy.mpl.geoaxes ... | 0.495361 | 0.964355 |
[](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/Sampling%20with%20conv%20SPNs.ipynb)
# **Image Sampling**: Sampling MNIST images
In this notebook, we'll set up an SPN to generate new MNIST images ... | github_jupyter | !pip install libspn-keras matplotlib
import libspn_keras as spnk
from tensorflow import keras
spnk.set_default_accumulator_initializer(
spnk.initializers.Dirichlet()
)
import numpy as np
import tensorflow_datasets as tfds
from libspn_keras.layers import NormalizeAxes
import tensorflow as tf
def take_first(a, b)... | 0.819388 | 0.990112 |
```
import pandas as pd
import numpy as np
import requests
import bs4 as bs
import urllib.request
```
## Extracting features of 2020 movies from Wikipedia
```
link = "https://en.wikipedia.org/wiki/List_of_American_films_of_2020"
source = urllib.request.urlopen(link).read()
soup = bs.BeautifulSoup(source,'lxml')
table... | github_jupyter | import pandas as pd
import numpy as np
import requests
import bs4 as bs
import urllib.request
link = "https://en.wikipedia.org/wiki/List_of_American_films_of_2020"
source = urllib.request.urlopen(link).read()
soup = bs.BeautifulSoup(source,'lxml')
tables = soup.find_all('table',class_='wikitable sortable')
len(tables)... | 0.157363 | 0.407098 |
## 2.6 Q学習で迷路を攻略
```
# 使用するパッケージの宣言
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# 初期位置での迷路の様子
# 図を描く大きさと、図の変数名を宣言
fig = plt.figure(figsize=(5, 5))
ax = plt.gca()
# 赤い壁を描く
plt.plot([1, 1], [0, 1], color='red', linewidth=2)
plt.plot([1, 2], [2, 2], color='red', linewidth=2)
plt.plot([2, 2], [... | github_jupyter | # 使用するパッケージの宣言
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# 初期位置での迷路の様子
# 図を描く大きさと、図の変数名を宣言
fig = plt.figure(figsize=(5, 5))
ax = plt.gca()
# 赤い壁を描く
plt.plot([1, 1], [0, 1], color='red', linewidth=2)
plt.plot([1, 2], [2, 2], color='red', linewidth=2)
plt.plot([2, 2], [2, 1], color='red', li... | 0.335351 | 0.889481 |
```
from datetime import datetime
import logging
logging.basicConfig(filename='train_initialization.log', filemode='w', format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
logging.info('SCRIPT INICIADO')
import os
from keras.preprocessing.image import ImageDataGenerator... | github_jupyter | from datetime import datetime
import logging
logging.basicConfig(filename='train_initialization.log', filemode='w', format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S', level=logging.INFO)
logging.info('SCRIPT INICIADO')
import os
from keras.preprocessing.image import ImageDataGenerator
fro... | 0.740268 | 0.141548 |
# VacationPy
----
#### Note
* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think throug... | github_jupyter | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import gmaps
import os
import json
# Import API key
from config import g_key
gmaps.configure(api_key=g_key)
city_data = "..\\WeatherPy\\weather_py_city_data.csv"
city_data_df = pd.read_csv(city_data)
city_d... | 0.349533 | 0.834474 |
```
#Dependencies
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import cv2
from scipy.ndimage.filters import gaussian_filter
#load image and smooth it
img = cv2.imread("simulated_rails.png", cv2.IMREAD_GRAYSCALE)
img = gaussian_filter(img, sigma=0.8)
print(img.shape)
plt.imshow(img, cmap =... | github_jupyter | #Dependencies
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import cv2
from scipy.ndimage.filters import gaussian_filter
#load image and smooth it
img = cv2.imread("simulated_rails.png", cv2.IMREAD_GRAYSCALE)
img = gaussian_filter(img, sigma=0.8)
print(img.shape)
plt.imshow(img, cmap ="gra... | 0.571169 | 0.539287 |
<a href="https://colab.research.google.com/github/angeruzzi/RegressionModel_ProdutividadeAgricola/blob/main/RegressionModel_ProdutividadeAgricola_Amendoin.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Predição Produtividade Agrícola: Produção de ... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import LassoLars
from sklearn.linear_model import BayesianRidge
from sklearn.neighbors import KNeighborsRegres... | 0.596903 | 0.954605 |
# Odds and Addends
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
```
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
impo... | github_jupyter | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py and create directories
import os
if not os.path.exists('utils.py'):
!wget https://github.com/AllenDowney/Thi... | 0.70028 | 0.983597 |
```
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
train_data = pd.read_csv('data/heart.csv')
tr... | github_jupyter | import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
train_data = pd.read_csv('data/heart.csv')
train_... | 0.705886 | 0.500854 |
# Training Neural Networks
The network we built in the previous part isn't so smart, it doesn't know anything about our handwritten digits. Neural networks with non-linear activations work like universal function approximators. There is some function that maps your input to the output. For example, images of handwritt... | github_jupyter | import torch
from torch import nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
... | 0.843057 | 0.992489 |
### Geek and the equation
Given a number N, find the value of below equation for the given number.
