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 |
|---|---|---|---|---|
```
import pandas as pd
```
### Generate a brief statistic summary for corresponding data
* Sort daily gain/loss for January of 2018 and store the result back to a .csv file
```
FILE = r"C:\Users\pavan\Desktop\SP500 (1).csv"
data = pd.read_csv(FILE)
data.shape
data.columns
data.head()
data['Gain'] = data.Close - dat... | github_jupyter | import pandas as pd
FILE = r"C:\Users\pavan\Desktop\SP500 (1).csv"
data = pd.read_csv(FILE)
data.shape
data.columns
data.head()
data['Gain'] = data.Close - data.Open
data.head()
data['Date'] = pd.to_datetime(data.Date)
data.loc[data.Date.dt.year==2018,].sort_values(['Gain'],ascending=False).to_csv("./2018_Gain_Loss.cs... | 0.113801 | 0.90291 |
```
import keras
keras.__version__
```
# 5.2 - Using convnets with small datasets
This notebook contains the code sample found in Chapter 5, Section 2 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 conte... | github_jupyter | import keras
keras.__version__
import os, shutil
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'
# The directory where we will
# store our smaller dataset
base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'
os.mkd... | 0.528533 | 0.980205 |
# Qiskit Pulseで高エネルギー状態へアクセスする
ほとんどの量子アルゴリズム/アプリケーションでは、$|0\rangle$と$|1\rangle$によって張られた2次元空間で計算が実行されます。ただし、IBMのハードウェアでは、通常は使用されない、より高いエネルギー状態も存在します。このセクションでは、Qiskit Pulseを使ってこれらの状態を探索することにフォーカスを当てます。特に、$|2\rangle$ 状態を励起し、$|0\rangle$、$|1\rangle$、$|2\rangle$の状態を分類するための識別器を作成する方法を示します。
このノートブックを読む前に、[前の章](./calibrating-... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.signal import find_peaks
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
import qiskit.pulse as pulse
import qiskit.pulse.library as pulse_lib
f... | 0.473414 | 0.957873 |
# Inaugural Project - Housing demand and taxation
### - *Mathilde Pilgaard, Klara Krogh Hammerum, Louise Albæk Jensen og Oluf Kelkjær*
A given household can spend cash $m$ on either housing or consumption $c$. Quality of housing, $h$, grants household utility and has the cost $p_{h}$ which is subject to progressive tax... | github_jupyter | # Importing relevant packages
from scipy import optimize
import numpy as np
par1 = {'m':0.5,
'phi':0.3,
'epsilon': 0.5,
'r': 0.03,
'tau_g': 0.012,
'tau_p': 0.004,
'p_bar': 3
}
# Creating utility function
def u_func(c, h, phi):
return c**(1-phi)*h**phi
# Creating o... | 0.591959 | 0.938857 |
# Introduction
This notebook shows how to run a strategy against feeds from the Binance crypto exchange using the **roboquant** algo-trading framework.
<img src="https://upload.wikimedia.org/wikipedia/commons/1/12/Binance_logo.svg" alt="Binance" width="400"/>
Roboquant includes a dedicated module for crypto trading... | github_jupyter | %use @http://roboquant.org/roboquant-crypto.json
Welcome()
feed.retrieve("BTCBUSD", "ETHBUSD", interval = Interval.FIVE_MINUTES, timeframe = tf, limit = 250)
val feed = BinanceHistoricFeed()
val tf = Timeframe.past(500.days)
feed.retrieve("BTCBUSD", "ETHBUSD", timeframe = tf)
feed.timeframe
for (asset in feed.assets... | 0.709019 | 0.985608 |
```
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ubuntu/mesolitica-tpu.json'
import string
char_vocabs = [''] + list(string.ascii_lowercase + string.digits) + [' ']
sr = 16000
maxlen = 18
maxlen_subwords = 100
minlen_text = 1
global_count = 0
from google.cloud import storage
import numpy as np
impor... | github_jupyter | import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/ubuntu/mesolitica-tpu.json'
import string
char_vocabs = [''] + list(string.ascii_lowercase + string.digits) + [' ']
sr = 16000
maxlen = 18
maxlen_subwords = 100
minlen_text = 1
global_count = 0
from google.cloud import storage
import numpy as np
import si... | 0.311846 | 0.178025 |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# `GiRaFFE_NRPy` C code library: Conservative-to-Primitive ... | github_jupyter | par.initialize_param(par.glb_param(type="bool", module=thismodule, parname="enforce_orthogonality_StildeD_BtildeU", defaultval=True))
par.initialize_param(par.glb_param(type="bool", module=thismodule, parname="enforce_speed_limit_StildeD", defaultval=True))
par.initialize_param(par.glb_param(type="bool", module=thismod... | 0.540681 | 0.932944 |
# Computing Galactic Orbits of Stars with Gala
## Authors
Adrian Price-Whelan, Stephanie T. Douglas
## Learning Goals
* Query the Gaia data release 2 catalog to retrieve data for a sample of well-measured, nearby stars
* Define high-mass and low-mass stellar samples using color-magnitude selections
* Calculate orbits... | github_jupyter | # astropy imports
import astropy.coordinates as coord
from astropy.table import QTable
import astropy.units as u
from astroquery.gaia import Gaia
# Third-party imports
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# gala imports
import gala.coordinates as gc
import gal... | 0.673621 | 0.991836 |
# Running PARC for clustering analysis of Covid-19 scRNA cells
### Introduction
Parc is a fast clustering algorithm designed to effectively cluster heterogeneity in large single cell data. We show how PARC enables downstream analysis on the recent dataset published by [Liao. et al (2020)](https://www.nature.com/articl... | github_jupyter | import matplotlib.pyplot as plt
import warnings
from numba.errors import NumbaPerformanceWarning
import numpy as np
import pandas as pd
import scanpy as sc
import parc
import harmonypy as hm
datadir = "/home/shobi/Thesis/Data/Covid/GSE145926_RAW/"
file_batches = ['GSM4475051_C148_filtered_feature_bc_matrix.h5','GSM447... | 0.31363 | 0.88642 |
# Diversificación y fuentes de riesgo en un portafolio II - Una ilustración con mercados internacionales.
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://upload.wikimedia.org/wikipedia/commons/5/5f/Map_International_Markets.jpg" width="500px" height="300px" />
> Entonces, la clase pasada vimos cómo... | github_jupyter | # Importamos pandas y numpy
import pandas as pd
import numpy as np
# Resumen en base anual de rendimientos esperados y volatilidades
annual_ret_summ = pd.DataFrame(columns=['EU', 'RU', 'Francia', 'Alemania', 'Japon'], index=['Media', 'Volatilidad'])
annual_ret_summ.loc['Media'] = np.array([0.1355, 0.1589, 0.1519, 0.143... | 0.303113 | 0.987129 |
<div style="width: 100%; overflow: hidden;">
<div style="width: 150px; float: left;"> <img src="https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png" alt="Data For Science, Inc" align="left" border="0" width=150px> </div>
<div style="float: left; margin-left: 10px;"> <h1>Tra... | github_jupyter | from collections import Counter
from pprint import pprint
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import openpyxl
import watermark
%load_ext watermark
%matplotlib inline
%watermark -n -v -m -g -iv
plt.style.use('./d4sci.mplstyle')
book = openpyxl.Workbook()
boo... | 0.274643 | 0.969498 |
```
#Importing the required libraries
import pandas as pd
```
## Dynamic Asset Allocation or Balanced Advantage Fund
These mutual funds invest in both Stocks and Debt/Bonds. Allocation between debt and socks can vary as per market conditions.
