code stringlengths 2.5k 6.36M | kind stringclasses 2
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|---|---|---|---|---|
# Notes on linux
This is a simple note, I don't have clear index to arrage the content in this note. I just take the notes which I may be easy forget.
** Author: Yue-Wen FANG **
** Contact: fyuewen@gmail.com **
** Revision history: created in 16th, December 2017, at Kyoto **
## 1. 配置ssh连接
Professionally speaki... | github_jupyter | # Notes on linux
This is a simple note, I don't have clear index to arrage the content in this note. I just take the notes which I may be easy forget.
** Author: Yue-Wen FANG **
** Contact: fyuewen@gmail.com **
** Revision history: created in 16th, December 2017, at Kyoto **
## 1. 配置ssh连接
Professionally speaki... | 0.480479 | 0.296311 |
```
#data https://archive.ics.uci.edu/ml/datasets/sms+spam+collection
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import nltk
%matplotlib inline
plt.style.use('ggplot')
nltk.download()
messages = [line.rstrip() for line in open('SMSSpamCollection')]
print(len(messages))
messages[10]
for me... | github_jupyter | #data https://archive.ics.uci.edu/ml/datasets/sms+spam+collection
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import nltk
%matplotlib inline
plt.style.use('ggplot')
nltk.download()
messages = [line.rstrip() for line in open('SMSSpamCollection')]
print(len(messages))
messages[10]
for messag... | 0.483892 | 0.395659 |
submitted by Tarang Ranpara
## Part 1 - training CBOW and Skipgram models
```
# load library gensim (contains word2vec implementation)
import gensim
# ignore some warnings (probably caused by gensim version)
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import multiprocessing
cores... | github_jupyter | # load library gensim (contains word2vec implementation)
import gensim
# ignore some warnings (probably caused by gensim version)
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import multiprocessing
cores = multiprocessing.cpu_count() # Count the number of cores
from tqdm import tqd... | 0.322846 | 0.513363 |
# TensorFlow Basics
Import the library:
```
import tensorflow as tf
print(tf.__version__)
```
### Simple Constants
Let's show how to create a simple constant with Tensorflow, which TF stores as a tensor object:
```
hello = tf.constant('Hello World')
type(hello)
x = tf.constant(100)
type(x)
```
### Running Session... | github_jupyter | import tensorflow as tf
print(tf.__version__)
hello = tf.constant('Hello World')
type(hello)
x = tf.constant(100)
type(x)
sess = tf.Session()
sess.run(hello)
type(sess.run(hello))
sess.run(x)
type(sess.run(x))
x = tf.constant(2)
y = tf.constant(3)
with tf.Session() as sess:
print('Operations with Constants')
... | 0.47171 | 0.979433 |
```
import numpy as np
import pickle
from itertools import chain
from collections import OrderedDict
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import matplotlib.pylab as plt
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
from tor... | github_jupyter | import numpy as np
import pickle
from itertools import chain
from collections import OrderedDict
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import matplotlib.pylab as plt
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.a... | 0.552781 | 0.603873 |
```
import pandas as pd
import numpy as np
import warnings
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from tqdm import tqdm, tqdm_notebook
warnings.filterwarnings('ignore')
# BEWARE, ignoreing warnings is not always a good... | github_jupyter | import pandas as pd
import numpy as np
import warnings
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from tqdm import tqdm, tqdm_notebook
warnings.filterwarnings('ignore')
# BEWARE, ignoreing warnings is not always a good ide... | 0.635109 | 0.79653 |
```
%matplotlib inline
from pathlib import Path
import dask.dataframe as dd
import pandas as pd
YEAR = 2019
slookup = pd.read_csv('ghcn_mos_lookup.csv')
```
# GHCN
```
names = ['ID', 'DATE', 'ELEMENT', 'DATA_VALUE', 'M-FLAG', 'Q-FLAG', 'S-FLAG', 'OBS-TIME']
ds = dd.read_csv(f's3://noaa-ghcn-pds/csv/{YEAR}.csv', st... | github_jupyter | %matplotlib inline
from pathlib import Path
import dask.dataframe as dd
import pandas as pd
YEAR = 2019
slookup = pd.read_csv('ghcn_mos_lookup.csv')
names = ['ID', 'DATE', 'ELEMENT', 'DATA_VALUE', 'M-FLAG', 'Q-FLAG', 'S-FLAG', 'OBS-TIME']
ds = dd.read_csv(f's3://noaa-ghcn-pds/csv/{YEAR}.csv', storage_options={'anon... | 0.346652 | 0.587322 |
This notebook is based off of the [SGD mnist](https://github.com/fastai/fastai/blob/master/courses/ml1/lesson4-mnist_sgd.ipynb) lesson from fastai
In this notebook we will start with a pytorch neural network implementation of logitistic regression and then program it ourselves
# Imports and Paths
```
%load_ext auto... | github_jupyter | %load_ext autoreload
%autoreload 2
%matplotlib inline
from data_sci.imports import *
from data_sci.utilities import *
from data_sci.fastai import *
from data_sci.fastai.dataset import *
from data_sci.fastai.metrics import *
from data_sci.fastai.torch_imports import *
from data_sci.fastai.model import *
import torch.nn... | 0.806777 | 0.987841 |
# Analytical solution of the 1-D diffusion equation
As discussed in class, many physical problems encountered in the field of geosciences can be described with a diffusion equation, i.e. _relating the rate of change in time to the curvature in space_:
$$\frac{\partial u}{\partial t} = \kappa \frac{\partial^2 u}{\part... | github_jupyter | # first some basic Python imports
import matplotlib.pyplot as plt
import numpy as np
import scipy.special
from ipywidgets import interactive
plt.rcParams['figure.figsize'] = [8., 5.]
plt.rcParams['font.size'] = 16
from IPython.display import Audio, display
def plot_steady_state(n=1):
# set number of lines with n
... | 0.470493 | 0.994396 |
# LIBRARIES
```
import matplotlib.pyplot as plt
import pylab
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, roc_auc_score,mean_squared_error,f1_score
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
import seaborn as sb
from sklearn.utils impor... | github_jupyter | import matplotlib.pyplot as plt
import pylab
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, roc_auc_score,mean_squared_error,f1_score
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
import seaborn as sb
from sklearn.utils import resample
from s... | 0.189709 | 0.295395 |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/NER_DEMOGRAPHICS.ipynb)
# **Detect demograph... | github_jupyter | import os
import json
with open('/content/workshop_license_keys.json', 'r') as f:
license_keys = json.load(f)
license_keys.keys()
secret = license_keys['JSL_SECRET']
os.environ['SPARK_NLP_LICENSE'] = license_keys['SPARK_NLP_LICENSE']
os.environ['JSL_OCR_LICENSE'] = license_keys['JSL_OCR_LICENSE']
os.environ['AWS... | 0.439988 | 0.85931 |
# Example of using DOpt Federov Exchange Algorithm
## Algorithm obtained from
- **Algorithm AS 295:** A Fedorov Exchange Algorithm for D-Optimal Design
- **Author(s):** Alan J. Miller and Nam-Ky Nguyen
- **Source:** Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 43, No. 4, pp. 669-677,... | github_jupyter | import numpy as np
import math as m
import dopt
print( dopt.dopt.__doc__ )
# Sample data set
data = [
[ -1, -1, -1 ],
[ 0, -1, -1 ],
[ 1, -1, -1 ],
[ -1, 0, -1 ],
[ 0, 0, -1 ],
[ 1, 0, -1 ],
[ -1, 1, -1 ],
[ 0, 1, -1 ],
[ 1, 1, -1 ],
[ -1, -1, 0 ],
[ 0, -1, 0 ],
[ 1, -1,... | 0.237487 | 0.910306 |
<img align="left" src="https://lever-client-logos.s3.amazonaws.com/864372b1-534c-480e-acd5-9711f850815c-1524247202159.png" width=200>
<br></br>
<br></br>
## *Data Science Unit 4 Sprint 3 Assignment 2*
# Convolutional Neural Networks (CNNs)
# Assignment
- <a href="#p1">Part 1:</a> Pre-Trained Model
- <a href="#p2">Pa... | github_jupyter | import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D()
from tensorflow.keras.models impor... | 0.629205 | 0.941385 |
```
# look at tools/set_up_magics.ipynb
yandex_metrica_allowed = True ; get_ipython().run_cell('# one_liner_str\n\nget_ipython().run_cell_magic(\'javascript\', \'\', \'// setup cpp code highlighting\\nIPython.CodeCell.options_default.highlight_modes["text/x-c++src"] = {\\\'reg\\\':[/^%%cpp/]} ;\')\n\n# creating magics\... | github_jupyter | # look at tools/set_up_magics.ipynb
yandex_metrica_allowed = True ; get_ipython().run_cell('# one_liner_str\n\nget_ipython().run_cell_magic(\'javascript\', \'\', \'// setup cpp code highlighting\\nIPython.CodeCell.options_default.highlight_modes["text/x-c++src"] = {\\\'reg\\\':[/^%%cpp/]} ;\')\n\n# creating magics\nfro... | 0.133331 | 0.227491 |
# Feature Engineering and Creation
#### v 2.0
In this feature engineering pipeline, the focus will be to try to improve the result for XGBoost model.