Input:
First line of input contains testcase T. For each testcase, there will be a single line containing a number N as input.
Output:
For each testcase, print the resultant of the equation.
Constraints:
1<=T<=100
1<=... | github_jupyter | t = int(input())
for i in range(t):
n = int(input())
res = 0
for m in range(1, n+1):
res += ((m+1)**2) - ((3*m)+1) + m
print(res)
# Input:
# 5
# 1
# 2
# 3
# 4
# 5
# Output:
# 1
# 2
# 10
# 11
# 12
def ternary (n):
if n == 0:
return '0'
nums = []
while n:
n, r = div... | 0.196017 | 0.970716 |
```
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="robotic-tract-334610-6605cbffd65c.json"
import numpy as np
import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
import random
random.seed(38)
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import pylab
# Jo... | github_jupyter | import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="robotic-tract-334610-6605cbffd65c.json"
import numpy as np
import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
import random
random.seed(38)
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import pylab
# Join E... | 0.367157 | 0.449634 |
# Artificial Intelligence Nanodegree
## Convolutional Neural Networks
---
In this notebook, we train an MLP to classify images from the MNIST database.
### 1. Load MNIST Database
```
from keras.datasets import mnist
# use Keras to import pre-shuffled MNIST database
(X_train, y_train), (X_test, y_test) = mnist.loa... | github_jupyter | from keras.datasets import mnist
# use Keras to import pre-shuffled MNIST database
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("The MNIST database has a training set of %d examples." % len(X_train))
print("The MNIST database has a test set of %d examples." % len(X_test))
import matplotlib.pyplot a... | 0.686055 | 0.980375 |
# Deep Q-Network (DQN)
---
In this notebook, you will implement a DQN agent with OpenAI Gym's LunarLander-v2 environment.
### 1. Import the Necessary Packages
```
import gym
import random
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
```
### 2. Insta... | github_jupyter | import gym
import random
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
env = gym.make('LunarLander-v2')
env.seed(0)
print('State shape: ', env.observation_space.shape)
print('Number of actions: ', env.action_space.n)
from dqn_agent import Agent
agent... | 0.608478 | 0.953923 |
# Módulo 4: APIs
## Spotify
<img src="https://developer.spotify.com/assets/branding-guidelines/logo@2x.png" width=400></img>
En este módulo utilizaremos APIs para obtener información sobre artistas, discos y tracks disponibles en Spotify. Pero primero.. ¿Qué es una **API**?<br>
Por sus siglas en inglés, una API es una... | github_jupyter | import requests
id_im = '6mdiAmATAx73kdxrNrnlao'
url_base = 'https://api.spotify.com/v1'
ep_artist = '/artists/{artist_id}'
url_base+ep_artist.format(artist_id=id_im)
r = requests.get(url_base+ep_artist.format(artist_id=id_im))
r.status_code
r.json() | 0.244724 | 0.924005 |
# Tokenizing notebook
First, the all important `import` statement.
```
from ideas import token_utils
```
## Getting information
We start with a very simple example, where we have a repeated token, `a`.
```
source = "a = a"
tokens = token_utils.tokenize(source)
for token in tokens:
print(token)
```
Notice how t... | github_jupyter | from ideas import token_utils
source = "a = a"
tokens = token_utils.tokenize(source)
for token in tokens:
print(token)
print(tokens[0] == tokens[2])
print(tokens[0] == tokens[2].string)
print(tokens[0] == 'a') # <-- Our normal choice
source = """
if True:
pass
"""
token_utils.print_tokens(source)
source ... | 0.338952 | 0.977989 |
Compare speed between native and cythonized math functions
```
%load_ext Cython
%%cython
from libc.math cimport log, sqrt
def log_c(float x):
return log(x)/2.302585092994046
def sqrt_c(float x):
return sqrt(x)
import os
from os.path import expanduser
import pandas as pd
import pandas.io.sql as pd_sql
import ma... | github_jupyter | %load_ext Cython
%%cython
from libc.math cimport log, sqrt
def log_c(float x):
return log(x)/2.302585092994046
def sqrt_c(float x):
return sqrt(x)
import os
from os.path import expanduser
import pandas as pd
import pandas.io.sql as pd_sql
import math
from functions.auth.connections import postgres_connection
c... | 0.367043 | 0.787605 |
# 横向联邦学习任务示例
这是一个使用Delta框架编写的横向联邦学习的任务示例。
数据是分布在多个节点上的[MNIST数据集](http://yann.lecun.com/exdb/mnist/)，每个节点上只有其中的一部分样本。任务是训练一个卷积神经网络的模型，进行手写数字的识别。
本示例可以直接在Deltaboard中执行并查看结果。<span style="color:#FF8F8F;font-weight:bold">在点击执行之前，需要修改一下个人的Deltaboard API的地址，具体请看下面第4节的说明。</span>
## 1. 引入需要的包
我们的计算逻辑是用torch写的。所以首先引入```nump... | github_jupyter | from typing import Dict, Iterable, List, Tuple, Any, Union
import numpy as np
import torch
from delta import DeltaNode
from delta.task import HorizontalTask
from delta.algorithm.horizontal import FedAvg
class LeNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Con... | 0.850701 | 0.976602 |