## Exctracting Dynamic Asset Allocation or Balanced Advantage Mutual Fund'... | github_jupyter | #Importing the required libraries
import pandas as pd
daa_lump_sum_rtn = pd.read_html(
"https://www.moneycontrol.com/mutual-funds/performance-tracker/returns/dynamic-asset-allocation-or-balanced-advantage.html")
df1 = pd.DataFrame(daa_lump_sum_rtn[0])
#Renaming historical returns column names
df1.rename({'1W': '1W... | 0.466846 | 0.866698 |
# Produce Eastern Hydro Profile Using Multiple Data Sources
Following data sources are used to generate eastern_hydro_v3.csv
* EIA monthly net generation for conventional hydro plants from Form 923
* Hourly total hydro generation profiles of 4 Independent System Operators (ISO): ISONE, NYISO, PJM and SWPP in 2016
* Ho... | github_jupyter | import json
import pytz
import pandas as pd
from tqdm import tqdm
from collections import defaultdict
from timezonefinder import TimezoneFinder
from powersimdata.input.grid import Grid
from powersimdata.network.usa_tamu.constants.zones import (interconnect2loadzone,
... | 0.451568 | 0.872728 |
# Functions
One of the core principles of any programming language is, **"Don't Repeat Yourself"**.
If you have an action that should occur many times, you can define that action once and then call that code whenever you need to carry out that action.
We are already repeating ourselves in our code, so this is a goo... | github_jupyter | # Let's define a function.
def function_name(argument_1, argument_2):
# Do whatever we want this function to do,
# using argument_1 and argument_2
# Use function_name to call the function.
function_name(value_1, value_2)
print("2+2 is equal to: ", 2+2)
print("3+2 is equal to: ", 3+2)
print("3+3 is equal to: ... | 0.444565 | 0.974629 |
<a href="https://colab.research.google.com/github/BaiganKing/DS-Unit-2-Kaggle-Challenge/blob/master/module1/assignment_kaggle_challenge_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science, Unit 2: Predictive Modeling
# Kagg... | github_jupyter | train['functional'] = (train['status_group']=='functional').astype(int)
# Reduce cardinality for NEIGHBORHOOD feature ...
# Get a list of the top 10 neighborhoods
top10 = train['NEIGHBORHOOD'].value_counts()[:10].index
# At locations where the neighborhood is NOT in the top 10,
# replace the neighborhood with 'OTHER... | 0.385375 | 0.974797 |
# Imports
```
import math
import pandas as pd
import pennylane as qml
import time
from keras.datasets import mnist
from matplotlib import pyplot as plt
from pennylane import numpy as np
from pennylane.templates import AmplitudeEmbedding, AngleEmbedding
from pennylane.templates.subroutines import ArbitraryUnitary
from... | github_jupyter | import math
import pandas as pd
import pennylane as qml
import time
from keras.datasets import mnist
from matplotlib import pyplot as plt
from pennylane import numpy as np
from pennylane.templates import AmplitudeEmbedding, AngleEmbedding
from pennylane.templates.subroutines import ArbitraryUnitary
from sklearn.decomp... | 0.549157 | 0.879665 |
# cMLP Lagged VAR Demo
- In this notebook, we train a cMLP model on linear VAR data with lagged interactions.
- After examining the Granger causality discovery, we train a debiased model using only the discovered interactions.
```
import torch
import numpy as np
import matplotlib.pyplot as plt
from synthetic import s... | github_jupyter | import torch
import numpy as np
import matplotlib.pyplot as plt
from synthetic import simulate_lorenz_96
from models.cmlp import cMLP, cMLPSparse, train_model_ista, train_unregularized
# For GPU acceleration
device = torch.device('cuda')
# Simulate data
p = 10
X_np, GC = simulate_lorenz_96(p=p, F=5, T=1000, delta_t=1)
... | 0.715821 | 0.895294 |
# Predict phonon DoS for new materials and evaluate their specific heat capacities
- `ComprehensiveEvaluation`: the function that evaluate phonon DoS and heat capacities with the input `*.cif` file
- `AtomEmbeddingAndSumLastLayer`: the model function
```
import glob
import torch
import torch_geometric
import torch_sc... | github_jupyter | import glob
import torch
import torch_geometric
import torch_scatter
import e3nn
from e3nn import rs, o3
from e3nn.point.data_helpers import DataPeriodicNeighbors
from e3nn.networks import GatedConvParityNetwork
from e3nn.kernel_mod import Kernel
from e3nn.point.message_passing import Convolution
import pymatgen
from... | 0.413004 | 0.808823 |
# Deep Deterministic Policy Gradients (DDPG)
---
In this notebook, we train DDPG with OpenAI Gym's Pendulum-v0 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
from ddpg_agen... | github_jupyter | import gym
import random
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
from ddpg_agent import Agent
env = gym.make('Pendulum-v1')
env.seed(2)
agent = Agent(state_size=3, action_size=1, random_seed=2)
def ddpg(n_episodes=1000, max_t=300, print_every=1... | 0.454472 | 0.936401 |
```
import pandas as pd
import numpy as np
df = pd.read_csv('./data/in_micro_PERSONS_PerthOnly_2011.csv')
df.rename(columns={
'AREAENUM':'sample_geog',
"ABSHID":'serialno'
},inplace=True)
zonecol = 'sample_geog'
df.columns
df
def produce_one_marginal(col, df):
gdf = pd.DataFrame(df[[zonecol,col]])
gdf ... | github_jupyter | import pandas as pd
import numpy as np
df = pd.read_csv('./data/in_micro_PERSONS_PerthOnly_2011.csv')
df.rename(columns={
'AREAENUM':'sample_geog',
"ABSHID":'serialno'
},inplace=True)
zonecol = 'sample_geog'
df.columns
df
def produce_one_marginal(col, df):
gdf = pd.DataFrame(df[[zonecol,col]])
gdf = gd... | 0.132739 | 0.210604 |
# Titanic's data analysis and machine learning
## By Jérémy P. Schneider
As someone new in this field, I decided to take my first challenge with the Titanic dataset from Kaggle (https://www.kaggle.com/c/titanic)
## My OS
For this work I used a computer with :
* Windows 7
* Intel(R) Core(TM) i5-2500K CPU @ 3.3... | github_jupyter | import time
import pandas as pd
import matplotlib.pyplot as plt
import math
import seaborn as sns
import numpy as np
from sklearn import svm
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection ... | 0.242475 | 0.945801 |
```
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import math
%matplotlib inline
# Load data from filesystem
df = pd.read_csv('/kaggle/input/survey_results_public.csv', delimiter=',', nrows = None)
df.dataframeName = 'survey_results_public.csv'
pand... | github_jupyter | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import os
import pandas as pd
import math
%matplotlib inline
# Load data from filesystem
df = pd.read_csv('/kaggle/input/survey_results_public.csv', delimiter=',', nrows = None)
df.dataframeName = 'survey_results_public.csv'
pandasVe... | 0.788217 | 0.689423 |
<a href="https://colab.research.google.com/github/aly202012/Teaching/blob/master/Copy_of_dataexploration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
data = [50,50,47,97,49,3,53,42,26,74,82,62,37,15,70,27,36,35,48,52,63,64]
print(data)
impor... | github_jupyter | data = [50,50,47,97,49,3,53,42,26,74,82,62,37,15,70,27,36,35,48,52,63,64]
print(data)
import numpy as np
grades = np.array(data)
print(grades)
# حدثت مضاعفه للبيانات الاصليه من حيث العدد
print (type(data),'x 2:', data * 2)
print('---')
# تم تطبيق عمليه حسابيه علي القيم الموجوده وبالتالي تضاعفت الارقام من حيث ال... | 0.187207 | 0.810028 |
<div align="right" style="text-align:right"><i>Peter Norvig<br>May 2015</i></div>
# When Cheryl Met Eve: A Birthday Story
The *Cheryl's Birthday* logic puzzle [made the rounds](https://www.google.com/webhp?#q=cheryl%27s+birthday),
and I wrote [code](Cheryl.ipynb) that solves it. In that notebook I said that one rea... | github_jupyter | # Albert and Bernard just became friends with Cheryl, and they want to know when her birthday is.
