code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
<a href="https://colab.research.google.com/github/healthonrails/annolid/blob/main/docs/tutorials/Train_networks_tutorial_v1.0.1.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Annolid (A segmenation based on mutiple animal tracking package)
In t... | github_jupyter |
### Model Selection
In order to select the best model for our data, I am going to look at scores for a variety of scikit-learn models and compare them using visual tools from Yellowbrick.
### Imports
```
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import os
import pandas as pd
from sklearn.m... | github_jupyter |
# Antarctic Circumnavigation Expedition Cruise Track data processing
## GLONASS and Trimble GPS data
Follow the steps as described here: http://epic.awi.de/48174/
Import relevant packages
```
import pandas as pd
import csv
import MySQLdb
import datetime
import math
import numpy as np
```
### STEP 1 - Extract data ... | github_jupyter |
# EuroSciPy 2018: NumPy tutorial (https://github.com/gertingold/euroscipy-numpy-tutorial)
## Let's do some slicing
```
mylist = list(range(10))
print(mylist)
```
Use slicing to produce the following outputs:
[2, 3, 4, 5]
[0, 1, 2, 3, 4]
[6, 7, 8, 9]
[0, 2, 4, 6, 8]
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
[7, 5, 3]
## ... | github_jupyter |
```
'''Check that the simulated kappa_gmf values are properly simulated, and also check them as a function of Z'''
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import h5py
import matplotlib.lines as mlines
from fancy import Uhecr
from fancy.interfaces.stan import uv_to_coord
from fan... | github_jupyter |
#Grae prediction using Parallel Random Forest
Grading is necessary tool for measure outcome of the sudying process. Unsuccessful lerning process may depend on many factors such as student attenction, teacher and background knowledge. In many academic instituetions, the prerequisite subjects are required before re... | github_jupyter |
## 8.5 Optimization of Basic Blocks
### 8.5.1
> Construct the DAG for the basic block
> ```
d = b * c
e = a + b
b = b * c
a = e - d
```
```
+--+--+
| - | a
+-+++-+
| |
+---+ +---+
| |
+--v--+ +--v--+
e | + | | * | d,b
+-+++-+... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
# Using `matplotlib` to display inline images
In this notebook we will explore using `matplotlib` to display images in our notebooks, and work towards developing a reusable function to display 2D,3D, color, and label overlays for SimpleITK images.
We will also look at the subtleties of working with image filters that... | github_jupyter |
## Logistic Regression
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split,KFold
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,\
recall_score,roc_curve,auc
import expectation_reflection as ER
from sklearn.line... | github_jupyter |
---
## 01. Interpolación de Funciones
Eduard Larrañaga (ealarranaga@unal.edu.co)
---
## Interpolación
### Resumen
En este cuaderno se presentan algunas de las técnicas de interpolación de una función.