# Building Python Function-based Components
> Building your own lightweight pipelines components using the Pipelines SDK v2 and Python
A Kubeflow Pipelines component is a self-contained set of code that performs one step in your
ML workflow. A pipeline component is composed of:
* The component code, which implement... | github_jupyter | !pip install --upgrade kfp
import kfp
import kfp.dsl as dsl
from kfp.v2.dsl import (
component,
Input,
Output,
Dataset,
Metrics,
)
client = kfp.Client() # change arguments accordingly
@component
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
import k... | 0.830594 | 0.980949 |
```
import netCDF4
import math
import xarray as xr
import dask
import numpy as np
import time
import scipy
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib import transforms
from matplotlib.animation import PillowWriter
path_to_file = '/DFS-L/DATA/pritchard/gmooers/Workflow/MAPS/SPCAM/Sm... | github_jupyter | import netCDF4
import math
import xarray as xr
import dask
import numpy as np
import time
import scipy
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib import transforms
from matplotlib.animation import PillowWriter
path_to_file = '/DFS-L/DATA/pritchard/gmooers/Workflow/MAPS/SPCAM/Small_... | 0.196132 | 0.30795 |
<a href="https://colab.research.google.com/github/Ipsit1234/QML-HEP-Evaluation-Test-GSOC-2021/blob/main/QML_HEP_GSoC_2021_Task_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Task II: Quantum Generative Adversarial Network (QGAN) Part
You will e... | github_jupyter | !gdown --id 1r_MZB_crfpij6r3SxPDeU_3JD6t6AxAj -O events.npz
!pip install -q tensorflow==2.3.1
!pip install -q tensorflow-quantum
import tensorflow as tf
import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np
import seaborn as sns
from sklearn.metrics import roc_curve, auc
%matplotlib inline
imp... | 0.771499 | 0.989327 |
# Simple Stock Backtesting
https://www.investopedia.com/terms/b/backtesting.asp
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# fix_yahoo_finance is used to fetch data
import fix_yahoo_finance as yf
yf.pdr_override()
# input
symbol = 'M... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# fix_yahoo_finance is used to fetch data
import fix_yahoo_finance as yf
yf.pdr_override()
# input
symbol = 'MSFT'
start = '2016-01-01'
end = '2019-01-01'
# Read data
df = yf.download(symbol,sta... | 0.576542 | 0.869271 |
<center><font size="4"><span style="color:blue">Demonstration 1: general presentation with some quantities and statistics</span></font></center>
This is a general presentation of the 3W dataset, to the best of its authors' knowledge, the first realistic and public dataset with rare undesirable real events in oil wells... | github_jupyter | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.colors as mcolors
from matplotlib.patches import Patch
from pathlib import Path
from multiprocessing.dummy import Pool as ThreadPool
from collections import defaultdict
from natsort import natsorte... | 0.423696 | 0.936314 |
# Data
De [link](https://github.com/chihyaoma/regretful-agent/tree/master/tasks/R2R-pano)
Each JSON Lines entry contains a guide annotation for a path in the environment.
Data schema:
```python
{'split': str,
'instruction_id': int,
'annotator_id': int,
'language': str,
'path_id': int,
'scan': str,
'path': Seq... | github_jupyter | {'split': str,
'instruction_id': int,
'annotator_id': int,
'language': str,
'path_id': int,
'scan': str,
'path': Sequence[str],
'heading': float,
'instruction': str,
'timed_instruction': Sequence[Mapping[str, Union[str, float]]],
'edit_distance': float}
# Las features de todos los puntos del dataset pesan 3.... | 0.281011 | 0.890056 |
```
# default_exp utils
```
# Utils
> contains various util functions and classes
```
#hide
from nbdev.showdoc import *
#export
import os
import re
import pandas as pd
import numpy as np
from random import randrange
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.objects.conversion.l... | github_jupyter | # default_exp utils
#hide
from nbdev.showdoc import *
#export
import os
import re
import pandas as pd
import numpy as np
from random import randrange
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.objects.conversion.log import converter as log_converter
from fastai.torch_basics impor... | 0.155784 | 0.669853 |
# 选择
## 布尔类型、数值和表达式
- 注意：比较运算符的相等是两个等到，一个等到代表赋值
- 在Python中可以用整型0来代表False，其他数字来代表True
- 后面还会讲到 is 在判断语句中的用发
```
print(1>2)
yu=10000
a=eval(input('input money:'))
if a<=yu:
yu=yu-a
print("余额为：",yu)
else:
print("余额不足")
import os
a=eval(input('input money:'))
if a<=yu:
yu=yu-a
prin... | github_jupyter | print(1>2)
yu=10000
a=eval(input('input money:'))
if a<=yu:
yu=yu-a
print("余额为：",yu)
else:
print("余额不足")
import os
a=eval(input('input money:'))
if a<=yu:
yu=yu-a
print("余额为：",yu)
else:
print("余额不足")
'a'>'A'
'abc'>'acd'
bool(1.0)
bool(0.0)
import random
random.randint(0,10)
random.random()
r... | 0.029118 | 0.692102 |
# Основы Jupyter
## Simple python
Все данные храняться в оперативной памяти. Однажды созданную переменную можно использовать пока она не будет явно удалена.