# Cheryl gave them a list of 10 possible dates:
dates = ['May 15', 'May 16', 'May 19',
'June 17', 'June 18',
'July 14', 'July 16',
'August 14', 'August 15', 'August 17']
def month(date): ... | 0.621196 | 0.921145 |
<title>Learn Quantum Computation using Qiskit</title>
<div class="preface-top">
<div class="preface-checker-pattern"></div>
<div class="preface-summary">
<aside class="preface-summary-image"><img src="images/preface_illustration_2.svg"></aside>
<div class="preface-summary-text">
<p>
Greetings from th... | github_jupyter | # Click 'try', then 'run' to see the output,
# you can change the code and run it again.
print("This code works!")
from qiskit import QuantumCircuit
qc = QuantumCircuit(2) # Create circuit with 2 qubits
qc.h(0) # Do H-gate on q0
qc.cx(0,1) # Do CNOT on q1 controlled by q0
qc.measure_all()
qc.draw() | 0.302288 | 0.92912 |
```
# install keras, tensorflow and tflearn
# (1) Importing dependency
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,\
Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
np.random.seed(1000)
# (2) Get Data
i... | github_jupyter | # install keras, tensorflow and tflearn
# (1) Importing dependency
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,\
Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
np.random.seed(1000)
# (2) Get Data
impor... | 0.91282 | 0.697879 |
```
#使用seaborn
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("paper",font_scale=1.5,rc={'figure.dpi':300})
sns.set_style("ticks") # 风格选择包括："white", "dark", "whitegrid", "darkgrid", "ticks"
sns.set_style({'font.sans-serif': ['SimHei', 'C... | github_jupyter | #使用seaborn
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context("paper",font_scale=1.5,rc={'figure.dpi':300})
sns.set_style("ticks") # 风格选择包括："white", "dark", "whitegrid", "darkgrid", "ticks"
sns.set_style({'font.sans-serif': ['SimHei', 'Calib... | 0.219505 | 0.237267 |
# Lesson 3 Exercise 1: Three Queries Three Tables
<img src="images/cassandralogo.png" width="250" height="250">
### Walk through the basics of creating a table in Apache Cassandra, inserting rows of data, and doing a simple CQL query to validate the information. You will practice Denormalization, and the concept of 1 ... | github_jupyter | import cassandra
from cassandra.cluster import Cluster
try:
cluster = Cluster(['127.0.0.1']) #If you have a locally installed Apache Cassandra instance
session = cluster.connect()
except Exception as e:
print(e)
try:
session.execute("""
CREATE KEYSPACE IF NOT EXISTS udacity
WITH REPLICATION ... | 0.187579 | 0.902695 |
```
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)... | 0.208098 | 0.413773 |
# Everything is object in Python
# 1 Pass through arguments in constructor
```
class Passthrough:
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
pt = Passthrough(name="Zhaokang", age=35)
pt.name
```
# 2 Inheritance
> The order of base class defined... | github_jupyter | class Passthrough:
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
pt = Passthrough(name="Zhaokang", age=35)
pt.name
class Base:
# pass
def __init__(self, name, **kwargs):
print(name)
print(kwargs)
class Derived(Base):
... | 0.682574 | 0.647032 |
```
%load_ext autoreload
%autoreload 2
# default_exp pod.client
```
# Pod Client
```
# export
from pyintegrators.data.itembase import Edge, ItemBase
from pyintegrators.indexers.facerecognition.photo import resize
from pyintegrators.data.schema import *
from pyintegrators.imports import *
from hashlib import sha256
# ... | github_jupyter | %load_ext autoreload
%autoreload 2
# default_exp pod.client
# export
from pyintegrators.data.itembase import Edge, ItemBase
from pyintegrators.indexers.facerecognition.photo import resize
from pyintegrators.data.schema import *
from pyintegrators.imports import *
from hashlib import sha256
# export
DEFAULT_POD_ADDRESS... | 0.240329 | 0.400544 |
We left off with the disturbing realization that even though we are satisfied the requirements of the sampling theorem, we still have errors in our approximating formula. We can resolve this by examining the Whittaker interpolating functions which are used to reconstruct the signal from its samples.
```
%pylab inline... | github_jupyter | %pylab inline
from __future__ import division
t = linspace(-5,5,300) # redefine this here for convenience
fig,ax = subplots()
fs=5.0
ax.plot(t,sinc(fs * t))
ax.grid()
ax.annotate('This keeps going...',
xy=(-4,0),
xytext=(-5+.1,0.5),
arrowprops={'facecolor':'green','shrink':0.05},f... | 0.622574 | 0.986218 |
<h1 align='center'>WebScraping TripAdvisor</h1>
---
El código a continuación tiene por objetivo extraer la **[información solicitada](https://github.com/mozilla/geckodriver/releases/download/v0.28.0/geckodriver-v0.28.0-win64.zip "Word en Google Drive")**, desde la página de **[TripAdvisor](https://www.tripadvisor.cl/... | github_jupyter | %%capture --no-display
import warnings
warnings.filterwarnings('ignore')
import os
print(f'Si es la primera vez que corre este programa, por favor abra la terminal PowerShell de Anaconda' +
f' e ingrese el siguiente comando: "\033[4mpip install -r {os.getcwd()}\\requirements.txt\033[4m"')
import time
import p... | 0.220091 | 0.844601 |
# Rates calculation
This notebooks demonstrates basic rates calculation method. We say "basic"
because we have also implemented a PyTorch module for rates calculation that can work on GPU.
```
import sys
import numpy as np
import pandas as pd
from datetime import timedelta
sys.path.append('..')
from deepfield impor... | github_jupyter | import sys
import numpy as np
import pandas as pd
from datetime import timedelta
sys.path.append('..')
from deepfield import Field
model = Field('../open_data/norne_simplified/norne_simplified.data').load()
(model.wells
.drop_incomplete()
.get_wellblocks(model.grid)
.drop_outside()
.apply_perforations()
.calcu... | 0.198841 | 0.839767 |
```
%matplotlib inline
```
# Pyplot tutorial
An introduction to the pyplot interface.
Intro to pyplot
===============
:mod:`matplotlib.pyplot` is a collection of command style functions
that make matplotlib work like MATLAB.
Each ``pyplot`` function makes
some change to a figure: e.g., creates a figure, creates a... | github_jupyter | %matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()
import numpy as np
# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)... | 0.741487 | 0.987017 |
In this notebook, we will use a multi-layer perceptron to develop time series forecasting models.