---
## Interpolación
Los datos astrofísicos (experimentales y sint... | github_jupyter |
# Introduction #
Most of the techniques we've seen in this course have been for numerical features. The technique we'll look at in this lesson, *target encoding*, is instead meant for categorical features. It's a method of encoding categories as numbers, like one-hot or label encoding, with the difference that it also... | github_jupyter |
## Deploy an ONNX model to an IoT Edge device using ONNX Runtime and the Azure Machine Learning
```
!python -m pip install --upgrade pip
!pip install azureml-core azureml-contrib-iot azure-mgmt-containerregi... | github_jupyter |
```
% matplotlib inline
import json, re
import fiona
from shapely.geometry import MultiPolygon, shape
import pandas as pd
import geopandas as gp
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.collections import PatchCollection
from matplotlib.colors im... | github_jupyter |
# About this kernel
+ efficientnet_b3
+ CurricularFace
+ Mish() activation
+ Ranger (RAdam + Lookahead) optimizer
+ margin = 0.9
## Imports
```
import sys
sys.path.append('../input/shopee-competition-utils')
sys.path.insert(0,'../input/pytorch-image-models')
import numpy as np
import pandas as pd
import torch
... | github_jupyter |
```
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import math
import seaborn as sns
import multiprocessing
from multiprocessing import Process
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import t... | github_jupyter |
# North and South Chickamauga Creek USGS Stations Streamflow Data for HSPF Model
## Shuvashish Roy
### *We will use hydrofunctions package for flow data retrieval*
```
pip show hydrofunctions
import hydrofunctions as hf
import pandas as pd
%matplotlib inline
import hydrofunctions as hf
import pandas as pd
%matplotli... | github_jupyter |
<center>
<img src="https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# **Space X Falcon 9 First Stage Landing Prediction**
## Assignment: Machine Learning Prediction
Estimated time needed: **60**... | github_jupyter |
```
# While in argo environment: Import necessary packages for this notebook
import numpy as np
from matplotlib import pyplot as plt
import xarray as xr
import pandas as pd
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
%matplotlib inline
import glob
```
!python -m pip in... | github_jupyter |
```
%matplotlib inline
%run notebook_setup
ndim = 15
import time
import emcee
import numpy as np
import pymc3 as pm
from pymc3.step_methods.hmc import quadpotential as quad
np.random.seed(41)
with pm.Model() as model:
pm.Normal("x", shape=ndim)
potential = quad.QuadPotentialDiag(np.ones(ndim))
step_kwa... | github_jupyter |
<h1 id="i1"> Data 512 - A6 : Predicting Earthquakes : Final Project</h1>
Gautam Moogimane <br>
University of Washington - Fall 2018
<h2 id="i1">I. Introduction </h2>
Having stayed in Japan for a long time, I've been accustomed to getting up at odd hours, with everything shaking around me. Tremors and earthqua... | github_jupyter |
# Pre-Calculus Notebook
```
# Begin with some useful imports
import numpy as np
import pylab
from fractions import Fraction
from IPython.display import display, Math
from sympy import init_printing, N, pi, sqrt, symbols, Symbol
from sympy.abc import x, y, theta, phi
init_printing(use_latex='mathjax') # do this to all... | github_jupyter |
```
#importing the packages
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import joblib # for saving algorithm and preprocessing objects
from sklearn.linear_model import LinearRegression
# uploading the dataset
df = pd.read_csv('pollution_us_2000_... | github_jupyter |
# Modeling and Simulation in Python
Chapter 5: Design
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# If you want the figures to appear in the notebook,
# and you want to interact with them, use
# %matplotlib notebook
# If yo... | github_jupyter |
## Dependencies
```
import os
import cv2
import shutil
import random
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.utils import class_weight
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, coh... | github_jupyter |
# Covid-19 Prediction from Lung CT
This notebook utilizes a trained **classifier** to recognize **Covid-19** positive patients from their **CT lungs** scans in order to support the physician’s decision process with a quantitative approach.