```
a = 1
print(a)
```
Каждая ячейка аналогична выполнению кода в глобальной области видимости.
```
def mult_list(lst, n):
return lst * n
arr = mult_list... | github_jupyter | a = 1
print(a)
def mult_list(lst, n):
return lst * n
arr = mult_list([1, 2, 3], 3)
print(arr)
arr
print(mult_list(arr, 10))
mult_list(arr, 10)
mult_list(arr, 10);
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(font_scale=2, style='whitegrid', rc={'figure.figsize': (10, 6), '... | 0.295433 | 0.982339 |
```
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
import pymongo
from pymongo import MongoClient
# Connection to mongo
client = MongoClient('mongodb+srv://<user>:<password>@cluster0.l3pqt.mongodb.net/MSA?retryWrites=true&w=majority')
# Select database
db = client['MSA']
# see list of collections
cli... | github_jupyter | import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
import pymongo
from pymongo import MongoClient
# Connection to mongo
client = MongoClient('mongodb+srv://<user>:<password>@cluster0.l3pqt.mongodb.net/MSA?retryWrites=true&w=majority')
# Select database
db = client['MSA']
# see list of collections
client.... | 0.29931 | 0.262464 |
```
%%javascript
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
MathJax.Hub.Queue(
["resetEquationNumbers", MathJax.InputJax.TeX],
["PreProcess", MathJax.Hub],
["Reprocess", MathJax.Hub]
);
```
# Greek Letters
| Name | lower case | lower LaTeX | upper case | upper LaTeX |
|:---... | github_jupyter | %%javascript
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
MathJax.Hub.Queue(
["resetEquationNumbers", MathJax.InputJax.TeX],
["PreProcess", MathJax.Hub],
["Reprocess", MathJax.Hub]
);
$$
\frac{\partial z}{\partial x} = \lim_{\Delta x \rightarrow 0} \frac{f(x+\Delta x, y) - f(x,y)}... | 0.483648 | 0.988481 |
# Loan Credit Risk Prediction
When a financial institution examines a request for a loan, it is crucial to assess the risk of default to determine whether to grant it, and if so, what will be the interest rate.
This notebook takes advantage of the power of SQL Server and RevoScaleR (Microsoft R Server). The tables a... | github_jupyter | # WARNING.
# We recommend not using Internet Explorer as it does not support plotting, and may crash your session.
# INPUT DATA SETS: point to the correct path.
Loan <- "C:/Solutions/Loans/Data/Loan.txt"
Borrower <- "C:/Solutions/Loans/Data/Borrower.txt"
# Load packages.
library(RevoScaleR)
library("MicrosoftML")
lib... | 0.519278 | 0.911101 |
# mlp及深度学习常见技巧
我们将以mlp对为，基础模型，然后介绍一些深度学习常见技巧， 如： 权重初始化， 激活函数， 优化器， 批规范化， dropout，模型集成
```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
print(tf.__version__)
```
## 导入数据
```
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape([... | github_jupyter | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
print(tf.__version__)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape([x_train.shape[0], -1])
x_test = x_test.reshape([x_test.shape[0], -1])
print(x_train.shape, ' ', y_train.shape... | 0.94036 | 0.945399 |
# Prueba de concepto
## Emplazamiento local y post valoración global para ajustamiento con random forest
```
import sys
print(sys.version) #Python version
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metri... | github_jupyter | import sys
print(sys.version) #Python version
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score, train_test_split, RepeatedKFo... | 0.233444 | 0.778291 |
<div>
<img src="..\Week 01\img\R_logo.svg" width="100"/>
</div>
<div style="line-height:600%;">
<font color=#1363E1 face="Britannic" size=10>
<div align=center>Variables</div>
</font>
</div>
<div style="line-height:300%;">
<font color=#9A0909 face="Britannic" size=6>
... | github_jupyter | # valid: Has letters, numbers, dot and underscore
var_name.2 = 1
print(var_name.2)
# Invalid: Has the character '%'. Only dot(.) and underscore allowed
var_name% = 1
print(var_name%)
# Invalid: Starts with a number
2var_name = 1
print(2var_name)
# Valid: Can start with a dot(.) but the dot(.)should not be followed by a... | 0.344664 | 0.874077 |
# Mass on a spring
The computation itself comes from from Chabay and Sherwood's exercises VP07 and VP09 http://www.compadre.org/portal/items/detail.cfm?ID=5692#tabs. They have great learning activities in the assignments as well.