The dataset used for the examples of this notebook is on air pollution measured by concentration of
particulate matter (PM) of diameter less than or equal to 2.5 micrometers. There are other variables
such as air pressure,... | github_jupyter | from __future__ import print_function
import os
import sys
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
#set current working directory
os.chdir('D:/Practical Time Series')
#Read the dataset into a pandas.DataFrame
df = pd.read_csv('... | 0.628635 | 0.982757 |
```
# %load setup.py
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as mtick
from itertools import product
%run helpers.ipynb
params_suffstat = pd.read_csv('output/params_suffstat.csv')
params_sim = pd.read_csv('output/params_sim.csv')
params_full = pd.concat([params_suf... | github_jupyter | # %load setup.py
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as mtick
from itertools import product
%run helpers.ipynb
params_suffstat = pd.read_csv('output/params_suffstat.csv')
params_sim = pd.read_csv('output/params_sim.csv')
params_full = pd.concat([params_suffsta... | 0.527803 | 0.551815 |
```
import clustertools as ctools
import numpy as np
from astropy.table import QTable
import matplotlib.pyplot as plt
```
# Loading and Advancing
**Loading**
To manually load a snapshot of a cluster, simply read in the file via your preferred method, declare a StarCluster with the appropriate units and origin, and a... | github_jupyter | import clustertools as ctools
import numpy as np
from astropy.table import QTable
import matplotlib.pyplot as plt
m,x,y,z,vx,vy,vz=np.loadtxt('00000.dat',unpack=True)
cluster=ctools.StarCluster(units='pckms',origin='cluster')
cluster.add_stars(x,y,z,vx,vy,vz,m)
ctools.starplot(cluster)
cluster.analyze()
print('Total... | 0.556159 | 0.896523 |
```
import torch
import numpy as np
from torch import nn, optim
import pandas as pd
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.model_selection import StratifiedKFold
import torch.nn.functional as F
import torchvision
from sklearn.metrics import accuracy_score, confusion_matrix,f1_score... | github_jupyter | import torch
import numpy as np
from torch import nn, optim
import pandas as pd
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.model_selection import StratifiedKFold
import torch.nn.functional as F
import torchvision
from sklearn.metrics import accuracy_score, confusion_matrix,f1_score, pr... | 0.867148 | 0.692408 |
# Text Generation with LSTM
Recurrent neural networks are also known for their ability to generate text. As a result, the output of the neural network can be free-form text. In this section, we will see how to train an LSTM can on a textual document, such as classic literature, and learn to output new text that app... | github_jupyter | from tensorflow.keras.callbacks import LambdaCallback
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.utils import get_file
import numpy as np
import random
import sys
... | 0.625896 | 0.992349 |
# スキーム確認ツール for Navis(I1,I2,I3,I4,A26)
#### excelの全シートを読み込んでDFに格納
新しいフロートの生データ（テキスト版）からJAMSTECのデコード用DBに登録用のフィールドが存在するかを判定する
```
import os
import pandas as pd
import re
import termcolor
import Levenshtein # レーベンシュタイン距離ライブラリにある、ジャロ・ウインクラー距離を計算するのに使う
# jaro_dist = Levenshtein.jaro_winkler(srt1 , str2)
navis_excel = pd.... | github_jupyter | import os
import pandas as pd
import re
import termcolor
import Levenshtein # レーベンシュタイン距離ライブラリにある、ジャロ・ウインクラー距離を計算するのに使う
# jaro_dist = Levenshtein.jaro_winkler(srt1 , str2)
navis_excel = pd.read_excel('Navis.xlsx' , sheet_name=None) # sheet_name=Noneで全てのシート読み込む
def jaro_dist(str1,str2):
return Levenshtein.jaro_win... | 0.043437 | 0.80567 |
# Source contributions
```
import pickle
import numpy as np
import netCDF4 as nc
import pandas as pd
from calendar import monthrange
%matplotlib inline
```
###### Parameters:
```
# domain dimensions:
imin, imax = 1479, 2179
jmin, jmax = 159, 799
isize = imax-imin
jsize = jmax-jmin
# Mn model result folders:
folder... | github_jupyter | import pickle
import numpy as np
import netCDF4 as nc
import pandas as pd
from calendar import monthrange
%matplotlib inline
# domain dimensions:
imin, imax = 1479, 2179
jmin, jmax = 159, 799
isize = imax-imin
jsize = jmax-jmin
# Mn model result folders:
folder_ref = '/data/brogalla/run_storage/Mn-reference-202... | 0.323594 | 0.668124 |
# Traveling Salesman Problem
## Objective and Prerequisites
In this notebook, you will learn how to:
1. Formulate the Traveling Salesman Problem (TSP) as a MIP model.
2. Use lazy constraints to identify solutions of the TSP problem that are infeasible.
This modeling example is at the advanced level, where we assume ... | github_jupyter | import json
# Read capital names and coordinates from json file
capitals_json = json.load(open('capitals.json'))
capitals = []
coordinates = {}
for state in capitals_json:
if state not in ['AK', 'HI']:
capital = capitals_json[state]['capital']
capitals.append(capital)
coordinates[capital] = (floa... | 0.504883 | 0.983391 |
## Deep Learning Challenge
### Loading the CIFAR10 data
The data can be loaded directly from keras (`keras.datasets.cifar10`).
```python
cifar10 = keras.datasets.cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
```
```
from tensorflow.keras.models import Sequential
from tensorfl... | github_jupyter | cifar10 = keras.datasets.cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.k... | 0.912974 | 0.976602 |
## Exploring `Series` and `DataFrame` Objects
### Working with pandas
*Curtis Miller*
Let's create some `Series`.
```
import pandas as pd
from pandas import Series, DataFrame
import numpy as np
ser1 = Series([1, 2, 3, 4])
ser2 = Series(['a', 'b', 'c'])
print(ser1)
print(ser2)
# Create a pandas Index
idx = pd.Index(["... | github_jupyter | import pandas as pd
from pandas import Series, DataFrame
import numpy as np
ser1 = Series([1, 2, 3, 4])
ser2 = Series(['a', 'b', 'c'])
print(ser1)
print(ser2)
# Create a pandas Index
idx = pd.Index(["New York", "Los Angeles", "Chicago",
"Houston", "Philadelphia", "Phoenix", "San Antonio",
... | 0.457621 | 0.938969 |
# R: Impact of 401(k) on Financial Wealth
In this real-data example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate the effect of 401(k) eligibility and participation on accumulated assets. The 401(k) data set has been analyzed in several studies, among ot... | github_jupyter | # Load required packages for this tutorial
library(DoubleML)
library(mlr3)
library(mlr3learners)
library(data.table)
library(ggplot2)
# suppress messages during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
# load data as a data.table
data = fetch_401k(return_type = "data.table", instrument = TRUE)
dim(data)... | 0.792986 | 0.984351 |
# Create a Local Docker Image
In this section, we will create an IoT Edge module, a Docker container image with an HTTP web server that has a scoring REST endpoint.
## Get Global Variables
```
import sys
sys.path.append('../../../common')
from env_variables import *
```
## Create Web Application & Inference Server f... | github_jupyter | import sys
sys.path.append('../../../common')
from env_variables import *
%%writefile $lvaExtensionPath/app.py
import threading
import cv2
import numpy as np
import io
import onnxruntime
import json
import logging
import linecache
import sys
from score import MLModel, PrintGetExceptionDetails
from flask import Flask, ... | 0.171616 | 0.461077 |
## Heat flow estimation from aerial radiometric measurements.
In this notebook we are concerned with the image processing of radiometric maps from Nova Scotia https://novascotia.ca/natr/meb/download/dp163.asp and the subsequent estimate of heat flow following the equations in Beamish and Busby (2016).
### About the d... | github_jupyter | """
If the colourmap matches all or part of the colour wheel or hue circle,
we can decompose the image to HSV and use H as a proxy for the data.