```
from esmlib import *
```
## Select an exam folder
With the following cod... | github_jupyter |
# Monte Carlo Simulations with Python (Part 1)
[Patrick Hanbury](https://towardsdatascience.com/monte-carlo-simulations-with-python-part-1-f5627b7d60b0)
- Notebook author: Israel Oliveira [\[e-mail\]](mailto:'Israel%20Oliveira%20'<prof.israel@gmail.com>)
```
%load_ext watermark
import numpy as np
import math
import r... | github_jupyter |
```
import sys; sys.path.append('../rrr')
from multilayer_perceptron import *
from figure_grid import *
from local_linear_explanation import *
from toy_colors import generate_dataset, imgshape, ignore_rule1, ignore_rule2, rule1_score, rule2_score
import lime
import lime.lime_tabular
```
# Toy Color Dataset
This is a ... | github_jupyter |
```
import os
import re
import torch
import pickle
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
tqdm.pandas()
```
# 1. Pre-processing
### Create a combined dataframe
> This creates a dataframe containing the image IDs & labels for both original images provided by the Bristol Myers Squibb pharmac... | github_jupyter |
```
import functools
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import shapely.geometry
import skimage.draw
import tensorflow as tf
import pydicom
import pymedphys
import pymedphys._dicom.structure as dcm_struct
# Put all of the DICOM data here, file structure doesn't matter:
data_path_root ... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.datasets import mnist
(x_train, y_train), _ = mnist.load_data()
x_train = x_train / 255.0
x_train = np.expand_dims(x_train, axis=3)
print(x_train.shape)
print(y_train.shape)
num_classes = 10
plt.imshow(np.squeeze(x_train[10]))
... | github_jupyter |
## Accessing High Resolution Electricity Access (HREA) data with the Planetary Computer STAC API
The HREA project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging VIIRS satellite imagery with computational methods, these high-resolution data provide new t... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.auto import tqdm
import torch
from torch import nn
import gin
import pickle
import io
from sparse_causal_model_learner_rl.trainable.gumbel_switch import With... | github_jupyter |
# [ATM 623: Climate Modeling](../index.ipynb)
[Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany
# Lecture 16: Modeling the seasonal cycle of surface temperature
### About these notes:
This document uses the interactive [`Jupyter notebook`](https://jupyter.org) format. T... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
# Набор данных взят с https://www.kaggle.co... | github_jupyter |
<h1>2b. Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using tf.estimator and evaluate its performance. The dataset is rather small (7700 samples), so we can do it all in-memory. We will also simply pass the raw data in as-is.
```
import tensorflow as tf
import p... | github_jupyter |
```
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=50, centers=2, cluster_std=0.5, random_state=4)
y = 2 * y - 1
plt.scatter(X[y == -1, 0], X[y == -1, 1], marker='o', label="-1 class")
plt.scatter(X[y == +1, 0], X[y == +1, 1], marker='x', label="+1 class")
plt.xlabel("x1")
plt.ylabel("x2")
plt.leg... | github_jupyter |
### Set up google colab and unzip train data
```
# Set up google drive in google colab
#save this change to master
from google.colab import drive
drive.mount('/content/drive')
# Unzip training data from drive
!unzip -q 'drive/My Drive/VOCdevkit.zip'
```
### Import Libraries
```
import random
import os
import math
i... | github_jupyter |
# LeetCode #804. Unique Morse Code Words
## Question
https://leetcode.com/problems/unique-morse-code-words/
International Morse Code defines a standard encoding where each letter is mapped to a series of dots and dashes, as follows: "a" maps to ".-", "b" maps to "-...", "c" maps to "-.-.", and so on.
Fo... | github_jupyter |
*아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.*
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.org/github/rickiepark/nlp-with-pytorch/blob/master/chapter_1/PyTorch_Basics.ipynb"><img src="https://jupyter.org... | github_jupyter |
#1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
```
!pip install git+https://github.com/google/starthinker
```
#2. Get Cloud Project ID
To run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/mast... | github_jupyter |
```
# Exploratory Data Analysis and Plotting Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Scikit-Learn Models
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClass... | github_jupyter |
```
import pymongo
uri = "mongodb://user1:MongoPassWd1@localhost:27017/"
client = pymongo.MongoClient(uri, 27017)
db = client["DjangoServer"]
col = db["dangdangBook"]
col.estimated_document_count()
from bson import json_util
import json
from bson import ObjectId
class JSONEncoder(json.JSONEncoder):
"""处理ObjectId... | github_jupyter |
# Sklearn compatible Grid Search for classification
Grid search is an in-processing technique that can be used for fair classification or fair regression. For classification it reduces fair classification to a sequence of cost-sensitive classification problems, returning the deterministic classifier with the lowest em... | github_jupyter |
# "Reverse mode autodiff"
> "Understand how machine learning frameworks implement autodiff"
- toc:true
- branch: master
- badges: true
- comments: true
- author: Saibaba Telukunta
- categories: [autodiff, autograd, gradient, partial-derivative, chain-rule]
Introduction
--
Gradient descent (GD) is the main workhorse ... | github_jupyter |
```
import pandas as pd
import numpy as np
def load_datasets():
file = '../Data-files/1352S_101F.xlsx'
sheets = ['整数-101F', 'top5F', 'top3F', '3F']
tables = {name: pd.read_excel(file, sheet_name=name) for name in sheets}
for i in tables.values():