# Model the spring force
Let's call the current length of the spring $L$ and relaxed len... | github_jupyter | ## constants and data
g = 9.8
L0 = 0.26
ks = 1.8
dt = .02
## objects (origin is at ceiling)
ceiling = box(pos=vector(0,0,0), length=0.2, height=0.01, width=0.2)
ball = sphere(radius=0.025,color=color.orange, make_trail = True)
spring = helix(pos=ceiling.pos, color=color.cyan, thickness=.003, coils=40, radius=0.010)
#... | 0.525612 | 0.984246 |
```
import numpy as np
import pandas as pd
import scipy as sp
import sklearn as sl
import seaborn as sns; sns.set()
import matplotlib as mpl
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
%matplotlib inline
```
# ... | github_jupyter | import numpy as np
import pandas as pd
import scipy as sp
import sklearn as sl
import seaborn as sns; sns.set()
import matplotlib as mpl
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
%matplotlib inline
df = pd.re... | 0.434701 | 0.879871 |
<a href="https://colab.research.google.com/github/thehimalayanleo/Private-Machine-Learning/blob/master/Fed_Averaging_Pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import torch
import torch.optim as optim
import torch.nn as nn
import t... | github_jupyter | import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import copy
from torch.utils.data import DataLoader, Dataset
class Net(nn.Module):
def __init__(se... | 0.764628 | 0.864081 |
# Training a model on the UD Corpus
This notebook looks at how to train a model using the Universal Dependencies Corpus.
We will learn how to (1) download the UD Corpus, (2) train a tokenizer and a tagger model on a specific language and then (3) pack it all up in a zip model that we'll use locally.
This notebook is... | github_jupyter | ! cd /work; curl --remote-name-all https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-2837/ud-treebanks-v2.2.tgz
! tar -xzf /work/ud-treebanks-v2.2.tgz -C /work
! ls -lh /work/ud-treebanks-v2.2/UD_English-ParTUT
! mkdir /work/my_model-1.0
python3 /work/NLP-Cube/cube/main.py --train=tokenizer --tra... | 0.471223 | 0.942242 |
# Chapter 2 Housing Example
## Data
```
import sys
sys.path.append('../src/')
from fetch_housing_data import fetch_housing_data,load_housing_data
from CombinedAttrAdders import CombinedAttributesAdder
fetch_housing_data()
housing = load_housing_data()
housing.head()
```
## Take a look
```
housing.info()
housing.oc... | github_jupyter | import sys
sys.path.append('../src/')
from fetch_housing_data import fetch_housing_data,load_housing_data
from CombinedAttrAdders import CombinedAttributesAdder
fetch_housing_data()
housing = load_housing_data()
housing.head()
housing.info()
housing.ocean_proximity.value_counts()
housing.describe()
%matplotlib inlin... | 0.45641 | 0.928926 |
```
import keras
keras.__version__
```
# Text generation with LSTM
This notebook contains the code samples found in Chapter 8, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particu... | github_jupyter | import keras
keras.__version__
import keras
import numpy as np
path = keras.utils.get_file(
'nietzsche.txt',
origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
text = open(path).read().lower()
print('Corpus length:', len(text))
# Length of extracted character sequences
maxlen = 60
# We sample a ... | 0.599602 | 0.970604 |
**7장 – 앙상블 학습과 랜덤 포레스트**
_이 노트북은 7장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />구... | github_jupyter | # 파이썬 ≥3.5 필수
import sys
assert sys.version_info >= (3, 5)
# 사이킷런 ≥0.20 필수
import sklearn
assert sklearn.__version__ >= "0.20"
# 공통 모듈 임포트
import numpy as np
import os
# 노트북 실행 결과를 동일하게 유지하기 위해
np.random.seed(42)
# 깔끔한 그래프 출력을 위해
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('ax... | 0.382257 | 0.978426 |
# Data Visualization
## Specifications
This workflow should produce three publication-quality visualizations:
1. Daily-mean radiative fluxes at top of atmosphere and surface from ERA5 for 22 September 2020.
2. Intrinsic atmospheric radiative properties (reflectivity, absorptivity, and transmissivity) based on the St... | github_jupyter | from utils import check_environment
check_environment("visualize")
import logging
import os
import cartopy.crs as ccrs
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from google.cloud import storage
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib import colors
import n... | 0.654122 | 0.912864 |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
```
# ResNet34 inference
```
import albumentations
import gc
import numpy as np
import pandas as pd
import pretrainedmodels
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from pathlib import Path
from torch.utils.dat... | github_jupyter | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import albumentations
import gc
import numpy as np
import pandas as pd
import pretrainedmodels
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from pathlib import Path
from torch.utils.data import DataLoader
from tqdm impo... | 0.762247 | 0.701585 |
```
import pandas as pd
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
df = pd.read_csv("COVIDiSTRESS June 17.csv",encoding='latin-1')
df.head()
df=df.drop(columns=['Dem_Expat','Country',
'Unnamed: 0',
'neu',
'ext',
'ope',
'agr',
'con',
'Duration..in.seconds.',
'UserLanguage',
'Scale_PSS10_UCLA... | github_jupyter | import pandas as pd
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
df = pd.read_csv("COVIDiSTRESS June 17.csv",encoding='latin-1')
df.head()
df=df.drop(columns=['Dem_Expat','Country',
'Unnamed: 0',
'neu',
'ext',
'ope',
'agr',
'con',
'Duration..in.seconds.',
'UserLanguage',
'Scale_PSS10_UCLA_1',... | 0.48121 | 0.136695 |
<a href="https://colab.research.google.com/github/davidbro-in/natural-language-processing/blob/main/3_custom_embedding_using_gensim.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<a href="https://www.inove.com.ar"><img src="https://github.com/herna... | github_jupyter | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import multiprocessing
from gensim.models import Word2Vec
!wget https://www.gutenberg.org/cache/epub/31193/pg31193.txt
# Armar el dataset utilizando salto de línea para separar las oraciones/docs
df = pd.read_csv('/content/pg31193.txt', sep='/n... | 0.638497 | 0.917266 |
# Applications
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as la
```
## Polynomial Interpolation
[Polynomial interpolation](https://en.wikipedia.org/wiki/Polynomial_interpolation) finds the unique polynomial of degree $n$ which passes through $n+1$ points in the $xy$-plane. For example... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as la
x = np.array([-1,0,1])
X = np.column_stack([[1,1,1],x,x**2])
print(X)
y = np.array([1,0,1]).reshape(3,1)
print(y)
a = la.solve(X,y)
print(a)
x = np.array([0,3,8])
X = np.column_stack([[1,1,1],x,x**2])
print(X)
y = np.array([6,1,2]).reshap... | 0.326379 | 0.988503 |
Human Strategy
----------------
Human strategy is a strategy which asks the user to input a move rather than deriving its own action.
The history of the match is also shown in the terminal, thus you will be able to see the history of the game.
We are now going to open an editor. There we are going to create a script ... | github_jupyter | import axelrod as axl
import random
strategies = [s() for s in axl.strategies]
opponent = random.choice(axl.strategies)
me = axl.Human(name='Nikoleta')
players = [opponent(), me]
# play the match and return winner and final score
match = axl.Match(players, turns=3)
match.play()
print('You have competed against {}, t... | 0.173288 | 0.828384 |
# Max-Voting
### Getting Ready
```
import os
import pandas as pd
os.chdir(".../Chapter 2")
os.getcwd()
```
#### Download the dataset Cryotherapy.csv from the github location and copy the same to your working directory. Let's read the dataset.
```
cryotherapy_data = pd.read_csv("Cryotherapy.csv")
```
#### Let's tak... | github_jupyter | import os
import pandas as pd
os.chdir(".../Chapter 2")
os.getcwd()
cryotherapy_data = pd.read_csv("Cryotherapy.csv")
cryotherapy_data.head(5)
# Import required libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensem... | 0.646125 | 0.868213 |
## Random Forest importance
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.feature_selection import SelectFromModel
```
## Read Data
```
data = pd.r... | github_jupyter | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.feature_selection import SelectFromModel
data = pd.read_csv('../DoHBrwTest.csv')
data.shape
data.head()
X_... | 0.659076 | 0.894144 |
# Data preparation
```
import sqlite3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import Cdf
import Pmf
# suppress unnecessary warnings
import warnings
warnings.filterwarnings("ignore", module="numpy")
# define global plot parameters
params = {'axes.labe... | github_jupyter | import sqlite3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import Cdf
import Pmf
# suppress unnecessary warnings
import warnings
warnings.filterwarnings("ignore", module="numpy")
# define global plot parameters
params = {'axes.labelsize' : 12, 'axes.title... | 0.537041 | 0.832713 |
<center>
<h1>DatatableTon</h1>
💯 datatable exercises
<br>
<br>
<a href='https://github.com/vopani/datatableton/blob/master/LICENSE'>
<img src='https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache'>
</a>
<a href='https://github.com/vopani/datatableton'>
<img... | github_jupyter | !python3 -m pip install -U pip
!python3 -m pip install -U datatable
import datatable as dt
dt.__version__
data = dt.Frame()
data = dt.Frame(v1=range(10), v2=['Y', 'O', 'U', 'C', 'A', 'N', 'D', 'O', 'I', 'T'])
data
data.head(5)
data.tail(3)
data.nrows
data.ncols
data.shape
data.names | 0.370112 | 0.956145 |
Classical probability distributions can be written as a stochastic vector, which can be transformed to another stochastic vector by applying a stochastic matrix. In other words, the evolution of stochastic vectors can be described by a stochastic matrix.
