text stringlengths 2.5k 6.39M | kind stringclasses 3
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### Average Precision for test data
#### Import data
```
import pandas as pd
import numpy as np
import os
import glob
import matplotlib.pyplot as plt
%matplotlib inline
pd.options.display.float_format = "{:,.4f}".format
final_dir = "/scratch/jag2j/final_data/"
os.chdir(final_dir)
os.listdir()
result_list = glob.glob... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
## AmExpert 2021 โ Machine Learning Hackathon
### Problem Statement
* A mid-sized private bank that includes a variety of banking products, such as savings accounts, current accounts, investment products, credit products, and home loans.
* The task is to predict the next set of products (upto 3 products) for a set of... | github_jupyter |
```
%matplotlib inline
```
์ฌ์ฉ์ ์ ์ Dataset, Dataloader, Transforms ์์ฑํ๊ธฐ
==========================================================
**์ ์** : Sasank Chilamkurthy <https://chsasank.github.io>
**๋ฒ์ญ** : ์ ์ค์ฑ <https://github.com/Yunseong-Jeong>
๋จธ์ ๋ฌ๋ ๋ฌธ์ ๋ฅผ ํธ๋ ๊ณผ์ ์์ ๋ฐ์ดํฐ๋ฅผ ์ค๋นํ๋๋ฐ ๋ง์ ๋
ธ๋ ฅ์ด ํ์ํฉ๋๋ค.
PyTorch๋ ๋ฐ์ดํฐ๋ฅผ ๋ถ๋ฌ์ค๋ ๊ณผ์ ์ ์ฝ๊ฒํด์ฃผ๊ณ , ๋ ์ ์ฌ์ฉํ... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
<a href="https://colab.research.google.com/github/gumdropsteve/intro_to_python/blob/main/day_02/intro_to_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Intro to Python
Intro to Python, day 2.
## Variables
```
a = 'this is a string'
... | github_jupyter |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import os
import tensorflow as tf
print(tf.__version__)
import boto3
from sagemaker import get_execution_role
tf.compat.v1.enable_eager_execution()
import utils
import data
import extract... | github_jupyter |
```
import tensorflow as tf
import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import numpy
import PIL
from PIL import Image
np.random.seed(1337) # for reproducibility
import random
from ke... | github_jupyter |
<a href="https://colab.research.google.com/github/Educat8n/Reinforcement-Learning-for-Game-Playing-and-More/blob/main/Module3/Module_3.3_Application_of_RL_in_Finance_TensorTrader_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Install Tens... | github_jupyter |
# Interacting with Python
__Content modified under Creative Commons Attribution license CC-BY 4.0,
code under BSD 3-Clause License ยฉ 2020 R.C. Cooper__
These notebooks are a combination of original work and modified notebooks from [Engineers Code](https://github.com/engineersCode/EngComp.git) learning modules. The le... | github_jupyter |
```
from scipy import stats
import scipy
import matplotlib.pyplot as plt
import pandas as pd
import math
""" Precificaรงรฃo utilizando Black and Scholes.
cp: +1 -> call; -1 put
s: valor da aรงรฃo
k: strike
t: tempo em dias atรฉ expirar a opรงรฃo
v: volatilidade
rf: taxa de juros neutra risco
"""
def bl... | github_jupyter |
```
import pickle
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os
from scipy.stats import pearsonr
```
# Load Data
```
# test
order = pickle.load(open('C:/Users/Vanda/PycharmProjects/dense/results/nppmi/order/glove.6B.400k.300d.txt_f_conceptnet56_top50000_base_order.p', 'rb'))
map_c = p... | github_jupyter |
# Classification using Stochastic Gradient Descent
### Dr. Tirthajyoti Sarkar, Fremont, CA 94536
In this notebook, we show the application of Stochastic Gradient Descent for classification problems. **This is particularly useful compared to some other popular classifiers like Random Forest as the training dataset and ... | github_jupyter |
# Bert NER on SageMaker using PyTorch
This uses the Biocreative II gene mention dataset https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/
```
import sys, os
import logging
sys.path.append("src")
logging.basicConfig(level="INFO", handlers=[logging.StreamHandler(sys.stdout... | github_jupyter |
# [deplacy](https://koichiyasuoka.github.io/deplacy/) per l'analisi sintattica
## con [NLP-Cube](https://github.com/Adobe/NLP-Cube)
```
!pip install deplacy nlpcube
from cube.api import Cube
nlp=Cube()
nlp.load("it")
doc=nlp("Chi non beve in compagnia o รจ un ladro o รจ una spia.")
import deplacy
deplacy.render(doc)
de... | github_jupyter |
# scona
scona is a tool to perform network analysis over correlation networks of brain regions.