## Imports and Setup
```
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
from collections import Cou... | github_jupyter | import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
from collections import Counter
from sklearn.decomposition import TruncatedSVD, PCA
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
sns.s... | 0.39712 | 0.921852 |
```
# Imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from statsmodels.tsa.arima.model import ARIMA
sns.set_theme()
# Load data (city)
df = pd.read_csv('../data/GlobalLandTemperaturesByCity.csv')
df['dt'] = pd.DatetimeIndex(df['dt'])
df['Year'] = pd.DatetimeIndex(df[... | github_jupyter | # Imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from statsmodels.tsa.arima.model import ARIMA
sns.set_theme()
# Load data (city)
df = pd.read_csv('../data/GlobalLandTemperaturesByCity.csv')
df['dt'] = pd.DatetimeIndex(df['dt'])
df['Year'] = pd.DatetimeIndex(df['dt'... | 0.675551 | 0.712945 |
# WeatherPy
----
#### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
from scipy.s... | github_jupyter | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
from scipy.stats import linregress
# Import API key
from api_keys import weather_api_key
# Incorporated citipy to determine city based on latitude and longitude
from citipy import citipy
# Outp... | 0.386185 | 0.830113 |
# Direct Marketing with Amazon SageMaker Autopilot
---
---
## Contents
1. [Introduction](#Introduction)
1. [Prerequisites](#Prerequisites)
1. [Downloading the dataset](#Downloading)
1. [Upload the dataset to Amazon S3](#Uploading)
1. [Setting up the SageMaker Autopilot Job](#Settingup)
1. [Launching the SageMaker Au... | github_jupyter | import sagemaker
import boto3
from sagemaker import get_execution_role
region = boto3.Session().region_name
session = sagemaker.Session()
bucket = session.default_bucket()
prefix = 'sagemaker/autopilot-dm'
role = get_execution_role()
sm = boto3.Session().client(service_name='sagemaker',region_name=region)
!wget -N... | 0.206174 | 0.98679 |
## 캐글 데이터셋 링크
+ original: https://www.kaggle.com/trolukovich/apparel-images-dataset
+ me: https://www.kaggle.com/airplane2230/apparel-image-dataset-2
```
import numpy as np
import pandas as pd
import tensorflow as tf
import glob as glob
import cv2
all_data = np.array(glob.glob('./clothes_dataset/*/*.jpg', recursive=... | github_jupyter | import numpy as np
import pandas as pd
import tensorflow as tf
import glob as glob
import cv2
all_data = np.array(glob.glob('./clothes_dataset/*/*.jpg', recursive=True))
# 색과 옷의 종류를 구별하기 위해 해당되는 label에 1을 삽입합니다.
def check_cc(color, clothes):
labels = np.zeros(11,)
# color check
if(color == 'black'):
... | 0.183484 | 0.79736 |
```
from PIL import Image
img = Image.open("cat.jpg")
# 아래에 명시한 위치들을 기반으로 짤려서 나오게 된다.
# (0, 0)이 좌측 상단임을 기억하도록 하자!
dim = (0, 0, 400, 400)
crop_img = img.crop(dim)
crop_img.show()
from PIL import Image
img = Image.open("cat.jpg")
# Image에는 Color Space 라는 공간이 있는데
# 이 공간에서 Color 값들을 빼고 조도로만
# 이미지를 재구성하면 GrayScale이 된다.... | github_jupyter | from PIL import Image
img = Image.open("cat.jpg")
# 아래에 명시한 위치들을 기반으로 짤려서 나오게 된다.
# (0, 0)이 좌측 상단임을 기억하도록 하자!
dim = (0, 0, 400, 400)
crop_img = img.crop(dim)
crop_img.show()
from PIL import Image
img = Image.open("cat.jpg")
# Image에는 Color Space 라는 공간이 있는데
# 이 공간에서 Color 값들을 빼고 조도로만
# 이미지를 재구성하면 GrayScale이 된다.
# 조... | 0.328637 | 0.663083 |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
accuracy = [
['MobileNetV2', 1, 0.9890, 0.3676],
['MobileNetV2', 2, 0.9916, 0.5084],
['MobileNetV2', 3, 0.9926, 0.4996],
['MobileNetV2', 4, 0.9939, 0.7712],
['MobileNe... | github_jupyter | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
accuracy = [
['MobileNetV2', 1, 0.9890, 0.3676],
['MobileNetV2', 2, 0.9916, 0.5084],
['MobileNetV2', 3, 0.9926, 0.4996],
['MobileNetV2', 4, 0.9939, 0.7712],
['MobileNetV2'... | 0.122327 | 0.68911 |
# Check `GDS` Python stack
This notebook checks all software requirements for the course Geographic Data Science are correctly installed.
A successful run of the notebook implies no errors returned in any cell *and* every cell beyond the first one returning a printout of `True`. This ensures a correct environment in... | github_jupyter | import black
import bokeh
import cenpy
import colorama
import contextily
import cython
import dask
import dask_ml
import datashader
import dill
import geopandas
import geopy
import hdbscan
import ipyleaflet
import ipyparallel
import ipywidgets
import mplleaflet
import nbdime
import networkx
import osmnx
import palettab... | 0.458106 | 0.826046 |
# Covid-19 Pandemic Analysis on April 3rd
## Import Data
The dataset can be download at [Kaggle](https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset).
```
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRe... | github_jupyter | import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import re
import math
%matplotlib inline
# Explore the d... | 0.720368 | 0.865963 |
# Chapter 5: Point-Neuron Network Models (with PointNet)
In this chapter we will create a heterogeneous network of point-model neurons and use the PointNet simulator which will run the network using the NEST simulator. As with the previous BioNet examples will create both a internal recurrently-connected network of di... | github_jupyter | import pandas as pd
pd.read_csv('sources/chapter05/converted_network/V1_node_types_bionet.csv', sep=' ')
pd.read_csv('sources/chapter05/converted_network/V1_node_types.csv', sep=' ')
pd.read_csv('sources/chapter05/converted_network/V1_V1_edge_types.csv', sep=' ')
from bmtk.builder.networks import NetworkBuilder
fro... | 0.462473 | 0.958382 |
# Research Project #
## Introduction ##
This data set relates to academic performance in students grade 1-12. This dataset was collected in 2016 using a "learner activity tracking" tool (xADI) from a learning management system (LMS). It was collected over the course of two academic semesters. Data was collected for 4... | github_jupyter | import sys, os
sys.path.insert(0, os.path.abspath('..'))
from scripts.project_functions import *
from scripts import project_functions_anamica
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
df = project_functions_anamica.load_process_data("../../data/data_raw/part_data.csv")
df
sns.boxplot(... | 0.165965 | 0.983971 |
```
import torch
torch.backends.cudnn.benchmark = True
from torch import nn
from torch.nn.functional import softmax, log_softmax
from torchmetrics import Accuracy
from resnet_cifar import resnet32
import pytorch_lightning as pl
import wandb
import torchvision.transforms as T
import sys
sys.path.append('../')
fr... | github_jupyter | import torch
torch.backends.cudnn.benchmark = True
from torch import nn
from torch.nn.functional import softmax, log_softmax
from torchmetrics import Accuracy
from resnet_cifar import resnet32
import pytorch_lightning as pl
import wandb
import torchvision.transforms as T
import sys
sys.path.append('../')
from d... | 0.809427 | 0.715064 |
# IAPWS-IF97 Libraries
## 1 Introduction to IAPWS-IF97
http://www.iapws.org/relguide/IF97-Rev.html
This formulation is recommended for industrial use (primarily the steam power industry) for the calculation of thermodynamic properties of ordinary water in its fluid phases, including vapor-liquid equilibrium.