i.index += 1
return tables
def to_ot... | github_jupyter |
# TicTacToe
### Main File
NOTE:Add in Sections as they are completed
### Completed Sections
1. TODO:
2. TODO:
3. TODO:
4. TODO:
5. Complete
6. TODO:
7. TODO:
8. TODO:
## Sections
1. Selection of the first player. (e.g. Would you like to play first?)
2. Assignment of "O" or "X" for a user. (e.g. Please choose 'O... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# 06. Distributed CNTK using custom docker images
In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training.
## Prerequisites
* ... | github_jupyter |
```
from time import time
from importlib import reload
import pandas as pd
import sklearn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, ElasticNet, LogisticRegression
from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor, GradientBoostingClassifi... | github_jupyter |
<a href="https://colab.research.google.com/github/skojaku/cidre/blob/second-edit/examples/example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# About this notebook
In this notebook, we apply CIDRE to a network with communities and demonstrate h... | github_jupyter |
# Fine-Tuning a BERT Model and Create a Text Classifier
In the previous section, we've already performed the Feature Engineering to create BERT embeddings from the `reviews_body` text using the pre-trained BERT model, and split the dataset into train, validation and test files. To optimize for Tensorflow training, we ... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
```
### Loading Data
```
data = pd.read_csv('Coffee.csv')
```
### Finding out the any missing values across columns
**We can see there are no missing values**
```
pd.isnull(data).sum()
```
### Looking at some part of dat... | github_jupyter |
```
import pandas as pd
import numpy as np
from tqdm import tqdm
import seaborn as sns
import matplotlib.pyplot as plt
import re
from nltk.corpus import stopwords
stop = list(set(stopwords.words('english')))
f_train = open('../data/train_14k_split_conll.txt','r',encoding='utf8')
line_train = f_train.readlines()
f_val... | github_jupyter |
```
import pyspark
import pandas as pd
import csv
from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors
from pyspark import SparkContext ,SparkConf
from pyspark.ml import Pipeline
from pyspark.ml.feature import OneHotEncoder, StringIndexer
from pyspark import SQLContext as sql
from pyspark.... | github_jupyter |
# Exploratory Data Analysis
## Loading Imports and Data
```
!python -m spacy download en_core_web_lg
!pip install squarify
# Base
import pandas as pd
import numpy as np
from collections import Counter
# Plotting
import squarify
import matplotlib.pyplot as plt
import seaborn as sns
# NLP Libraries
import spacy
from ... | github_jupyter |
# 2-22: Intro to scikit-learn
<img src="https://www.cityofberkeley.info/uploadedImages/Public_Works/Level_3_-_Transportation/DSC_0637.JPG" style="width: 500px; height: 275px;" />
---
** Regression** is useful for predicting a value that varies on a continuous scale from a bunch of features. This lab will introduce th... | github_jupyter |
# Author: Faique Ali
## Task 01 : Prediction Using Supervised ML
<p>
Using Linear Regression, predict the percentage of an student based on his no. of study hours.