This tutorial will go through the basic functionality of scona, taking us from our inputs (a matrix of structural regional measures over subjects) to a report of local network measures for each brain region, and network le... | github_jupyter |
# DocTable Schemas
Your database table column names and types come from a schema class defined using the `@doctable.schema` decorator. In addition to providing a schema definition, this class can be used to encapsulate data when inserting or retrieving from the database.
At its most basic, your schema class operates ... | github_jupyter |
# Persistence model
You remember your professor of the Time Series class.
Don't build a crazy model before trying with persistence. A baseline is always valuable: sometimes it provides good enough results, but it always sets the level for more complex approaches.
<div class="alert alert-block alert-warning">
<b>Sim... | github_jupyter |
# Comparison of the accuracy of a cutting plane active learning procedure using the (i) analytic center; (ii) Chebyshev center; and (iii) random center on the diabetes data set
# The set up
```
import numpy as np
import pandas as pd
import active
import experiment
import logistic_regression as logr
from sklearn impor... | github_jupyter |
# Video Actor Synchroncy and Causality (VASC)
## RAEng: Measuring Responsive Caregiving Project
### Caspar Addyman, 2020
### https://github.com/infantlab/VASC
# Step 3: Analyse the data using scipy statsmodels
This script correlates and compares the timeseries of wireframes for the two figures in the video `["parent"... | github_jupyter |
<a href="http://cocl.us/pytorch_link_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " />
</a>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN... | github_jupyter |
```
# importing the necessary packages
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
tracks... | github_jupyter |
# UART Demo
This demo highlights the usefulness of using a more complex MMIO driver wrapper by implementing a wrapper to interact with UART hardware. This wrapper is included in the notebook as an example of how to create a more complicated MMIO driver, including how to interact with interrupts.
```
import asyncio, t... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:85% !important; }</style>"))
%load_ext autoreload
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.client import device_lib
from tensorflow.keras import layers,models,util... | github_jupyter |
# Fit the DDM on individual data
```
import rlssm
import pandas as pd
import os
```
## Import the data
```
data = rlssm.load_example_dataset(hierarchical_levels = 1)
data.head()
```
## Initialize the model
```
model = rlssm.DDModel(hierarchical_levels = 1)
```
## Fit
```
# sampling parameters
n_iter = 1000
n_ch... | github_jupyter |
The problem statement I am interested in is: **predicting the publisher's name from a given title**. For approaching this problem, first I am going to need a dataset consisting of article/post titles with their sources mentioned.
The dataset I am going to use is already there as a BigQuery public dataset ([link](http... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
<div class="alert alert-block alert-info">
<h1 style='color:black'>Python for beginners</h1>
<h1><i>Software WG tutorial at CNS*2021</i></h1>
</div>
<div class="alert alert-block alert-warning">
<h2 style='color:black'>Part 1: Virtual environments and Python package installation</h2>
</div>
### Using virt... | github_jupyter |
## Convergence Testing:
### K-Point Convergence:
Using a plane-wave energy cutoff of 520 eV, and Monkhorst pack k-grid densities of $i$ x $i$ x $i$ for $i$ ranging from 1 to 8.
```
import numpy as np
import matplotlib.pyplot as plt
kdensity = np.arange(3, 8.1)
kconv_energies = np.array([float(line.rstrip('\n'))
... | github_jupyter |
# Chaper 8 - Intrinsic Curiosity Module
#### Deep Reinforcement Learning *in Action*
##### Listing 8.1
```
import gym
from nes_py.wrappers import JoypadSpace #A
import gym_super_mario_bros
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT #B
env = gym_super_mario_bros.make('SuperMarioBros-v0'... | github_jupyter |
# Flow in a nano-porous material
In this notebook we'll explore flow induced in a nano-porous material. To do this we introduce a force $\mathbf{F}_x$ acting on each particle $i$ in the nano-porous material.
In the case of a gravitational field, we can formulate Darcy's law for flow as
\begin{align}
\mathbf{U} = \... | github_jupyter |
<a href="https://colab.research.google.com/github/SamuelLawrence876/Jamaica-stock-exchange-quantative-analysis/blob/master/Quantitative_analysis_with_the_JSE.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# This notebook serves as an introuction in... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Creating and Updating a Docker Image before Deployment as a Webservice
This notebook demonstrates how to make changes to an existing docker image, before deploying it as a webservice.