"""
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import requests
from skimage.color import rgb2hsv
import glob
def heat_equ... | 0.845209 | 0.972934 |
# Distirbuted Training of Mask-RCNN in Amazon SageMaker using EFS
This notebook is a step-by-step tutorial on distributed tranining of [Mask R-CNN](https://arxiv.org/abs/1703.06870) implemented in [TensorFlow](https://www.tensorflow.org/) framework. Mask R-CNN is also referred to as heavy weight object detection model... | github_jupyter | aws_region = # <aws-region>
s3_bucket = # <your-s3_bucket>
!cat ./prepare-s3-bucket.sh
%%time
!./prepare-s3-bucket.sh {s3_bucket}
!cat ./prepare-efs.sh
%%time
!./prepare-efs.sh {s3_bucket}
!cat ./container/build_tools/build_and_push.sh
%%time
! ./container/build_tools/build_and_push.sh {aws_region}
tensorpack_i... | 0.452536 | 0.966663 |
# Analyzing a PAC
I'm trying to understand the spending habits of the Microsoft PAC since I work there. The following is primarily based on the code from [this workbook](https://github.com/boblannon/blogpost_fec-api-howto/blob/master/fec_api.ipynb) from [Bob Lannon](https://github.com/boblannon) that explains how to us... | github_jupyter | %pylab inline
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import numpy as np
import pandas as pd
import requests
import os
import json
from copy import deepcopy
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
logging.getLogger("requests").setLevel(logging.ERROR... | 0.195786 | 0.832985 |
# 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 API key
from api_keys import g_key
csvpath = '../WeatherPy/output_data/cities.csv'
cities_df = pd.read_csv(csvpath)
cities_df['Max Temp'] = cities_df['Max Temp']*9/5 -459.67... | 0.677581 | 0.844216 |
```
%matplotlib inline
import datetime as dt
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import mean_squared_error
from alphamind.api import *
from PyFin.api import *
plt.style.use('ggplot')
engine = SqlEngine('postgres+psycopg2://postgres:A12345678!@10.63.6.220/alp... | github_jupyter | %matplotlib inline
import datetime as dt
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import mean_squared_error
from alphamind.api import *
from PyFin.api import *
plt.style.use('ggplot')
engine = SqlEngine('postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha')... | 0.5144 | 0.600423 |
# Iris species classification.
**Problem statement** -
Given _Sepal Length_ ,_Sepal width_, _Petal length_ and _Petal width_ classify
each instance into one of **Iris-setosa, Iris-versicolor or Iris-virginica** species using Machine learning Classification algorithms.
## Importing the required libraries.
```
impor... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, f1_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTr... | 0.758153 | 0.939526 |
## 实体链指比赛方案分享
### 1. **任务与难点介绍**
面向中文短文本的实体链指，简称 EL（Entity Linking），是NLP、知识图谱领域的基础任务之一，即对于给定的一个中文短文本（如搜索 Query、微博、对话内容、文章/视频/图片的标题等），EL将其中的实体与给定知识库中对应的实体进行关联。
此次任务的输入输出定义如下：
输入：中文短文本以及该短文本中的实体集合。
输出：输出文本此中文短文本的实体链指结果。每个结果包含：实体 mention、在中文短文本中的位置偏移、其在给定知识库中的 id，如果为 NIL 情况，需要再给出实体的上位概念类型。
传统的实体链指任务主要是针对长文档，长文档拥有在写的上下文信息... | github_jupyter | ## 环境配置：基于PaddlePaddle 1.8.4开发（python 3.7）， 使用单块V100（32G）训练
## 各个文件的作用：
### eval.py 官方提供的评估脚本
### post_matching.py 实体消歧模型后处理，对每个实体选取概率最大的一个作为kb_id（若小于一个阈值，则取NIL）（单模）
### main_nil.py 实体分类模型的推理代码，对实体消歧模型预测为NIL的实体预测其类别
### post_nil.py 实体分类模型的后处理代码，生成提交文件（单模）
### utils.py 定义各种训练，推理过程中需要的函数等
### main_matching.py 实... | 0.196094 | 0.63576 |
<a href="https://colab.research.google.com/github/szoha/test/blob/master/Copy_of_astho_ubaid.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction
In this notebook, we implement [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training o... | github_jupyter | !git clone https://github.com/roboflow-ai/pytorch-YOLOv4.git
%cd /content/pytorch-YOLOv4
!pip install -r requirements.txt
# download yolov4 weights that have already been converted to PyTorch
!gdown https://drive.google.com/uc?id=1fcbR0bWzYfIEdLJPzOsn4R5mlvR6IQyA
# REPLACE this link with your Roboflow dataset (export ... | 0.425605 | 0.973215 |
```
import requests
import simplejson as json
import pandas as pd
import numpy as np
import os
import json
import math
from openpyxl import load_workbook
notebook_path = os.path.abspath("OLS matching.ipynb")
# Path to config file
config_path = os.path.join(os.path.dirname(notebook_path), "Data/config.json")
# Path to... | github_jupyter | import requests
import simplejson as json
import pandas as pd
import numpy as np
import os
import json
import math
from openpyxl import load_workbook
notebook_path = os.path.abspath("OLS matching.ipynb")
# Path to config file
config_path = os.path.join(os.path.dirname(notebook_path), "Data/config.json")
# Path to asc... | 0.469277 | 0.216239 |
# CSCE 5290 - Final Project
## Pre-trained model evaluation (BART, T5)
Dan Waters (danwaters@my.unt.edu)
```
!pip install transformers
from transformers import pipeline
summarizer = pipeline("summarization")
# Get the data (not the one with the start tokens)
!gdown --id 17u3TvSpRq17mFVJ1pEKf6D9fYgBFj4lI
import pandas ... | github_jupyter | !pip install transformers
from transformers import pipeline
summarizer = pipeline("summarization")
# Get the data (not the one with the start tokens)
!gdown --id 17u3TvSpRq17mFVJ1pEKf6D9fYgBFj4lI
import pandas as pd
cnn_df = pd.read_csv('cnn_cleaned_test_10k.csv')
cnn_df = cnn_df[['text', 'summary']]
cnn_df.head(5)
art... | 0.443841 | 0.752581 |
```
import numpy as np
import scipy.stats as stats
from matplotlib import pyplot as plt
x = np.arange(0, 1, 0.01)
uno = np.full(x.shape,1)
low = np.full(x.shape,0.01)
ph = 0.5
#plt.rcParams['figure.dpi'] = 72
plt.rcParams['figure.figsize'] = [10, 10]
plt.xticks([0,1 / 6, 0.25, 3/8, 0.5, 5/8, 0.75, 5/6,1])
plt.plot(... | github_jupyter | import numpy as np
import scipy.stats as stats
from matplotlib import pyplot as plt
x = np.arange(0, 1, 0.01)
uno = np.full(x.shape,1)
low = np.full(x.shape,0.01)
ph = 0.5
#plt.rcParams['figure.dpi'] = 72
plt.rcParams['figure.figsize'] = [10, 10]
plt.xticks([0,1 / 6, 0.25, 3/8, 0.5, 5/8, 0.75, 5/6,1])
plt.plot(x,un... | 0.411111 | 0.725041 |
<h1 style="text-align:center">Deep Learning </h1>
<h1 style="text-align:center"> Lab Session 2 - 3 Hours </h1>
<h1 style="text-align:center"> Convolutional Neural Network (CNN) for Handwritten Digits Recognition</h1>
<b> Student 1:</b> CANALE
<b> Student 2:</b> ELLENA
The aim of this session is to practice with... | github_jupyter | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test =... | 0.832509 | 0.985608 |
This notebook is part of the `nbsphinx` documentation: https://nbsphinx.readthedocs.io/.
# Code Cells
## Code, Output, Streams
An empty code cell:
Two empty lines:
```
```
Leading/trailing empty lines:
```
# 2 empty lines before, 1 after
```
A simple output:
```
6 * 7
```
The standard output stream:
```
pr... | github_jupyter | ```
Leading/trailing empty lines:
A simple output:
The standard output stream:
Normal output + standard output
The standard error stream is highlighted and displayed just below the code cell.