The ... | github_jupyter | python -m pip install iapws
from iapws import IAPWS97
sat_steam=IAPWS97(P=1,x=1) # saturated steam with known P,x=1
sat_liquid=IAPWS97(T=370, x=0) #saturated liquid with known T,x=0
steam=IAPWS97(P=2.5, T=500) # steam with known P and T(K)
print(sat_steam.h, sat_liquid.h, steam.... | 0.388502 | 0.885532 |
```
import os
import sys
import collections
import csv
import pandas as pd
import numpy as np
import tensorflow as tf
import pandas as pd
import numpy as np
import time
from pymongo import MongoClient
import urllib
import multiprocess
import pickle
import random
# BERT files
os.listdir("../bert-master")
sys.path.inser... | github_jupyter | import os
import sys
import collections
import csv
import pandas as pd
import numpy as np
import tensorflow as tf
import pandas as pd
import numpy as np
import time
from pymongo import MongoClient
import urllib
import multiprocess
import pickle
import random
# BERT files
os.listdir("../bert-master")
sys.path.insert(0,... | 0.237664 | 0.113334 |
# Preparação
Carregar as biliotecas e ler os caminhos de ENV.
```
# Import Libraries
import os
import pandas as pd
import joblib
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection im... | github_jupyter | # Import Libraries
import os
import pandas as pd
import joblib
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_... | 0.58166 | 0.775562 |
Data Manipulation in Pandas
1. Renaming columns
2. Sorting Data
3. Binning
4. Handling missing values
5. Apply methods in Pandas
6. Aggregation of Data Using Pandas
7. Merging data using Pandas
Column Names are called as labels..
```
import numpy as np
import pandas as pd
data={'Title':[None, 'Robinson Crusoe'... | github_jupyter | import numpy as np
import pandas as pd
data={'Title':[None, 'Robinson Crusoe', 'Moby Dick'],
'Author':['sa', 'Daniel Defoe', 'Herman Melville']}
df = pd.DataFrame(data)
df.dropna(how='all', inplace= True)
df
df
df.columns=['TITLE', 'AUTHOR'] #Changing the keys of the column
print(df)
# other method of re... | 0.254694 | 0.902136 |
# Should we remove top-level switches?
```
import io
import zipfile
import os
import pandas
from plotnine import *
import plotnine
plotnine.options.figure_size = (12, 8)
import yaml
from lxml import etree
import warnings
import re
warnings.simplefilter(action='ignore')
def get_yaml(archive_name, yaml_name):
arch... | github_jupyter | import io
import zipfile
import os
import pandas
from plotnine import *
import plotnine
plotnine.options.figure_size = (12, 8)
import yaml
from lxml import etree
import warnings
import re
warnings.simplefilter(action='ignore')
def get_yaml(archive_name, yaml_name):
archive = zipfile.ZipFile(archive_name)
retu... | 0.282889 | 0.584983 |
Plot Tide Forecasts
===================
Plots the daily tidal displacements for a given location
OTIS format tidal solutions provided by Ohio State University and ESR
- http://volkov.oce.orst.edu/tides/region.html
- https://www.esr.org/research/polar-tide-models/list-of-polar-tide-models/
- ftp://ftp.esr.org/pub/... | github_jupyter | pip3 install --user ipywidgets
jupyter nbextension install --user --py widgetsnbextension
jupyter nbextension enable --user --py widgetsnbextension
jupyter-notebook
from __future__ import print_function
import os
import datetime
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widgets
import ip... | 0.70619 | 0.867822 |
# 1) Defining how we assess performance
## What do we mean by "loss"?
<img src="images/lec3_pic01.png">
<img src="images/lec3_pic02.png">
*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-regression/lecture/cGUQ3/what-do-we-mean-by-loss) 1:00*
<!--TEASER_END-->
How do we formalize this notion of ... | github_jupyter | # 1) Defining how we assess performance
## What do we mean by "loss"?
<img src="images/lec3_pic01.png">
<img src="images/lec3_pic02.png">
*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-regression/lecture/cGUQ3/what-do-we-mean-by-loss) 1:00*
<!--TEASER_END-->
How do we formalize this notion of ... | 0.788461 | 0.985732 |
# Stochastic Gradient Langevin Dynamics in MXNet
```
%matplotlib inline
```
In this notebook, we will show how to replicate the toy example in the paper <a name="ref-1"/>[(Welling and Teh, 2011)](#cite-welling2011bayesian). Here we have observed 20 instances from a mixture of Gaussians with tied means:
$$
\begin{alig... | github_jupyter | %matplotlib inline
import mxnet as mx
import mxnet.ndarray as nd
import numpy
import logging
import time
import matplotlib.pyplot as plt
def load_synthetic(theta1, theta2, sigmax, num=20):
flag = numpy.random.randint(0, 2, (num,))
X = flag * numpy.random.normal(theta1, sigmax, (num, )) \
... | 0.569374 | 0.973494 |
<a href="https://colab.research.google.com/github/sahooamarjeet/ML_Case_Study/blob/master/Customer_Analytics.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
```
Mount Gdrive... | github_jupyter | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
import os;os.listdir("/content/gdrive/My Drive/Colab Notebooks")
df = pd.read_csv("/content/gdrive/My Drive/Colab Notebooks/WA_Fn-UseC_-Marketing-Customer-Value-Analy... | 0.432543 | 0.933794 |
## Introduction
I was thinking for a while about my master thesis topic and I wanted it was related to data mining and artificial intelligence because I want to learn more about this field. I want to work with machine learning and for that, I have to study it more. Writing a thesis is a great way to get more experienc... | github_jupyter | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import export_graphviz
from sklearn.metrics import classification_report, confusion_matrix, ac... | 0.560493 | 0.981524 |
```
from datascience import *
path_data = '../data/'
import matplotlib
matplotlib.use('Agg')
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
import numpy as np
```
# Iteration
It is often the case in programming – especially when dealing with randomness – that we want to repeat ... | github_jupyter | from datascience import *
path_data = '../data/'
import matplotlib
matplotlib.use('Agg')
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
import numpy as np
def bet_on_one_roll():
"""Returns my net gain on one bet"""
x = np.random.choice(np.arange(1, 7)) # roll a die onc... | 0.468547 | 0.977392 |
# TIME EVOLUTION OF THE EFT COUNTER-TERMS
```
import numpy as np
from scipy.interpolate import interp1d,InterpolatedUnivariateSpline
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('default')
```
## Quijote simulations
```
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=67.11, O... | github_jupyter | import numpy as np
from scipy.interpolate import interp1d,InterpolatedUnivariateSpline
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('default')
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=67.11, Ob0=0.049, Om0= 0.2685)
z = np.array([0,0.5,1,2,3])
c2 = np.array([2.629, 0.977... | 0.669745 | 0.886617 |
# CNTK 208: Training Acoustic Model with Connectionist Temporal Classification (CTC) Criteria
This tutorial assumes familiarity with 10\* CNTK tutorials and basic knowledge of data representation in acoustic modelling tasks. It introduces some CNTK building blocks that can be used in training deep networks for speech r... | github_jupyter | import os
import cntk as C
import numpy as np
# Select the right target device
import cntk.tests.test_utils
cntk.tests.test_utils.set_device_from_pytest_env() # (only needed for our build system)
data_dir = os.path.join("..", "Tests", "EndToEndTests", "Speech", "Data")
print("Current directory {0}".format(os.getcwd()... | 0.460046 | 0.934574 |
# 범주형 데이터 처리
범주형 데이터 Categorical Data
* 명목형 자료(nominal data)
* 숫자로 바꾸어도 그 값이 크고 작음을 나타내는 것이 아니라 단순히 범주를 표시
* 예) 성별(주민번호), 혈액형
* 순서형 자료(ordinal data)
* 범주의 순서가 상대적으로 비교 가능,
* 예) 비만도(저체중, 정상, 과체중, 비만, 고도비만), 학점,선호도
* 대부분 수치형 자료를 그룹화 하여 순서형 자료로 바꿀수 있다.