Knowing how to do this can be helpful,... | github_jupyter |
```
import argparse
import copy
import json
import os
import sys
try:
import apex
except:
print("No APEX!")
import numpy as np
import torch
from torch import nn
import yaml
from det3d import torchie
from det3d.datasets import build_dataloader, build_dataset
from det3d.models import build_detector
from det3d.to... | github_jupyter |
# CMAES : Covariance Matrix Adaptation Evolutionary Strategy
Setup code and utility functions to plot and explore
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
from numpy.random import multivariate_normal
im... | github_jupyter |
# Plotting and typesetting
The following are functions for plotting and typesetting.
```
%pylab inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
font = {'size': 12}
plt.rc('font', **font)
# Function for creating a surface plot.
def plot3d(f,lim=(-5,5),title='Surface plot',detail=0.05,
... | github_jupyter |
# Model Optimization with an Image Classification Example
1. [Introduction](#Introduction)
2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)
3. [Train the model](#Train-the-model)
## Introduction
***
Welcome to our model optimization example for image classification. In this demo, we will use the ... | github_jupyter |
# M11. Binary Representation
I've so far deferred any exposition of the binary system, mostly for reasons which come down to expedience. There is nothing remarkable about positional notation, much less its specific manifestation in base 2 (which is all binary really is).
It's only on two counts that I'll qualify the... | github_jupyter |
# BERT Transformer Classifier
### with HuggingFace and Tensorflow 2
```
# slient install
!pip install -q -r requirements.txt
# all imports
import os, time
from datetime import datetime
from tqdm.notebook import trange, tqdm
import requests
import europy
from europy.notebook import load_global_params
from europy.decor... | github_jupyter |
```
! pip install transformers
# ! pip install scipy sklearn
# ! pip install farasapy
# ! pip install pyarabic
# ! git clone https://github.com/UBC-NLP/marbert
# ! git clone https://github.com/aub-mind/arabert
! pip install datasets
# ! pip install huggingface_hub
# ! apt install git-lfs
# ! git config --global user.em... | github_jupyter |
<a href="https://colab.research.google.com/github/Shantanu9326/Data-Science-Portfolio/blob/master/911_Calls_Project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 911 Calls Capstone Project
For this capstone project we will be analyzing some 911... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
```
import os
import numpy as np
import ipywidgets as widgets
from glmtools.io.glm import GLMDataset
from datetime import datetime, timedelta
from glmtools.test.common import get_sample_data_path
sample_path = get_sample_data_path()
samples = [
"OR_GLM-L2-LCFA_G16_s20181830433000_e20181830433200_c20181830433231.nc... | github_jupyter |
Copyright 2019 Google LLC
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed... | github_jupyter |
# Machine Learning and Topological Data Analysis
Mathieu Carriรจre, https://mathieucarriere.github.io/website/
```
import numpy as np
import gudhi as gd
import gudhi.representations
import matplotlib.pyplot as plt
```
In this notebook, we will see how to efficiently combine machine learning and topological data analy... | github_jupyter |
This notebook was prepared by [wdonahoe](https://github.com/wdonahoe). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Implement a function that groups identical items based on their order in the list.
* [Constraints](#Constraints... | github_jupyter |
# Transfer Learning
*by Marvin Bertin*
<img src="../images/keras-tensorflow-logo.jpg" width="400">
# Using Transfer Learning to Train an Image Classification Model
Deep learning allows you to learn features automatically from the data. In general this requires a lot of training examples, especially for problems wher... | github_jupyter |
# Introduction to CT
In this exercise sheet we will get to know the Computed Tomography reconstruction problem
## Load Data
```
import torch
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
%matplotlib inline
batch_size = 4
# datasets (MNIST)
transform_test = transforms.Compose([
tr... | github_jupyter |
<a href="https://colab.research.google.com/github/amandaleonel/deep-learning-v2-pytorch/blob/master/intro-to-pytorch/Part%202%20-%20Neural%20Networks%20in%20PyTorch%20(Exercises).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neural networks with... | github_jupyter |
# ้ๅญใใผใชใจๅคๆ
ๅคๅ
ธ็ใชใใผใชใจๅคๆใฏๆณขใไฟกๅทใฎ่งฃๆใซใใใฆ้่ฆใชใใผใซใงใใใ้ขๆฐใใใใใ็ฐใชใๅจๆณขๆฐใๆใคๆๅใซๅ่งฃใใพใใ
ใใฎ้ขๆฃ็ใชๅฏพๅฟใงใใ้ขๆฃใใผใชใจๅคๆใฏใ$n$ ๅใฎ่ค็ด ๆฐ $x_0,\ldots,x_{N-1}$ ใซไฝ็จใใๆฌกๅผใฎใใใซใๅฅใฎ $n$ ๅใฎ่ค็ด ๆฐๅ $\tilde x_0,\ldots,\tilde x_{N-1}$ ใธใจๅคๆใใพใใ
$$\tilde x_k = \sum_{y=0}^{N-1}e^{-\frac{2\pi ikn}N} \cdot x_k$$
$n$ ๅใฎ้ๅญใใใใซๅฏพใใ้ๅญใใผใชใจๅคๆ๏ผไธ่ฌใซQFTใจ็ฅใใพใ๏ผใฏๅๅบๅบ็ถๆ
$x\in \{0,1\}^n$ ... | github_jupyter |
# Implementing FIR filters
<div align="right"><a href="https://people.epfl.ch/paolo.prandoni">Paolo Prandoni</a>, <a href="https://www.epfl.ch/labs/lcav/">LCAV, EPFL</a></div>
<br />
Digital filters are fully described by their constant-coefficient difference equation (CCDE) and a CCDE can be easily translated into a ... | github_jupyter |
```
import sys
sys.path.append('../')
import soynlp
print(soynlp.__version__)
```
soynlp 0.0.46+ ์์๋ soynlp.noun.LRNounExtractor ๋ฅผ ๋ณด์ํ LRNounExtractor_v2 ๋ฅผ ์ ๊ณตํฉ๋๋ค.