The standard output stream comes afterwards (with no special highlighting).
Finally, the "normal" output is display... | 0.779574 | 0.949389 |
**This notebook is an exercise in the [Introduction to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/machine-learning-competitions).**
---
# Introduction
In this exercise, you will create and submit ... | github_jupyter | # Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex7 import *
# Set up filepaths
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-dat... | 0.640299 | 0.955527 |
# Inverse Kinematics Optimization
The previous doc explained features and how they define objectives of a constrained optimization problem. Here we show how to use this to solve IK optimization problems.
At the bottom there is more general text explaining the basic concepts.
## Demo of features in Inverse Kinematics... | github_jupyter | import sys
sys.path.append('../../lib') #rai/lib')
import numpy as np
import libry as ry
C = ry.Config()
C.addFile('../../../rai-robotModels/pr2/pr2.g')
C.addFile('../../../rai-robotModels/objects/kitchen.g')
C.view()
goal = C.addFrame("goal")
goal.setShape(ry.ST.sphere, [.05])
goal.setColor([.5,1,1])
goal.setPosition... | 0.337749 | 0.95594 |
# ml lab6
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
```
### 1. read data
```
data = scipy.io.loadmat('data/ex6data1.mat')
X = data['X']
X.shape
```
### 2. random init centroids
```
def rand_centroids(X, K):
rand_indices = np.arange(len(X))
np.random.shuffle(rand_indices)
c... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
data = scipy.io.loadmat('data/ex6data1.mat')
X = data['X']
X.shape
def rand_centroids(X, K):
rand_indices = np.arange(len(X))
np.random.shuffle(rand_indices)
centroids = X[rand_indices][:K]
return centroids
rand_centroids(X, 3)
def ... | 0.430147 | 0.918663 |
# Pandoc Markdown Syntax
Pandoc supports a large number of input file formats for processing (including markdown, reStructuredText, textile, HTML, DocBook, LaTeX, MediaWiki markup, TWiki markup, OPML, Emacs Org-Mode, Txt2Tags, Microsoft Word docx, LibreOffice ODT, EPUB, or Haddock markup) into a vast number of output ... | github_jupyter | 3**4
% title
% author(s) (separated by semicolons)
% date (treated as a text string)
---
title: title
author: author(s) (separated by semicolons)
date: date (treated as a text string)
...
# Level One
## Level Two
### Level Three
...
###### Level Six
A Level One Header
==================
A Level Two Header
--------... | 0.915427 | 0.982757 |
```
import torch
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import time
import dataset.dataset as dataset
import datasplit.datasplit as datasplit
import model.models as models
import trainer.trainer as trainer
import... | github_jupyter | import torch
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import time
import dataset.dataset as dataset
import datasplit.datasplit as datasplit
import model.models as models
import trainer.trainer as trainer
import uti... | 0.494629 | 0.624752 |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | 0.628407 | 0.400867 |
# Practical Deep Learning for Coders, v3
# 00_notebook_tutorial
**Important note:** You should always work on a duplicate of the course notebook. On the page you used to open this, tick the box next to the name of the notebook and click duplicate to easily create a new version of this notebook.<br>
You will get error... | github_jupyter | 1+1
3/2
# Import necessary libraries
from fastai.vision import *
import matplotlib.pyplot as plt
from PIL import Image
a = 1
b = a + 1
c = b + a + 1
d = c + b + a + 1
a, b, c ,d
plt.plot([a,b,c,d])
plt.show()
Image.open('images/notebook_tutorial/cat_example.jpg')
from fastai import*
from fastai.vision import *
?I... | 0.243642 | 0.916969 |
```
import numpy
%matplotlib notebook
import matplotlib.pyplot
import scipy.interpolate
import scipy.integrate
import pynverse
```
# Arc Length Reparameterization
## Overview
To have control over the speed and acceleration of an object along a path, the path should be parameterized on distance. It is much easier to ... | github_jupyter | import numpy
%matplotlib notebook
import matplotlib.pyplot
import scipy.interpolate
import scipy.integrate
import pynverse
l_a = lambda t: numpy.array([[3 + 0 * t], [0 + 1 * t]])
l_b = lambda t: numpy.array([[1 - 1 * t], [3 + 0 * t]])
fig, ax = matplotlib.pyplot.subplots()
t_a = t_b = numpy.linspace(0, 1, 100)
ax.p... | 0.518059 | 0.981275 |
```
import os
import pickle
import pandas as pd
import numpy as np
from PIL import Image
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.utils import to_categorical
from keras.preprocessing import image
EMOTIONS = [
"angry",
"calm",
"disgust",
"fear",
... | github_jupyter | import os
import pickle
import pandas as pd
import numpy as np
from PIL import Image
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.utils import to_categorical
from keras.preprocessing import image
EMOTIONS = [
"angry",
"calm",
"disgust",
"fear",
... | 0.50952 | 0.379148 |
# XGBoost
Ranklib is a relatively old library and doesn't have the wide spread use that XGBoost does. Ranklib is still under active development, but the fork of the project OSC created reflects an older version.
The ES-LTR plugin is designed to work with XGBoost model format. This notebook starts with the `classic` t... | github_jupyter | import ltr.judgments as judge
df = [j for j in judge.judgments_from_file(open('data/classic-training.txt'))]
df = judge.judgments_to_dataframe(df)
df
import pandas as pd
import xgboost as xgb
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 50,150
df = df[['grade', 'features0']]
features = df[['feat... | 0.26341 | 0.924005 |
# Survival Analysis in Python
Chapter 1
Allen B. Downey
[MIT License](https://en.wikipedia.org/wiki/MIT_License)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
import ... | github_jupyter | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
import utils
from utils import decorate
from empyrical_dist import Pmf, Cdf
Dataset from:
V.J. Menon and D.C. Agrawal, Re... | 0.796055 | 0.892609 |
<a href="https://colab.research.google.com/github/jaya-shankar/education-impact/blob/jaya-shankar/randomForest.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!rm -rf education-impact
!rm education-impact
!git clone https://github.com/jaya-shank... | github_jupyter | !rm -rf education-impact
!rm education-impact
!git clone https://github.com/jaya-shankar/education-impact.git
!pip install tensorflow_decision_forests
!pip install wurlitzer
root = "education-impact/"
datasets_path = {
"infant_mortality" : root+ "datasets/Infant_Mortality_Rate.csv",
... | 0.455199 | 0.904566 |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20,20)
import os,time
from glob import glob
from PIL import Image
from sklearn import neighbors
import re
df = pd.read_csv('flags_url.csv')
def read_flag(countrycode='IN',file='', res=(128,64)):
countrycod... | github_jupyter | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20,20)
import os,time
from glob import glob
from PIL import Image
from sklearn import neighbors
import re
df = pd.read_csv('flags_url.csv')
def read_flag(countrycode='IN',file='', res=(128,64)):
countrycode = ... | 0.158891 | 0.365145 |
# Aggregated model - class - test set: 3 domain features
## Table of contents
1. [Linear Regression](#LinearRegression)
2. [MLP (Dense)](#MLP)
3. [AE combined latent](#AE_combined)
4. [AE OTU latent](#AE_latentOTU)
```
import sys
sys.path.append('../../Src/')
from data import *
from train_2 import *
from transfer_lea... | github_jupyter | import sys
sys.path.append('../../Src/')
from data import *
from train_2 import *
from transfer_learning import *
from test_functions import *
from layers import *
from utils import *
from loss import *
from metric import *
from results import *
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.k... | 0.361277 | 0.873215 |
```
class ContosoSIS(BaseOEAModule):
def __init__(self, oea, source_folder='contoso_sis', pseudonymize = True):
BaseOEAModule.__init__(self, oea, source_folder, pseudonymize)
self.schemas['studentattendance'] = [['id', 'string', 'no-op'],
['student_id', 's... | github_jupyter | class ContosoSIS(BaseOEAModule):
def __init__(self, oea, source_folder='contoso_sis', pseudonymize = True):
BaseOEAModule.__init__(self, oea, source_folder, pseudonymize)
self.schemas['studentattendance'] = [['id', 'string', 'no-op'],
['student_id', 'strin... | 0.278453 | 0.238107 |
# Solve a Generalized Assignment Problem using Lagrangian relaxation
This tutorial includes data and information that you need to set up decision optimization engines and build mathematical programming models to solve a Generalized Assignment Problem using Lagrangian relaxation.