```
import pandas as pd
```
### 샘플데이터
```
df... | github_jupyter | import pandas as pd
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['A', 'B', 'B', 'A', 'A', 'F']})
df
df["grade"] = df["raw_grade"].astype("category")
df
df.info()
df["grade"].cat.categories
df["grade"].cat.categories = ["very good", "good", "very bad"]
df
df["grade"]
df["grade"] = df["grade"].cat.set_categori... | 0.191214 | 0.967899 |
# Genotype data formatting
This module implements a collection of workflows used to format genotype data.
## Overview
The module streamlines conversion between PLINK and VCF formats (possibly more to add), specifically:
1. Conversion between VCF and PLINK formats
2. Split data (by specified input, by chromosomes, b... | github_jupyter | sos run genotype_formatting.ipynb merge_plink \
--genoFile data/genotype/chr1.bed data/genotype/chr6.bed \
--cwd output/genotype \
--name chr1_chr6 \
--container container/bioinfo.sif
```
## Command interface
## PLINK to VCF
## VCF to PLINK
## Split PLINK by genes
## Split PLINK by Chromos... | 0.332527 | 0.8474 |
---
_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-text-mining/resources/d9pwm) course resource._
---
# Assignment 1
In this... | github_jupyter | import pandas as pd
import numpy as np
doc = []
with open('dates.txt') as file:
for line in file:
doc.append(line)
df = pd.Series(doc)
df.head(10)
# df.shape
def date_sorter():
# Extract dates
df_dates = df.str.replace(r'(\d+\.\d+)', '')
df_dates = df_dates.str.extractall(r'[\s\.,\-/]*?(?P<ddm... | 0.31216 | 0.909867 |
# Realization of Recursive Filters
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Quantization of Variables and Operations
A... | github_jupyter | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig
N = 8192 # length of signals
w = 8 # wordlength for requantization of multiplications
def uniform_midtread_quantizer(x):
# linear uniform quantization
xQ = Q * np.floor(x/Q + 1/2)
return xQ
def no_q... | 0.620737 | 0.99406 |
```
%matplotlib inline
```
PyTorch: 새 autograd Function 정의하기
----------------------------------------
$y=\sin(x)$ 을 예측할 수 있도록, $-\pi$ 부터 $pi$ 까지
유클리드 거리(Euclidean distance)를 최소화하도록 3차 다항식을 학습합니다.
다항식을 $y=a+bx+cx^2+dx^3$ 라고 쓰는 대신 $y=a+b P_3(c+dx)$ 로 다항식을 적겠습니다.
여기서 $P_3(x)= rac{1}{2}\left(5x^3-3x
ight)$ 은 3차
`르장드르 다항... | github_jupyter | %matplotlib inline
import torch
import math
class LegendrePolynomial3(torch.autograd.Function):
"""
torch.autograd.Function을 상속받아 사용자 정의 autograd Function을 구현하고,
텐서 연산을 하는 순전파 단계와 역전파 단계를 구현해보겠습니다.
"""
@staticmethod
def forward(ctx, input):
"""
순전파 단계에서는 입력을 갖는 텐서를 받아 출력을 갖는 ... | 0.744935 | 0.964656 |
# Proyecto de Investigación: Modelos Numéricos
---
```
import ipywidgets as widgets
from matplotlib import pyplot as plt
import numpy as np
from numpy import linalg as LA
from math import sqrt, pi
from project import eigen
```
## Modelo Matemático en Ecuaciones Diferenciales
$ m(x) \frac{\partial^{2}u}{\partial t^{... | github_jupyter | import ipywidgets as widgets
from matplotlib import pyplot as plt
import numpy as np
from numpy import linalg as LA
from math import sqrt, pi
from project import eigen
L = 420
EJ = 9.45e11
m = 64.8
p = 100
ML = 50000
widgetN = widgets.FloatText()
display(widgetN)
N = 20
h = L/N
M = np.zeros((N, N))
for i in range(... | 0.378 | 0.983534 |
# Chunking using RNN and also Bi-LSTM
## Importing required Libraries
```
# Let us import required Libraries
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.backend as K
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequ... | github_jupyter | # Let us import required Libraries
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.backend as K
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import numpy as np
from gensim.models ... | 0.469034 | 0.832781 |
# DS1000E Rigol Waveform Examples
**Scott Prahl**
**March 2021**
This notebook illustrates shows how to extract signals from a `.wfm` file created by a the Rigol DS1000E scope. It also validates that the process works by comparing with `.csv` and screenshots.
Two different `.wfm` files are examined one for the DS1... | github_jupyter | #!pip install RigolWFM
import numpy as np
import matplotlib.pyplot as plt
try:
import RigolWFM.wfm as rigol
except ModuleNotFoundError:
print('RigolWFM not installed. To install, uncomment and run the cell below.')
print('Once installation is successful, rerun this cell again.')
repo = "https://github.com... | 0.450601 | 0.951414 |
# PostgreSQL: use sql magic %sql
* pip install ipython-sql
* doc: https://pypi.org/project/ipython-sql/
---
* author: [Prasert Kanawattanachai](prasert.k@chula.ac.th)
* YouTube: https://www.youtube.com/prasertcbs
* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)
---
```
from IPython.display import Y... | github_jupyter | from IPython.display import YouTubeVideo
YouTubeVideo('bgHPGiE0rkg', width=720, height=405)
import pandas as pd
import psycopg2 # postgresql db driver
print(f'pandas version: {pd.__version__}')
print(f'psycopg2 version: {psycopg2.__version__}')
%load_ext sql
import getpass
host='192.168.211.137'
port=5432
dbname='... | 0.18665 | 0.66689 |
<a href="https://colab.research.google.com/github/tuanyuan2008/cs4641/blob/master/randomized-optimization/4_peaks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip3 install mlrose
import mlrose
import numpy as np
import matplotlib.pyplot as... | github_jupyter | ! pip3 install mlrose
import mlrose
import numpy as np
import matplotlib.pyplot as plt
import timeit
fitness = mlrose.FourPeaks(t_pct=0.15)
state = np.array([1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0])
fitness.evaluate(state)
# Define decay schedules
schedule = mlrose.GeomDecay()
problem = mlrose.DiscreteOpt(length = 12, f... | 0.340156 | 0.888081 |
# Introduction à la librairie PyTorch -- Solutions
Matériel de cours rédigé par Pascal Germain, 2019
************
### Partie 1
```
class regression_logistique:
def __init__(self, rho=.01, eta=0.4, nb_iter=50, seed=None):
# Initialisation des paramètres de la descente en gradient
self.rho = rho ... | github_jupyter | class regression_logistique:
def __init__(self, rho=.01, eta=0.4, nb_iter=50, seed=None):
# Initialisation des paramètres de la descente en gradient
self.rho = rho # Paramètre de regularisation
self.eta = eta # Pas de gradient
self.nb_iter = nb_iter # Nombre d'itérati... | 0.817574 | 0.864024 |
# Classifying Fashion-MNIST
Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 9... | github_jupyter | import torch
from torchvision import datasets, transforms
import helper
# 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))])
# Download and load the training data
trainset = datasets.Fa... | 0.652241 | 0.991456 |
```
import wandb
wandb.init(project="Channel_Charting")
import torch
from torch import nn
from torch.optim import SGD
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision.transforms import Compose, ToTensor, Normalize
from torchvision.datasets import MNIST
from ignite.engine impor... | github_jupyter | import wandb
wandb.init(project="Channel_Charting")
import torch
from torch import nn
from torch.optim import SGD
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision.transforms import Compose, ToTensor, Normalize
from torchvision.datasets import MNIST
from ignite.engine import Ev... | 0.87464 | 0.586227 |
# Spatial joins
Goals of this notebook:
- Based on the `countries` and `cities` dataframes, determine for each city the country in which it is located.