</p>
# Imports
```
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_... | github_jupyter |
# Analysis of SHEMAT-Suite models
The created geological models with gempy were exported as SHEMAT-Suite input files. SHEMAT-Suite (https://git.rwth-aachen.de/SHEMAT-Suite/SHEMAT-Suite-open) [1] is a code for solving coupled heat transport in porous media. It is written in fortran and uses a finite differences scheme ... | github_jupyter |
**Chapter 19 – Training and Deploying TensorFlow Models at Scale**
_This notebook contains all the sample code in chapter 19._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb"><img src="https://ww... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 3, Module 4*
---
# Model Interpretation
- Visualize and interpret **partial dependence plots**
- Explain individual predictions with **shapley value plots**
### Setup
Run the code cell below. You can work locally (follow the [local setup instructions](https://lambdascho... | github_jupyter |
<div align="center">
<font size="5">__cta-lstchain: Notebook for training and storage Random Forests for LST1 data analysis__</font>
<font size="4">
To run this notebook you will need the last version of cta-lstchain:
git clone https://github.com/cta-observatory/cta-lstchain
<br>
<br>
**If you have ctapipe alread... | github_jupyter |
# Napa County Model
Build the model for Napa County California. The goal of the model will be to predict the total burn area of wildfires in Napa County for a given month
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from catboost import... | github_jupyter |
# Thomas Robbins
### GEOS 505 Final Project Nov. 15 submission
#### In conjunction with the abstract that was previously submitted for this project, the work below is the submission requirement for Nov. 15th, 2018.
##### Below are snippets of code that represent the data chosen for this project, along with brief exp... | github_jupyter |
<a href="https://colab.research.google.com/github/ayan59dutta/MLCC-Exercises/blob/master/Preliminaries/creating_and_manipulating_tensors.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### Copyright 2017 Google LLC.
```
# Licensed under the Apache... | github_jupyter |
### Imports and Helper Functions
```
import numpy as np
import dask_xgboost as dxgb_gpu
import dask
import dask_cudf
from dask.delayed import delayed
from dask.distributed import Client, wait
import xgboost as xgb
import cudf
from cudf.dataframe import DataFrame
from collections import OrderedDict
import gc
from glob ... | github_jupyter |
## Import modules
```
import numpy as np
import matplotlib.pyplot as plt
import os
import time
from sklearn.linear_model import SGDRegressor, LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error
```
## Utils
```
d... | github_jupyter |
```
import pandas as pd
import numpy as np
%matplotlib inline
if 'bigDataFrame' in globals():
print("Exist, do nothing!")
else:
print("Read data.")
bigDataFrame = pd.read_pickle("../output/bigDataFrame.pkl")
bigDataFrame.rename(columns={"PM2.5": "PM25"}, inplace=True)
bigDataFrame.head()
pollutedPlaces ... | github_jupyter |
# Longest Palindromic Subsequence
In this notebook, you'll be tasked with finding the length of the *Longest Palindromic Subsequence* (LPS) given a string of characters.
As an example:
* With an input string, `ABBDBCACB`
* The LPS is `BCACB`, which has `length = 5`
In this notebook, we'll focus on finding an optimal... | github_jupyter |
# Pool-based Active Learning - Getting Started
The main purpose of this tutorial is to ease the implementation of our library `scikit-activeml` to new users. `scikit-activeml` is a library that executes the most important query strategies. It is built upon the well-known machine learning frame-work `scikit-learn`, whi... | github_jupyter |
# Centerpartiets budgetmotion 2022
https://www.riksdagen.se/sv/dokument-lagar/dokument/motion/centerpartiets-budgetmotion-2022_H9024121
```
import pandas as pd
import requests
pd.options.mode.chained_assignment = None
multiplier = 1_000_000
docs = [
{'utgiftsområde': 1, 'dok_id': 'H9024141'},
{'utgiftsområde... | github_jupyter |
## Plotting of profile results
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# common
import os
import os.path as op
# pip
import numpy as np
import pandas as pd
import math
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib import gridspec
# DEV: override installed teslakit
import sys
sys.path... | github_jupyter |
# Dropout regularization with gluon
```
import mxnet as mx
import numpy as np
from mxnet import gluon
from tqdm import tqdm_notebook as tqdm
```
## Context
```
ctx = mx.cpu()
```
## The MNIST Dataset
```
batch_size = 64
num_inputs = 784
num_outputs = 10
def transform(data, label):
return data.astype(np.float32... | github_jupyter |
# Update rules
```
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.animation as animation
from IPython.display import HTML
from matplotlib import cm
from matplotlib.colors import LogNorm
def sgd(f, df, x0, y0, lr, steps):
x = np.zeros(steps + 1)
y ... | github_jupyter |
### Interest Rate Regression Model using Linear Regression+RandomCV
- Five models were created. Three Interest Rate Regression Models and two Loan Default Classification Models
- This notebook is a deep dive into the Linear Regression Model created to predict Loan Interest Rates.