Quantum states also evolve and their evolution is described by u... | github_jupyter | import numpy as np
X = np.array([[0, 1], [1, 0]])
print("XX^dagger")
print(X @ X.T.conj())
print("X^daggerX")
print(X.T.conj() @ X)
print("The norm of the state |0> before applying X")
zero_ket = np.array([[1], [0]])
print(np.linalg.norm(zero_ket))
print("The norm of the state after applying X")
print(np.linalg.norm(X... | 0.510252 | 0.991828 |
# Think Bayes
Second Edition
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
```
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = ... | github_jupyter | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
import os
if not os.path.exists('utils.py'):
!wget https://github.com/AllenDowney/ThinkBayes2/raw/master/cod... | 0.5083 | 0.972623 |
# Chainer MNIST Model Deployment
* Wrap a Chainer MNIST python model for use as a prediction microservice in seldon-core
* Run locally on Docker to test
* Deploy on seldon-core running on minikube
## Dependencies
* [Helm](https://github.com/kubernetes/helm)
* [Minikube](https://github.com/kubernetes/miniku... | github_jupyter | pip install seldon-core
pip install chainer==6.2.0
#!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
# Network definition
class MLP(chainer.Chain):
def __init__(self, ... | 0.57678 | 0.892234 |
# Data Visualization - Plotting Data
- its import to explore and understand your data in Data Science
- typically various statistical graphics are plotted to quickly visualize and understand data
- various libraries work together with Pandas DataFrame and Series datastructrue to quickly plot a data table
- `matplotlib... | github_jupyter | DataFrame.plot(*args, **kwargs)
import pandas as pd
import matplotlib.pyplot as plt
# online raw data URLs
no2_url = 'https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/air_quality_no2.csv'
pm2_url = 'https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/air_quality_pm25_long.csv'
air_qu... | 0.619932 | 0.975762 |
```
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
%matplotlib inline
from glob import glob
all_q = {}
x_dirs = glob('yz/*/')
x_dirs[0].split('/')
'1qtable'.split('1')
for x_dir in x_dirs:
chain_length = x_dir.split('/')[1]
qtables = glob(f'{x_di... | github_jupyter | import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
%matplotlib inline
from glob import glob
all_q = {}
x_dirs = glob('yz/*/')
x_dirs[0].split('/')
'1qtable'.split('1')
for x_dir in x_dirs:
chain_length = x_dir.split('/')[1]
qtables = glob(f'{x_dir}{c... | 0.286469 | 0.281937 |
```
from theano.sandbox import cuda
cuda.use('gpu2')
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
?? BatchNormalization
```
## Setup
```
batch_size=64
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
(X_trai... | github_jupyter | from theano.sandbox import cuda
cuda.use('gpu2')
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
?? BatchNormalization
batch_size=64
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
(X_train.shape, y_train.shape... | 0.715921 | 0.837819 |
<img src="Polygons.png" width="320"/>
# Polygons and polylines
You can draw polygons or polylines on canvases by providing a sequence of points.
The polygons can have transparent colors and they may be filled or not (stroked).
A point can be an [x,y] pair or any but the first point can be a triple of [x,y] pairs rep... | github_jupyter | from jp_doodle import dual_canvas
from IPython.display import display
# In this demonstration we do most of the work in Javascript.
demo = dual_canvas.DualCanvasWidget(width=320, height=220)
display(demo)
demo.js_init("""
// Last entry in points list gives Bezier control points [[2,2], [1,0], [0,0]]
var points = [[5... | 0.716814 | 0.947575 |
# Path and shape regimes of rising bubbles
## Outline
1. [Starting point](#starting_point)
2. [Data visualization](#data_visualization)
3. [Manual binary classification - creating a functional relationship](#manuel_classification)
4. [Using gradient descent to find the parameters/weights](#gradient_descent)
5. [Using... | github_jupyter | # load and process .csv files
import pandas as pd
# python arrays
import numpy as np
# plotting
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
# machine learning
import sklearn
from sklearn import preprocessing
import torch
from torch import nn, optim
import torch.... | 0.561455 | 0.980525 |
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import json
import sys
import os
import scipy
import scipy.io
from scipy import stats
path_root = os.environ.get('DECIDENET_PATH')
path_code = os.path.join(path_root, 'code')
if path_code not in sys.path:
sys.path.append(path_code)
from dn... | github_jupyter | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import json
import sys
import os
import scipy
import scipy.io
from scipy import stats
path_root = os.environ.get('DECIDENET_PATH')
path_code = os.path.join(path_root, 'code')
if path_code not in sys.path:
sys.path.append(path_code)
from dn_uti... | 0.541409 | 0.772101 |
```
import numpy as np
import pandas as pd
import plotly.express as px
from scipy import stats
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm as anova
import itertools
from sklearn import linear_model