- To solve this problem, we will use the the concept of a 'spatial join' operation: combining information of geospatial datasets based on their spatial relationship.
... | github_jupyter | %matplotlib inline
import pandas as pd
import geopandas
pd.options.display.max_rows = 10
countries = geopandas.read_file("zip://./data/ne_110m_admin_0_countries.zip")
cities = geopandas.read_file("zip://./data/ne_110m_populated_places.zip")
rivers = geopandas.read_file("zip://./data/ne_50m_rivers_lake_centerlines.zip... | 0.330795 | 0.987067 |
# Unit 5 - Financial Planning
```
# Initial imports
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
from MCForecastTools import MCSimulation
import datetime
import json
%matplotlib inline
# Load .env enviroment variables
load_dotenv()
```
## Part 1 - Pe... | github_jupyter | # Initial imports
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
from MCForecastTools import MCSimulation
import datetime
import json
%matplotlib inline
# Load .env enviroment variables
load_dotenv()
# Set current amount of crypto assets
my_btc = 1.2
my... | 0.642545 | 0.691341 |
<a href="https://colab.research.google.com/github/DiploDatos/AprendizajePorRefuerzos/blob/master/lab_1_intro_rl.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Notebook 1: Introducción al aprendizaje por refuerzos
Curso Aprendizaje por Refuerzos,... | github_jupyter |
desde Mac, reemplazar *apt-get* por *brew*
desde Windows, descargarla desde
[https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)
(Nota: las animaciones son a modo ilustrativo, si no se desea instalar la librería se puede directamente eliminar la línea de código donde se llama al método ``env.render(... | 0.47658 | 0.990169 |
## Python Data Structures Exercises
```
mydata = [{'Born': '2007',
'City': 'Cauneside',
'Crypto': ('FTH', 'Feathercoin'),
'Description': 'Natus voluptas repellat consequatur. Nihil nobis reprehenderit libero sunt nulla.\nVeniam quia ab consectetur voluptatibus reprehenderit debitis sint.',
'Email': 'kaspars94@... | github_jupyter | mydata = [{'Born': '2007',
'City': 'Cauneside',
'Crypto': ('FTH', 'Feathercoin'),
'Description': 'Natus voluptas repellat consequatur. Nihil nobis reprehenderit libero sunt nulla.\nVeniam quia ab consectetur voluptatibus reprehenderit debitis sint.',
'Email': 'kaspars94@lacis-krievins.biz',
'FavoriteURL': 'ht... | 0.2763 | 0.57069 |
```
import pandas as pd
train = pd.read_csv('train.csv', index_col='_id')
test = pd.read_csv('test.csv', index_col='_id')
train.info(), test.info()
train.shape, test.shape
y_train = list(train['target'])
train = train.drop('target', axis=1)
train.info(verbose=True)
train.loc[:,'sample'] = 'train'
test.loc[:,'sample'] =... | github_jupyter | import pandas as pd
train = pd.read_csv('train.csv', index_col='_id')
test = pd.read_csv('test.csv', index_col='_id')
train.info(), test.info()
train.shape, test.shape
y_train = list(train['target'])
train = train.drop('target', axis=1)
train.info(verbose=True)
train.loc[:,'sample'] = 'train'
test.loc[:,'sample'] = 'te... | 0.192046 | 0.224013 |
```
#library
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.applications as ap
#mount file from google drive
from google.colab import drive
drive.mount('/content/drive')
#grab the data
img512 = np.l... | github_jupyter | #library
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.applications as ap
#mount file from google drive
from google.colab import drive
drive.mount('/content/drive')
#grab the data
img512 = np.load(... | 0.558809 | 0.123551 |
# Population Tool: Alpha
## First Step: Define functions we need
Import necessary packages and declare constant variables
```
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
# Use these paths to pull real data
POP_DATA_PATH = 'https://esa.un.org/unpd/wpp/DVD/File... | github_jupyter | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
# Use these paths to pull real data
POP_DATA_PATH = 'https://esa.un.org/unpd/wpp/DVD/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2017_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx'
POP_RELATABLE_PATH = 'ht... | 0.457379 | 0.8474 |
# Inspecting TFX metadata
## Learning Objectives
1. Use a GRPC server to access and analyze pipeline artifacts stored in the ML Metadata service of your AI Platform Pipelines instance.
In this lab, you will explore TFX pipeline metadata including pipeline and run artifacts. A hosted **AI Platform Pipelines** instan... | github_jupyter | import os
import ml_metadata
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
from ml_metadata.metadata_store import metadata_store
from ml_metadata.proto import metadata_store_pb2
from tfx.orchestration import metadata
from tfx.types import standard_artifacts
!python -c "import tf... | 0.281801 | 0.90764 |
### 基于维特比算法来优化上述流程
此项目需要的数据：
1. 综合类中文词库.xlsx： 包含了中文词，当做词典来用
2. 以变量的方式提供了部分unigram概率word_prob
举个例子： 给定词典=[我们 学习 人工 智能 人工智能 未来 是]， 另外我们给定unigram概率：p(我们)=0.25, p(学习)=0.15, p(人工)=0.05, p(智能)=0.1, p(人工智能)=0.2, p(未来)=0.1, p(是)=0.15
#### Step 1: 根据词典，输入的句子和 word_prob来创建带权重的有向图（Directed Graph） 参考：课程内容
有向图的每一条边是一个单词的概率（只要存在... | github_jupyter | import pandas as pd
import numpy as np
path = "./data/综合类中文词库.xlsx"
data_frame = pd.read_excel(path, header = None)
dic_word_list = data_frame[data_frame.columns[0]].tolist()
dic_words = dic_word_list # 保存词典库中读取的单词
# 以下是每一个单词出现的概率。为了问题的简化，我们只列出了一小部分单词的概率。 在这里没有出现的的单词但是出现在词典里的，统一把概率设置成为0.00001
# 比如 p("学院")=p("概率")=.... | 0.138084 | 0.827131 |
```
# Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Author(s): Kevin P. Murphy (murphyk@gmail.com) and Mahmoud Soliman (mjs@aucegypt.edu)
```
<a href="https://opensource.org/licenses/MIT" t... | github_jupyter | # Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Author(s): Kevin P. Murphy (murphyk@gmail.com) and Mahmoud Soliman (mjs@aucegypt.edu)
#@title Setup { display-mode: "form" }
%%time
# If you... | 0.666171 | 0.913097 |
<a href="https://colab.research.google.com/github/simecek/ECCB2021/blob/main/notebooks/10_Integrated_Gradients_G4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Data
```
import tensorflow as tf
from tensorflow.keras import Sequential
from tenso... | github_jupyter | import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv1D, BatchNormalization, MaxPooling1D, Dropout, GlobalAveragePooling1D, Dense
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display, HTML
# get train dataset
!wget... | 0.918713 | 0.936285 |
```
import pandemic_simulator as ps
import random
from tf_agents.specs import BoundedArraySpec
import numpy as np
import base64
import IPython
import matplotlib.pyplot as plt
import os
import reverb
import tempfile
import tensorflow as tf
from tf_agents.agents.ddpg import critic_network
from tf_agents.agents.sac imp... | github_jupyter | import pandemic_simulator as ps
import random
from tf_agents.specs import BoundedArraySpec
import numpy as np
import base64
import IPython
import matplotlib.pyplot as plt
import os
import reverb
import tempfile
import tensorflow as tf
from tf_agents.agents.ddpg import critic_network
from tf_agents.agents.sac import ... | 0.405096 | 0.203075 |
# Computational Astrophysics
## Fundamentals of Visualization
---
## Eduard Larrañaga
Observatorio Astronómico Nacional\
Facultad de Ciencias\
Universidad Nacional de Colombia
---
### About this notebook
In this notebook we present some of the fundamentals of visualization using `python`.