When you finish this tutorial, you'll... | github_jupyter | import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
try:
import cplex
except:
raise Exception('Please install CPLEX. See https://pypi.org/project/cplex/')
B = [15, 15, 15]
C = [
[ 6, 10, 1],
[12, 12, 5],
[15, 4, 3],
... | 0.342022 | 0.985454 |
# 项目：全美枪支数据分析
## 目录
<ul>
<li><a href="#intro">简介</a></li>
<li><a href="#wrangling">数据整理</a></li>
<li><a href="#eda">探索性数据分析</a></li>
<li><a href="#conclusions">结论</a></li>
</ul>
<a id='intro'></a>
## 简介
> 该数据来自联邦调查局 (FBI) 的全国即时犯罪背景调查系统 (NICS)。NICS 用于确定潜在买家是否有资格购买枪支或爆炸物。枪支店可以进入这个系统，以确保每位客户没有犯罪记录或符合资格购买。该数据已经收纳了来自 cen... | github_jupyter | # 用这个框对你计划使用的所有数据包进行设置
# 导入语句。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# 加载数据并打印几行。进行这几项操作，来检查数据
# 类型，以及是否有缺失数据或错误数据的情况。
df_gun = pd.read_excel('gun_data.xlsx')
df_census = pd.read_csv('U.S. Census Data.csv')
#分析枪支持有数据
df_gun.info()
df_gun.fillna(0, inplace=True... | 0.282394 | 0.842831 |
# Covid-19 status in Chile
> Covid-19 overview in Chile
- toc: true
- badges: true
- comments: true
- author: Alonso Silva Allende
- categories: [jupyter]
- image: images/Chile-total-confirmed-cases.png
```
#hide
import numpy as np
import pandas as pd
import altair as alt
#hide
from IPython.display import display_htm... | github_jupyter | #hide
import numpy as np
import pandas as pd
import altair as alt
#hide
from IPython.display import display_html, HTML
#hide
update_date = pd.to_datetime('today') - pd.offsets.Hour(19)
today = update_date.strftime('%Y-%m-%d')
today
#hide
date_one_week_ago = (update_date - pd.offsets.Day(7)).strftime('%Y-%m-%d')
date_on... | 0.236957 | 0.645176 |
```
%load_ext autoreload
%autoreload 2
from GPy.models import GPRegression
from GPy.kern import Matern52, Exponential
from summit.utils.models import GPyModel, ModelGroup
from summit.utils.dataset import DataSet
from GPy.inference.optimization import Adam, RProp, Optimizer
from scipydirect import minimize as direct
imp... | github_jupyter | %load_ext autoreload
%autoreload 2
from GPy.models import GPRegression
from GPy.kern import Matern52, Exponential
from summit.utils.models import GPyModel, ModelGroup
from summit.utils.dataset import DataSet
from GPy.inference.optimization import Adam, RProp, Optimizer
from scipydirect import minimize as direct
import ... | 0.735642 | 0.689155 |
# The Inference Button: Bayesian GLMs made easy with PyMC3
Author: Thomas Wiecki
This tutorial appeared as a post in a small series on Bayesian GLMs on my blog:
1. [The Inference Button: Bayesian GLMs made easy with PyMC3](http://twiecki.github.com/blog/2013/08/12/bayesian-glms-1/)
2. [This world is far from Nor... | github_jupyter | %matplotlib inline
from pymc3 import *
import numpy as np
import matplotlib.pyplot as plt
size = 200
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
# y = a + b*x
true_regression_line = true_intercept + true_slope * x
# add noise
y = true_regression_line + np.random.normal(scale=.5, size=size)
data ... | 0.77373 | 0.974067 |
# Simulations with Model Violations: Aperiodic
In this set of simulations, we will test power spectrum parameterization performance across power spectra which violate model assumptions, specifically in the aperiodic component.
In particular, we will explore the influence of simulating data and fitting with aperiodic ... | github_jupyter | %matplotlib inline
from os.path import join as pjoin
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import spearmanr, mode
from fooof import FOOOF, FOOOFGroup, fit_fooof_3d
from fooof.plts import plot_spectrum
from fooof.sim import gen_power_spectrum, gen_group_power_spectra
from fooof.sim.utils ... | 0.687 | 0.98652 |
```
import pandas as pd
import itertools
from sklearn.metrics import confusion_matrix
from tqdm import tqdm
tqdm.pandas()
```
# Summary
ABCD Face recognition models are regular convolutional neural networks models. They represent face photos as vectors. We find the distance between these two vectors to compare tw... | github_jupyter | import pandas as pd
import itertools
from sklearn.metrics import confusion_matrix
from tqdm import tqdm
tqdm.pandas()
# Ref: https://github.com/serengil/deepface/tree/master/tests/dataset
idendities = {
"Angelina": ["img1.jpg", "img2.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img10.jpg", "img11.jpg"],
... | 0.324985 | 0.878419 |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ignore')
# TPU or GPU ... | 0.553264 | 0.547343 |
<a href="https://colab.research.google.com/github/nsstnaka/machine_learning_handson/blob/master/stock_price_prediction_with_rnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# ディープラーニングによる株価予測
直近50営業日の4本値＋出来高をもとに、終値を予想します。
## 前準備
ライブラリのimport
... | github_jupyter | import pandas as pd
import pandas_datareader as pdr
import numpy as np
import tensorflow as tf
import seaborn as sns
import matplotlib.pyplot as plt
tf.__version__
df = pdr.data.DataReader('^DJI', 'yahoo', '2017-04-01', '2020-03-31') # '^DJI'の部分を変えると違う株価を拾える（例：AAPL, GOOG）
df.reset_index(inplace=True) # 後続処理のためインデック... | 0.558809 | 0.976625 |
# Preliminary XGBoost
This notebook outlines preliminary work done to tune the XGBoost classifier. **The results in this notebook are superceeded by those in `xgb_tuning.ipynb` for the purposes of the report.** This notebook does however show comparable results for gradient boosting and explores a broader design space.... | github_jupyter | import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from itertools import cycle
from sklearn.metrics import RocCurveDisplay
classes = ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']
def plot_mc_roc(Y_test_bin, Y_test_proba, n_classes, title='ROC Curve'):
... | 0.63375 | 0.917635 |
# Modern Data Science
**(Module 11: Data Analytics (IV))**
---
- Materials in this module include resources collected from various open-source online repositories.
- You are free to use, change and distribute this package.