from numpy import ones,vstack
from numpy.linalg import lstsq
df=pd.read_csv('../d... | github_jupyter | import numpy as np
import pandas as pd
import plotly.express as px
from scipy import stats
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm as anova
import itertools
from sklearn import linear_model
from numpy import ones,vstack
from numpy.linalg import lstsq
df=pd.read_csv('../data/... | 0.37502 | 0.312344 |
# Compare slit profile with reference profile for [O III]
Repeat of previous workbook but for a different line
I first work through all the steps individually, looking at graphs of the intermediate results. This was used while iterating on the algorithm, by swapping out the value of `db` below for different slits th... | github_jupyter | from pathlib import Path
import yaml
import numpy as np
from numpy.polynomial import Chebyshev
from astropy.io import fits
from astropy.wcs import WCS
import astropy.units as u
from matplotlib import pyplot as plt
import seaborn as sns
import mes_longslit as mes
dpath = Path.cwd().parent / "data"
pvpath = dpath / "pve... | 0.507324 | 0.903932 |
## 1. The World Bank's international debt data
<p>It's not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. <a href="https://www.worldba... | github_jupyter | %%sql
postgresql:///international_debt
SELECT *
FROM international_debt
LIMIT 10;
%%sql
SELECT
COUNT(DISTINCT (country_name)) AS total_distinct_countries
FROM international_debt;
%%sql
SELECT DISTINCT(indicator_code) AS distinct_debt_indicators
FROM international_debt
ORDER BY distinct_debt_indicators;
%%sql
SE... | 0.262464 | 0.989682 |
<a href="https://colab.research.google.com/github/ibaiGorordo/Deeplab-ADE20K-Inference/blob/master/DeepLab_ADE20K_inference_Demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Overview
This colab demonstrates the steps to use the DeepLab model t... | github_jupyter | import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
%tensorflow_version 1.x
import tensorflow as tf
class DeepLabModel(object):
"""Class to load deeplab model and r... | 0.7324 | 0.985977 |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pymc3 as pm
import numpy.random as npr
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
```
# Introduction
Let's say there are three bacteria species that characterize the gut, and we... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pymc3 as pm
import numpy.random as npr
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
def proportion(arr):
arr = np.asarray(arr)
return arr / arr.sum()
healthy_proportions = pro... | 0.486332 | 0.956796 |
# Exampville Mode Choice
Discrete choice modeling is at the heart of many transportion planning models.
In this example, we will examine the development of a mode choice model for
Exampville, an entirely fictional town built for the express purpose of
demostrating the use of discrete choice modeling tools for transp... | github_jupyter | import larch, numpy, pandas, os
import larch.exampville
skims = larch.OMX( larch.exampville.files.skims, mode='r' )
skims
hh = pandas.read_csv( larch.exampville.files.hh )
pp = pandas.read_csv( larch.exampville.files.person )
tour = pandas.read_csv( larch.exampville.files.tour )
hh.info()
pp.info()
tour.info()
tou... | 0.338186 | 0.989662 |
### Analyze_rotated_stable_points - evaluate bias in rotated DEMs using selected unchanged points
These points were picked on hopefully stable points in mostly flat places: docks, lawns, bare spots in middens. Also, the yurt roofs. Typically, 3 to 5 points were picked on most features.
```
import pandas as pd
import ... | github_jupyter | import pandas as pd
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inline
# define some functions
def pcoord(x, y):
"""
Convert x, y to polar coordinates r, az (geographic convention)
r,az = pcoord(x, y)
"""
r = np.sqrt( x**2 + y**2 )
az=np.degrees( np.arcta... | 0.499512 | 0.894698 |
# Calculating NDVI: Part 2
This exercise follows on from the previous section. In the [previous part of this exercise](../session_4/03_calculate_ndvi_part_2.ipynb), you constructed a notebook to resample a year's worth of Sentinel-2 data into quarterly time steps.
In this section, you will conitnue from where you end... | github_jupyter | measurements = ['red', 'green', 'blue', 'nir']
```
If you completed the above step, your `load_ard` cell should look like:
sentinel_2_ds = load_ard(
dc=dc,
products=["s2_l2a"],
x=x, y=y,
time=("2019-01", "2019-12"),
output_crs="EPSG:6933",
measurements=['red... | 0.94672 | 0.991381 |
[](https://www.pythonista.io)
# Expresiones con operadores en Python.
Los operadores son signos o palabras reservadas que el intérprete de Python identifica dentro de sus sintaxis para realizar una acción (operación) específica.
```
<objeto 1> <operador> <objeto 2>
```
```
<... | github_jupyter | <objeto 1> <operador> <objeto 2>
<objeto 1> <operador 1> <objeto 2> <operador 2> .... <operador n-1> <objeto n>
1 + 1
15 * 4 + 1 / 3 ** 5
3 + 2
3 - 2
3 * 2
3 ** 2
3 ** 0.5
3 / 2
3 // 2
3 % 2
12 * 5 + 2 / 4 ** 2
(12 * 5) + (2 / (4 ** 2))
(12 * 5) + (2 / 4) ** 2
(12 * (5 + 2) / 3) ** 2
>>> 3 / 4
0
>>> 10 / ... | 0.525612 | 0.97506 |
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