---
### Simple Data Plo... | github_jupyter | import numpy as np
from matplotlib import pyplot as plt
data = np.loadtxt('plotdata.txt', comments='#')
x = data[:,0]
y = data[:,1]
plt.plot(x,y)
plt.show()
plt.plot(x, y, label=r'first curve label')
plt.xlabel(r'$x$ axis label')
plt.ylabel(r'$y$ axis label')
plt.legend()
plt.show()
plt.plot(x, y, '--r', label=r'first... | 0.591369 | 0.975969 |
```
import dgl.nn as dglnn
from dgl import from_networkx
import torch.nn as nn
import torch as th
import torch.nn.functional as F
import dgl.function as fn
import networkx as nx
import pandas as pd
import socket
import struct
import random
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import... | github_jupyter | import dgl.nn as dglnn
from dgl import from_networkx
import torch.nn as nn
import torch as th
import torch.nn.functional as F
import dgl.function as fn
import networkx as nx
import pandas as pd
import socket
import struct
import random
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Sta... | 0.76986 | 0.376795 |
# HW04: Sentiment Analysis
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.model import fit
from fastai.dataset import *
import torchtext
from torchtext import vocab, data
from torchtext.datasets import language_modeling
from fastai.rnn_reg import *
from fastai.rnn_train import *
from fastai... | github_jupyter | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.model import fit
from fastai.dataset import *
import torchtext
from torchtext import vocab, data
from torchtext.datasets import language_modeling
from fastai.rnn_reg import *
from fastai.rnn_train import *
from fastai.nlp import *
from fastai.lm_rnn... | 0.456894 | 0.594698 |
# Exploring Raw Data with _ctapipe_
Here are just some very simplistic examples of going through and inspecting the raw data, using only the very simple pieces that are implemented right now.
```
# some setup (need to import the things we will use later)
from ctapipe.utils.datasets import get_path
from ctapipe.io.hes... | github_jupyter | # some setup (need to import the things we will use later)
from ctapipe.utils.datasets import get_path
from ctapipe.io.hessio import hessio_event_source
from ctapipe import visualization, io
from matplotlib import pyplot as plt
from astropy import units as u
%matplotlib inline
source = hessio_event_source(get_path("ga... | 0.460774 | 0.976152 |
```
%matplotlib inline
```
# L1 Penalty and Sparsity in Logistic Regression
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1, L2 and Elastic-Net penalty are used for different values of C. We can see
that large values of C give more freedom to the model. Conversely, smaller
values ... | github_jupyter | %matplotlib inline
print(__doc__)
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRe... | 0.692018 | 0.915658 |
# Aprendizado de máquina - Parte 1
_Aprendizado de máquina_ (_machine learning_, ML) é um subcampo da inteligência artificial que tem por objetivo permitir que o computador _aprenda com os dados_ sem ser explicitamente programado. Em linhas gerais, no _machine learning_ se constrói algoritmos que leem dados, aprendem ... | github_jupyter |
## Estudo de caso: classificação de empréstimos bancários
O problema que estudaremos consiste em predizer se o pedido de empréstimo de uma pessoa será parcial ou totalmente aprovado por uma financeira. O banco de dados disponível da financeira abrange os anos de 2007 a 2011.
A aprovação do pedido baseia-se em uma an... | 0.603348 | 0.945601 |
Videocvičení naleznete zde: https://youtu.be/yL-A0N5JDJo
# Práce s obrázky
### Načítání balíčků
K práci s obrázky budeme používat knihovnu **cv2** s aliasem **cv**. Dále budeme používat knihovnu **numpy** s aliasem **np** pro matematické funkce a práci s poli a knihovnu **matplotlib** s aliasem **plt** pro vykreslová... | github_jupyter | import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import NoNorm
%matplotlib notebook
img_bgr = cv.imread("lena_original.jpg",cv.IMREAD_UNCHANGED)
img = cv.cvtColor(img_bgr, cv.COLOR_BGR2RGB)
plt.figure()
plt.imshow(img)
img_crop = img[20:270,150:400,:]
plt.figure()
plt... | 0.407569 | 0.9434 |
# Lab Notebook
Course: BioE 131
Lab No: Lab #7
Submission date:
Team members: Michael Fernandez, Jinho Ko
## Simulating the Data
```
import numpy as np
def b_generator(s, p):
data = np.random.choice( [0,1], size = s, replace = True, p = [p, 1.0-p])
data = np.packbits(data)
return ... | github_jupyter | import numpy as np
def b_generator(s, p):
data = np.random.choice( [0,1], size = s, replace = True, p = [p, 1.0-p])
data = np.packbits(data)
return data
def DNA_generator(s):
data = np.random.choice( ['A', 'T', 'C', 'G'], size = s, replace = True, p = [ 1.0/4.0 for _ in range(4) ] )
#data = np.pa... | 0.273477 | 0.786356 |
# Train faster, more flexible models with Amazon SageMaker Linear Learner
Today Amazon SageMaker is launching several additional features to the built-in linear learner algorithm. Amazon SageMaker algorithms are designed to scale effortlessly to massive datasets and take advantage of the latest hardware optimizations... | github_jupyter | import boto3
import io
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sagemaker
import sagemaker.amazon.common as smac
from sagemaker import get_execution_role
from sagemaker.predictor import csv_serializer, json_deserializer
# Set data locations
bucket = '<your_s3_bucket_her... | 0.534127 | 0.993704 |
```
from pyvis.network import Network
import networkx as nx
import json
import functools
import itertools
import collections
from matplotlib import pyplot as plt
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
# utility functions
def none_max(a, b):
if a is None:
return b
if b is None:... | github_jupyter | from pyvis.network import Network
import networkx as nx
import json
import functools
import itertools
import collections
from matplotlib import pyplot as plt
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
# utility functions
def none_max(a, b):
if a is None:
return b
if b is None:
... | 0.740456 | 0.567757 |
# RMSProp
我们在[“Adagrad”](adagrad.md)一节里提到，由于调整学习率时分母上的变量 $\boldsymbol{s}_t$ 一直在累加按元素平方的小批量随机梯度，目标函数自变量每个元素的学习率在迭代过程中一直在降低（或不变）。所以，当学习率在迭代早期降得较快且当前解依然不佳时，Adagrad 在迭代后期由于学习率过小，可能较难找到一个有用的解。为了应对这一问题，RMSProp 算法对 Adagrad 做了一点小小的修改 [1]。
## 算法
我们在[“动量法”](momentum.md)一节里介绍过指数加权移动平均。不同于 Adagrad 里状态变量 $\boldsymbol{s}_t$ 是截至时间... | github_jupyter | %matplotlib inline
import d2lzh as d2l
import math
from mxnet import nd
def rmsprop_2d(x1, x2, s1, s2):
g1, g2, eps = 0.2 * x1, 4 * x2, 1e-6
s1 = gamma * s1 + (1 - gamma) * g1 ** 2
s2 = gamma * s2 + (1 - gamma) * g2 ** 2
x1 -= eta / math.sqrt(s1 + eps) * g1
x2 -= eta / math.sqrt(s2 + eps) * g2
... | 0.493897 | 0.982906 |
**Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x**
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.nlp import *
from sklearn.linear_model import LogisticRegression
from sklearn.... | github_jupyter | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.nlp import *
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from torchtext import vocab, data, datasets
import pandas as pd
sl=1000
vocab_size=200000
PATH='data/arxiv/arxiv.csv'
# You can download a similar to J... | 0.653016 | 0.761428 |
<a href="https://colab.research.google.com/github/keithvtls/Numerical-Method-Activities/blob/main/Week%203-5%20-%20Roots%20of%20Equations/NuMeth_Group_4_Act_3Roots_of_Linear_Equation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### CONTRIBUTION
... | github_jupyter | ### Brute force algorithm(f(x)=0)
def f_of_x(f,roots,tol,i, epochs=100):
x_roots=[] # list of roots
n_roots= roots # number of roots needed to find
incre = i #increments
h = tol #tolerance is the starting guess
for epoch in range(epochs): # the list of iteration that will be using
... | 0.611614 | 0.980913 |
```
import pandas as pd