- We will begin by exploring the top f... | github_jupyter |
#### MTA Project mvp
###### Project Goal
- Generate data to facilitate micro-targeted advertising based on time of day and demographics.
- I am focusing on the Canal St. station with one month of data, but the methodology and
code can be easily be applied to other stations and time frames.
###### Milestone Reache... | github_jupyter |
## Deliverable 3. Create a Travel Itinerary Map.
```
# Dependencies and Setup
import pandas as pd
import requests
import gmaps
# Import API key
import sys
sys.path.append('../')
from config import g_key
# Configure gmaps
gmaps.configure(api_key=g_key)
# 1. Read the WeatherPy_vacation.csv into a DataFrame.
vacation_d... | github_jupyter |
# Using Biopython's PDB Header parser to get missing residues
Previously this worked out and had to be run at that time with a development version of Biopython that I got working [here](https://github.com/fomightez/BernBiopython). Now current Bioython has the essential functionality about missing residues in structur... | github_jupyter |
# Software Carpentry
# Welcome to Binder
This is where will do all our Python, Shell and Git live coding.
## Jupyter Lab
Let's quickly familiarise ourselves with the enironment ...
- the overal environment (ie your entire browser tab) is called:
*Jupyter Lab*
it contains menus, tabs, toolbars and a file b... | github_jupyter |
# Sorting Objects in Instance Catalogs
_Bryce Kalmbach_
This notebook provides a series of commands that take a Twinkles Phosim Instance Catalog and creates different pandas dataframes for different types of objects in the catalog. It first separates the full sets of objects in the Instance Catalogs before picking ou... | github_jupyter |
### CS/GTO: step 1 and 2
```
show_plots=False
import numpy as np
from scipy.linalg import eig, eigh, eigvals, eigvalsh
from scipy.optimize import minimize
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Qt5Agg')
%matplotlib qt5
import pandas as pd
#
# extend path by location of the dvr package
#
imp... | github_jupyter |
```
from bs4 import BeautifulSoup
from splinter import Browser
from pprint import pprint
import pymongo
import pandas as pd
import requests
!which chromedriver
executable_path = {'executable_path': 'chromedriver'}
browser = Browser("chrome", **executable_path)
url = ('https://mars.nasa.gov/news/')
browser.visit(url)
#... | github_jupyter |
# Cloud-based machine learning || 云端机器学习
Thus far, we have looked at building and fitting ML models “locally.” True, the notebooks have been located in the cloud themselves, but the models with all of their predictive and classification power are stuck in those notebooks. To use these models, you would have to load d... | github_jupyter |
# Mumbai House Price Prediction - Supervised Machine Learning-Regression Problem
## Data Preprocessing
# The main goal of this project is to Predict the price of the houses in Mumbai using their features.
# Import Libraries
```
# importing necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
%ma... | github_jupyter |
```
import os
import glob
import math
import time
from joblib import Parallel, delayed
import pandas as pd
import numpy as np
import scipy as sc
from sklearn.model_selection import KFold
import lightgbm as lgb
import warnings
import matplotlib.pyplot as plt
import matplotlib
from sklearn.model_selection import train_te... | github_jupyter |
```
from scipy.stats import ranksums, sem
import numpy as np
from statannot import add_stat_annotation
import copy
import os
import matplotlib.pyplot as plt
import matplotlib
save_dir = os.path.join("/analysis/fabiane/documents/publications/patch_individual_filter_layers/MIA_revision")
plt.style.use('ggplot')
matplotli... | github_jupyter |
```
import forecasting as fc
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter('ignore')
revenues = pd.read_csv('Final Tables by Tax Source/final_data_revenue.csv')
revenues['Date'] = pd.to_datetime(revenues['Date'])
revenues = revenues.iloc[:, 1:]