Prepared by and for
**Student Members** |
2006-2018 [TULIP Lab](http://www.tulip.org.au), Aus... | github_jupyter | from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
%matplotlib inline
diabetes = load_diabetes()
diabetes_X = diabetes.data[:, None, 2]
LinReg = LinearRegression()
from sklearn.model_selection import train_test_split
X_trainset, X_testset... | 0.708313 | 0.991546 |
# Single Layer Perceptron
```
import numpy as np
class Perceptron(object):
def __init__(self, input_size, lr = 1, epochs = 10):
self.W = np.zeros(input_size + 1)
self.epochs = epochs
self.lr = lr
def activation_fn(self, x):
return 1 if x >= 0 else 0
def predict(self, x):
z = self.W.T.dot(... | github_jupyter | import numpy as np
class Perceptron(object):
def __init__(self, input_size, lr = 1, epochs = 10):
self.W = np.zeros(input_size + 1)
self.epochs = epochs
self.lr = lr
def activation_fn(self, x):
return 1 if x >= 0 else 0
def predict(self, x):
z = self.W.T.dot(x)
a = self.activation_fn(z... | 0.70416 | 0.913754 |
## Evaluating HPO Space of SVD algorithm
This notebook contains evaluation of RMSE of SVD models at Movielens datasets
using different numbers of factors and regularization constants.
Initial setup: imports and working dir
```
import os
while not os.path.exists('.gitmodules'):
os.chdir('..')
from typing import ... | github_jupyter | import os
while not os.path.exists('.gitmodules'):
os.chdir('..')
from typing import Dict
import matplotlib.pyplot as plt
import pandas as pd
from parameters import get_env_parameters
from util.hpo_space_eval_utils import eval_svd_hpo_space, visualize_hpo_space
from util.datasets import MOVIELENS_100K, MOVIELENS... | 0.5144 | 0.832134 |
```
%matplotlib inline
```
ImageContainer object
=====================
This tutorial shows how to use `squidpy.im.ImageContainer` to interact
with image structured data.
The ImageContainer is the central object in Squidpy containing the high
resolution images. It wraps `xarray.Dataset` and provides different
croppin... | github_jupyter | %matplotlib inline
import squidpy as sq
import numpy as np
arr = np.ones((100, 100, 3))
arr[40:60, 40:60] = [0, 0.7, 1]
print(arr.shape)
img = sq.im.ImageContainer(arr, layer="img1")
img
arr1 = arr.transpose(2, 0, 1)
print(arr1.shape)
img = sq.im.ImageContainer(arr1, dims=("channels", "y", "x"), layer="img1")
img
... | 0.272508 | 0.990642 |
## 1. Import Libraries
```
import psycopg2
import pandas as pd
import numpy as np
import xgboost as xgb
import tensorflow as tf
from functools import reduce
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImpu... | github_jupyter | import psycopg2
import pandas as pd
import numpy as np
import xgboost as xgb
import tensorflow as tf
from functools import reduce
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessi... | 0.234582 | 0.616878 |
<a href="https://colab.research.google.com/github/yukinaga/ai_programming/blob/main/lecture_05/01_gradient_decent.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 勾配降下法
勾配降下法では、関数の傾き（勾配）に基づき関数を最小化します。
ディープラーニングにおいて、出力と正解の誤差を最小化するために使われます。
## 勾配降... | github_jupyter | import numpy as np
import matplotlib.pyplot as plt
def my_func(x): # 最小値を求める関数
return x**2 - 2*x
def grad_func(x): # 導関数
return 2*x - 2
eta = 0.1 # 学習係数
x = 4.0 # xに初期値を設定
record_x = [] # xの記録
record_y = [] # yの記録
for i in range(20): # 20回xを更新する
y = my_func(x)
record_x.append(x)
record_y.a... | 0.209551 | 0.976669 |
<a href="https://colab.research.google.com/github/amita-kapoor/UO-Artificial-Intelligence-Cloud-and-Edge-Implementations/blob/master/Excercise_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Classification Exercises
For these exer... | github_jupyter | import tensorflow as tf
import numpy as np
from tensorflow import keras
def built_model(input_shape, n_hidden, nb_classes, optimizer='SGD'):
'''
The function builds a fully connected neural network with two hidden layers
Arguments:
input_shape: The number of inputs to the neural network
n_hidden: Number of h... | 0.886972 | 0.993029 |
<a href="https://colab.research.google.com/github/aletcher/impossibility-global-convergence/blob/master/impossibility_global_convergence.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Accompanying code for the paper: [On the Impossibility of Global... | github_jupyter | import numpy as np
import torch
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-darkgrid')
#@markdown Plotting function.
def plot_param(th, algo, start=0):
fig, ax = plt.subplots(nrows=2, ncols=5, figsize=(20, 8))
ax = ax.flatten()
for i, algo in enumerate(algos):
ax[i].set_xlim(-... | 0.550849 | 0.945951 |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.graphics.tsaplots import plot_acf
from matplotlib.pyplot import figure
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
d... | github_jupyter | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.graphics.tsaplots import plot_acf
from matplotlib.pyplot import figure
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
def v... | 0.527803 | 0.800926 |
<img src="../img/logo_amds.png" alt="Logo" style="width: 128px;"/>
# AmsterdamUMCdb - Freely Accessible ICU Database
version 1.0.2 March 2020
Copyright © 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science
# <a id='freetextitems'></a>freetextitems table
The *freetextitems* table contains all observations... | github_jupyter | %matplotlib inline
import amsterdamumcdb
import psycopg2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import io
from IPython.display import display, HTML, Markdown
#matplotlib settings for image size
#needs to be in a different... | 0.36557 | 0.848282 |
<a href="https://colab.research.google.com/github/maxigaarp/Gestion-De-Datos-en-R/blob/main/Tarea1_V2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
En las clases de gestión de datos hemos aprendido acerca de la eficiencia de bases de datos, modelo... | github_jupyter | #rendim2017 <- read.csv("http://datos.mineduc.cl/datasets/180324-rendimiento-escolar-ano-2017.download/",row.names=NULL, sep=";")
#rendim2018 <- read.csv("http://datos.mineduc.cl/datasets/189328-rendimiento-escolar-ano-2018.download/",row.names=NULL, sep=";")
## Hay un problema descargando directamente los datos desde... | 0.258981 | 0.934455 |
```
%matplotlib inline
%run ../setup/nb_setup
```
# Orbits 3: Orbits in Triaxial Potentials
Author(s): Adrian Price-Whelan
## Learning goals
In this tutorial, we will introduce triaxial potential models, and explore the additional complexity that this brings to the landscape of orbits, as compared to orbits in axi... | github_jupyter | %matplotlib inline
%run ../setup/nb_setup
from astropy.constants import G
import astropy.units as u
from IPython.display import HTML
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import gala.dynamics as gd
import gala.integrate as gi
import gala.p... | 0.601477 | 0.993235 |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter | from random import randrange
from math import sin,cos, pi
# randomly pick an angle
random_angle = randrange(360)
print("random angle is",random_angle)
# pick angle in radian
rotation_angle = random_angle/360*2*pi
# the quantum state
quantum_state = [ cos(rotation_angle) , sin (rotation_angle) ]
the_expected_number_... | 0.705278 | 0.988734 |
```
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from joblib import dump
from src.mo... | github_jupyter | import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from joblib import dump
from src.models... | 0.312055 | 0.685818 |
# Create your own fake fMRI results
With this short jupyter notebook, you can create your own fake fMRI results. The only thing that you have to do is to specify the fake clusters that you want to create under **Targets**. After that you can run the whole notebook, either by using SHIFT+ENTER for each cell, or by sele... | github_jupyter | # Target: Location, radius, intensity
target = [([150, 105, 80], 20, 1.),
([30, 105, 80], 25, -1.),
([65, 30, 75], 30, -1.2),
([115, 30, 75], 30, 1.2)]
%pylab inline
import numpy as np
import nibabel as nb
from nilearn.plotting import plot_stat_map, plot_glass_brain, cm
from nilearn.image... | 0.761361 | 0.873107 |
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