import glob, os
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
basedir = '/Users/simon/Work/ECOSAT3/DATA/Dredges/'
gpd.read_file('/Users/simon/Work/ECOSAT3/DATA/Dredges/DR01/shapefile/dredge_01_events.shp')
#print glob.glob('%s/DR*')
#print os.l... | github_jupyter | import pandas as pd
import glob, os
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
basedir = '/Users/simon/Work/ECOSAT3/DATA/Dredges/'
gpd.read_file('/Users/simon/Work/ECOSAT3/DATA/Dredges/DR01/shapefile/dredge_01_events.shp')
#print glob.glob('%s/DR*')
#print os.listd... | 0.122733 | 0.389605 |
# DAT210x - Programming with Python for DS
## Module4- Lab2
```
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from sklearn import preprocessing
from sklearn.decomposition import PCA
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
```
### Some Boilerplat... | github_jupyter | import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from sklearn import preprocessing
from sklearn.decomposition import PCA
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
def scaleFeaturesDF(df):
# Feature scaling is a type of transformation that only chan... | 0.846863 | 0.951594 |
```
import json
import numpy as np
import operator
import math
def r_precision(G, R):
limit_R = R[:len(G)]
if len(G) != 0:
return len(list(set(G).intersection(set(limit_R)))) * 1.0 / len(G)
else:
return 0
def ndcg(G, R):
r = [1 if i in set(G) else 0 for i in R]
r = np.asfarray(r)
... | github_jupyter | import json
import numpy as np
import operator
import math
def r_precision(G, R):
limit_R = R[:len(G)]
if len(G) != 0:
return len(list(set(G).intersection(set(limit_R)))) * 1.0 / len(G)
else:
return 0
def ndcg(G, R):
r = [1 if i in set(G) else 0 for i in R]
r = np.asfarray(r)
dc... | 0.19923 | 0.68545 |
```
import pandas as pd
import cobra as co
```
# Convert the Tables that make up gapseq's "full model" to a cobrapy model Object
## Download the relevant tables into this repo's data folder
```
!wget -P "data/" "https://raw.githubusercontent.com/jotech/gapseq/f3d74944e5e4ee5a6ab328c4fd46b35fd53cee73/dat/seed_reacti... | github_jupyter | import pandas as pd
import cobra as co
!wget -P "data/" "https://raw.githubusercontent.com/jotech/gapseq/f3d74944e5e4ee5a6ab328c4fd46b35fd53cee73/dat/seed_reactions_corrected.tsv"
!wget -P "data/" "https://raw.githubusercontent.com/jotech/gapseq/f3d74944e5e4ee5a6ab328c4fd46b35fd53cee73/dat/seed_metabolites_edited.tsv"... | 0.37502 | 0.778691 |
### Summary Statistics and Quick Viz!
```
import pandas as pd
pd.options.display.max_rows = 30
```
### START HERE
Now we've learned about how to get our dataframe how we want it, let's try and get some fun out of it!
We have our data, now what?
We usually like to learn from it. We want to find out about maybe som... | github_jupyter | import pandas as pd
pd.options.display.max_rows = 30
df = pd.read_csv('../data/cereal.csv', index_col = 0)
df.head(15)
df.describe()
df.describe(include = "all")
df.sum()
manufacturer_column = df["mfr"]
manufacturer_column
manufacturer_freq = manufacturer_column.value_counts()
manufacturer_freq
manufacturer_freq... | 0.303938 | 0.984456 |
```
# We'll use requesta and BeautifulSoup again in this tutorial:
import requests
from bs4 import BeautifulSoup
## We'll also use the re module for regular expressions.
import re
## Let's look at this list of state universities in the US:
top_url = 'https://en.wikipedia.org/wiki/List_of_state_universities_in_the_Uni... | github_jupyter | # We'll use requesta and BeautifulSoup again in this tutorial:
import requests
from bs4 import BeautifulSoup
## We'll also use the re module for regular expressions.
import re
## Let's look at this list of state universities in the US:
top_url = 'https://en.wikipedia.org/wiki/List_of_state_universities_in_the_United_... | 0.398641 | 0.769384 |
# Statistics
```
import numpy as np
# 1D array
A1 = np.arange(20)
print(A1)
A.ndim
# 2D array
A2 = np.array([[11, 12, 13], [21, 22, 23]])
print(A2)
np.sum(A2, axis=0)
np.sum(A2)
A2.ndim
```
## Sum
- Sum of array elements over a given axis.
- **Syntax:** `np.sum(array); array-wise sum`
- **Syntax:** `np.sum... | github_jupyter | import numpy as np
# 1D array
A1 = np.arange(20)
print(A1)
A.ndim
# 2D array
A2 = np.array([[11, 12, 13], [21, 22, 23]])
print(A2)
np.sum(A2, axis=0)
np.sum(A2)
A2.ndim
# sum of 1D array
np.sum(A1)
# array-wise sum of 2D array
np.sum(A2)
A2
# sum of 2D array(axis=0, row-wise sum)
np.sum(A2, axis=0)
# sum of 2D arr... | 0.708616 | 0.991915 |
# Lesson 2.2:
# PowerGrid Models API - Using JSON Queries
This tutorial introduces the PowerGrid Models API and how it can be used to query model data.
__Learning Objectives:__
At the end of the tutorial, the user should be able to use the PowerGrid Models API to
*
*
*
## Getting Started
Before running any of ... | github_jupyter | # Establish connection to GridAPPS-D Platform:
from gridappsd import GridAPPSD
gapps = GridAPPSD("('localhost', 61613)", username='system', password='manager')
model_mrid = "_49AD8E07-3BF9-A4E2-CB8F-C3722F837B62" # IEEE 13 Node used for all example queries
topic = "goss.gridappsd.process.request.data.powergridmodel"
... | 0.438064 | 0.979136 |
```
%matplotlib inline
import numpy as np
import h5py
import os
from functools import reduce
from imp import reload
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from hangul.read_data import load_data, load_images, load_all_labels
from matplotlib import cm
from hangul import s... | github_jupyter | %matplotlib inline
import numpy as np
import h5py
import os
from functools import reduce
from imp import reload
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from hangul.read_data import load_data, load_images, load_all_labels
from matplotlib import cm
from hangul import style... | 0.359701 | 0.724675 |
# Training Pong Game by Using DQN
We use PyTorch to train a Deep Q Learning (DQN) agent on a Pong Game.
Reference Code:
- Pong_in_Pygame (Author: clear-code-projects)
+ Youtube: https://www.youtube.com/playlist?list=PL8ui5HK3oSiEk9HaKoVPxSZA03rmr9Z0k
+ Github: https://github.com/clear-code-projects/Pong_in_P... | github_jupyter | !git clone https://github.com/yenzu0329/DQN_for_Pong.git
!pip install pygame
import os
os.environ["SDL_VIDEODRIVER"] = "dummy"
%matplotlib inline
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class DeepQNetwork(nn.Module):
def __init__(sel... | 0.894522 | 0.955486 |
```
import pandas as pd
import numpy as np
from collections import defaultdict
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import confusion_matrix
from tqdm import tqdm
import itertools
import matplotlib.pyplot as plt
import re
%matplotlib inline
```
# Naive Bayes code (with Sentence)
##### St... | github_jupyter | import pandas as pd
import numpy as np
from collections import defaultdict
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import confusion_matrix
from tqdm import tqdm
import itertools
import matplotlib.pyplot as plt
import re
%matplotlib inline
def preprocess(str_arg):
cleaned_str=re.sub('[^... | 0.606149 | 0.789558 |
Predict the CaCO3 and TOC using the latest models (2021 Aug.) on the whole spetra.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#plt.style.use('ggplot')
plt.style.use('seaborn-colorblind')
#plt.style.use('dark_background')
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 3... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#plt.style.use('ggplot')
plt.style.use('seaborn-colorblind')
#plt.style.use('dark_background')
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['savefig.bbox'] = 'tight'
plt.rcParams['savefig.transparent'] = True
%m... | 0.371137 | 0.820829 |
```
import panel as pn
pn.extension('vtk')
```
The ``VTK`` pane renders VTK objects and vtk.js files inside a panel, making it possible to interact with complex geometries in 3D.