```
1. Create forecasts DataFrame, contain... | github_jupyter |
```
# Import dependencies pandas,
# requests, gmaps, census, and finally config's census_key and google_key
# Declare a variable "c" and set it to the census with census_key.
# https://github.com/datamade/census
# We're going to use the default year 2016, however feel free to use another year.
# Run a censu... | github_jupyter |
# Goals
### Train a classifier using resnet18 on natural-images dataset
### Understand what lies inside resnet18 network
# What is resnet
## Readings on resnet
1) Points from https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035
- The core idea of ResNet is introducing a so-call... | github_jupyter |
# Speech Identity Inference
Let's check if the pretrained model can really identify speakers.
```
import os
import numpy as np
import pandas as pd
from sklearn import metrics
from tqdm.notebook import tqdm
from IPython.display import Audio
from matplotlib import pyplot as plt
%matplotlib inline
import tensorflow as... | github_jupyter |
```
import numpy as np
import cs_vqe as c
import ast
import os
from openfermion import qubit_operator_sparse
import conversion_scripts as conv_scr
import scipy as sp
from openfermion import qubit_operator_sparse
import conversion_scripts as conv_scr
from openfermion.ops import QubitOperator
# with open("hamiltonians.t... | github_jupyter |
# Python for data analysis
For those who are new to using Python for scientific work, we first provide a short introduction to Python and the most useful packages for data analysis.
## Python
Disclaimer: We can only cover some of the basics here. If you are completely new to Python, we recommend to take an introductor... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pylab as plt
import scipy
import scipy.stats
import scipy.integrate
```
# Thème 3 : Analyse Bayésienne
## Jeu du pile ou face
On souhaite estimer, à partir d'un série de N mesures au jeu de pile ou face, la probabilité $p$ d'obtenir pile.
Pour cela, dans le... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import cv2
import pandas as pd
import matplotlib.image as mpimg
import pylab as pl
%matplotlib inline
image = cv2.imread('train_images/0a4e1a29ffff.png') # 000c1434d8d7 0a4e1a29ffff 7b87b0015282
#imgplot = plt.imshow(image)
imgplot = plt.imshow(cv2.cvtColor(image... | github_jupyter |
# Capstone Project: Create a Customer Segmentation Report for Arvato Financial Services
In this project, you will analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. You'll use unsupervised learning techniques to pe... | github_jupyter |
# 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 models
import matplotlib.pyplot as plt
import numpy as np
import xlrd
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_curve, auc
import itertools
... | github_jupyter |
Lambda School Data Science
*Unit 2, Sprint 2, Module 3*
---
<p style="padding: 10px; border: 2px solid red;">
<b>Before you start:</b> Today is the day you should submit the dataset for your Unit 2 Build Week project. You can review the guidelines and make your submission in the Build Week course for your cohort ... | github_jupyter |
> **Note:** In most sessions you will be solving exercises posed in a Jupyter notebook that looks like this one. Because you are cloning a Github repository that only we can push to, you should **NEVER EDIT** any of the files you pull from Github. Instead, what you should do, is either make a new notebook and write you... | github_jupyter |
# MASH analysis pipeline with data-driven prior matrices
This notebook is a pipeline written in SoS to run `flashr + mashr` for multivariate analysis described in Urbut et al (2019). This pipeline was last applied to analyze GTEx V8 eQTL data, although it can be used as is to perform similar multivariate analysis for ... | github_jupyter |
# Visualisation in Python - Matplotlib
Here is the sales dataset for an online retailer. The data is collected over a period of three years: 2012 to 2015. It contains the information of sales made by the company.
The products captured belong to three categories:
Furniture
Office Supplies
Technology
Also, the comp... | github_jupyter |
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