#### Parameters:
For layout and styling related parameters see the [customization user guide](../../user_guide/Customization.ipynb).
* **... | github_jupyter | import panel as pn
pn.extension('vtk')
dragon = pn.pane.VTK('https://raw.githubusercontent.com/Kitware/vtk-js/master/Data/StanfordDragon.vtkjs',
sizing_mode='stretch_width', height=400)
dragon
dragon.object = "https://github.com/Kitware/vtk-js-datasets/raw/master/data/vtkjs/TBarAssembly.vtkjs"
>... | 0.688887 | 0.886519 |
# Testing MHW Systems
```
# imports
from importlib import reload
import numpy as np
import os
from matplotlib import pyplot as plt
from pkg_resources import resource_filename
from datetime import date
import pandas
import sqlalchemy
import iris
import iris.quickplot as qplt
import h5py
from oceanpy.sst import io a... | github_jupyter | # imports
from importlib import reload
import numpy as np
import os
from matplotlib import pyplot as plt
from pkg_resources import resource_filename
from datetime import date
import pandas
import sqlalchemy
import iris
import iris.quickplot as qplt
import h5py
from oceanpy.sst import io as sst_io
from oceanpy.sst i... | 0.363647 | 0.782164 |
# Pandas
Pandas est une librairie Python dédiée à l'analyse de données.
## Series
La structure de données Series permet de gérer une **table de données à deux colonnes**, dans laquelle :
- les données sont ordonnées
- la première colonne contient une clé (index)
- le deuxième colonne contient des valeurs
- la deuxiè... | github_jupyter | import pandas as pd
animaux = ["chien", "chat", "lapin"]
pd.Series(animaux)
nombres = [10,4,8]
ns = pd.Series(nombres)
ns
nombres = [10,4,None]
pd.Series(nombres)
import numpy as np
np.isnan(np.nan)
personne = { "nom":"Dupont", "prénom":"Jean", "age":40 }
s = pd.Series(personne)
s
s.index
pd.Series(["Dupont","Jea... | 0.131312 | 0.986058 |
# Strings and Stuff in Python
```
import numpy as np
```
## Strings are just arrays of characters
```
s = 'spam'
s,len(s),s[0],s[0:2]
s[::-1]
```
#### But unlike numerical arrays, you cannot reassign elements:
```
s[0] = "S"
s
```
### Arithmetic with Strings
```
s = 'spam'
e = "eggs"
s + e
s + " " + e
4 * (s ... | github_jupyter | import numpy as np
s = 'spam'
s,len(s),s[0],s[0:2]
s[::-1]
s[0] = "S"
s
s = 'spam'
e = "eggs"
s + e
s + " " + e
4 * (s + " ") + e
print(4 * (s + " ") + s + " and\n" + e) # use \n to get a newline with the print function
"spam" == "good"
"spam" != "good"
"spam" == "spam"
"sp" < "spam"
"spam" < "eggs"
"sp" in "... | 0.33928 | 0.89096 |
# This notebook processes CAFE v3 ocean daily data for building climatologies. Only the last 100 years are used.
Currently only runs on Raijin, as control run data not yet transferred to Canberra
```
# Import packages -----
import pandas as pd
import xarray as xr
import numpy as np
from ipywidgets import FloatProgress... | github_jupyter | # Import packages -----
import pandas as pd
import xarray as xr
import numpy as np
from ipywidgets import FloatProgress
from dateutil.relativedelta import relativedelta
# Standard naming -----
fields = pd.DataFrame( \
{'name_CAFE': ['sst', 'patm_t', 'eta_t', 'sss', 'u_surf', 'v_surf', 'mld'],
'name_st... | 0.314051 | 0.761494 |
```
from datascience import *
path_data = '../../data/'
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
cones = Table.read_table(path_data + 'cones.csv')
nba = Table.read_table(path_data + 'nba_salaries.csv').relabeled(3, 'SALARY')
movies = Table.read_table(p... | github_jupyter |
from datascience import *
path_data = '../../data/'
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
cones = Table.read_table(path_data + 'cones.csv')
nba = Table.read_table(path_data + 'nba_salaries.csv').relabeled(3, 'SALARY')
movies = Table.read_table(path_... | 0.483648 | 0.946001 |
##Allele-specific expression analysis in An. coluzzii
```
import matplotlib.pyplot as P
%matplotlib inline
import numpy as np
import pandas as pd
RNA = ['A','B','C','D'] #These are wells that contain cDNA
D = pd.read_csv("round2/iPLEX_HYBRID_MAPHIG_6_24_16.csv")
#focus on UTR SNP
cyp9k1 = D.loc[D['Assay']=='CYP9K1-3... | github_jupyter | import matplotlib.pyplot as P
%matplotlib inline
import numpy as np
import pandas as pd
RNA = ['A','B','C','D'] #These are wells that contain cDNA
D = pd.read_csv("round2/iPLEX_HYBRID_MAPHIG_6_24_16.csv")
#focus on UTR SNP
cyp9k1 = D.loc[D['Assay']=='CYP9K1-3u']
#grab only cDNA
#cyp9k1 = cyp9k1.loc[cyp9k1['WELL'].str... | 0.118921 | 0.556098 |
# Ames Housing Dataset price modeling
We investigate the data to remove unnecessary columns and max-scale label
This could either happen in the private data lake or on the modeler's machine. In this case, we mimic a modeler requesting certain fields and a certain series of preprocessing steps.
```
import numpy as np... | github_jupyter | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.set_style("darkgrid")
df = pd.read_csv("data.csv")
print(f"Original size of dataframe {df.shape}")
residential_areas = {"RH", "RL", "RP", "RM"}
acceptable_housing_conditions = ... | 0.286469 | 0.98661 |
```
import bs4 as bs
import datetime as dt
import pandas as pd
import os
import pandas_datareader.data as web
import pickle
import requests
from dateutil.relativedelta import relativedelta, FR
end_date = pd.Timestamp(pd.to_datetime('today').strftime("%m/%d/%Y"))
start_date = end_date - relativedelta(years=3)
def save_s... | github_jupyter | import bs4 as bs
import datetime as dt
import pandas as pd
import os
import pandas_datareader.data as web
import pickle
import requests
from dateutil.relativedelta import relativedelta, FR
end_date = pd.Timestamp(pd.to_datetime('today').strftime("%m/%d/%Y"))
start_date = end_date - relativedelta(years=3)
def save_sp500... | 0.179387 | 0.172311 |
```
import pandas as pd
import geopandas as gpd
import seaborn as sns
import matplotlib.pyplot as plt
import husl
from legendgram import legendgram
import mapclassify
from matplotlib_scalebar.scalebar import ScaleBar
from matplotlib.colors import ListedColormap
from shapely.geometry import Point
from tqdm import tqdm
... | github_jupyter | import pandas as pd
import geopandas as gpd
import seaborn as sns
import matplotlib.pyplot as plt
import husl
from legendgram import legendgram
import mapclassify
from matplotlib_scalebar.scalebar import ScaleBar
from matplotlib.colors import ListedColormap
from shapely.geometry import Point
from tqdm import tqdm
clus... | 0.396769 | 0.669664 |
<a href="https://colab.research.google.com/github/sid-chaubs/data-mining-assignment-1/blob/main/DMT_1_PJ.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!git clone https://github.com/sid-chaubs/data-mining-assignment-1.git
%cd data-mining-assign... | github_jupyter | !git clone https://github.com/sid-chaubs/data-mining-assignment-1.git
%cd data-mining-assignment-1/
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import regex
from sklearn import tree, model_selection, preprocessing, ensemble
from scipy import stats
pd.set_option('display... | 0.463687 | 0.925869 |
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