version 2 ์์๋ (1) ๋ช
์ฌ ์ถ์ถ์ ์ ํ์ฑ์ ๋์์ผ๋ฉฐ, (2) ํฉ์ฑ๋ช
์ฌ์ ์ธ์์ด ๊ฐ๋ฅํฉ๋๋ค. ๋ํ (3) ๋ช
์ฌ์ ๋น๋๋ฅผ ์ ํํ ๊ณ์ฐํฉ๋๋ค.
์ฌ์ฉ๋ฒ์ version 1 ๊ณผ ๋น์ทํฉ๋๋ค. train_extract ํจ์๋ฅผ ํตํ์ฌ ๋ช
์ฌ ์ ์๋ฅผ ๊ณ์ฐํ ์ ์์ต๋๋ค. verb... | github_jupyter |
# Imports
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import patches,patheffects
import torch
from torchvision import datasets,transforms,models
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader , Dataset
import torchvision.tran... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
import scipy.stats as ss
from pandas_datareader import DataReader
from datetime import datetime
# Make plots larger
plt.rcParams['figure.figsize'] = (15, 9)
facebook = DataReader('FB', 'yahoo', dat... | github_jupyter |
# **Load Data**
```
#mount google drive home directory
from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive
#data analysis and visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
from pandas.plotting import scatter_mat... | github_jupyter |
<a href="https://colab.research.google.com/github/kalz2q/mycolabnotebooks/blob/master/arabictable.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title
%%html
<style>
table {
border-collapse: collapse;
border: 2px solid rgb(200, 200,... | github_jupyter |
# LFPCA through all conditions of anesthetized monkey
to run the monkey data through the analysis pipeline <br>
eyes open, eyes closed, and anesthesized <br>
write the code such that running through the notebook ONCE will reproduce all the results for all 3 datasets
visualize the results and compare the different con... | github_jupyter |
# Driver Assist Machine Learning Using RCNN-Masking
### What is Mask R-CNN:
- R-CNN stands for "Regions with CNN features", CNN stands for "Convolutional Neural Network".
- R-CNN grabs parts of an image (or region) as a bounding box, and computes each region for CNN features,
it then classifies each region to determ... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import graspy as gp
res_df1 = pd.read_csv("../results/20200305_adj_row_wise.csv")
res_df1 = res_df1[res_df1.delta != 0]
res_df1 = res_df1.sort_values(['m', 'delta'])
res_df1 = res_df1[res_df1.m <= 250]
res_df = pd.read_csv... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
#from datetime import datetime, timedelta
import datetime
import xarray as xr
#from math import atan2, log
import sys
import uuid
import matplotlib.pyplot as plt
import seawater as sw
import cartopy.crs as ccrs # import projections
import cartopy.f... | github_jupyter |
### You are looking at data from an e-commerce website. The site is very simple and has just 4 pages:
#### The first page is the home page. When you come to the site for the first time, you can only land on the home page as a first page.
#### From the home page, the user can perform a search and land on the search pa... | github_jupyter |
<a href="https://colab.research.google.com/github/google/applied-machine-learning-intensive/blob/master/content/04_classification/08_video_processing_project/colab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#### Copyright 2020 Google LLC.
```
... | github_jupyter |
<center>
<h1><b>Word Embedding Based Answer Evaluation System for Online Assessments (WebAES)</b></h1>
<h3>A smart system to automate the process of answer evaluation in online assessments.</h3>
<h5> LDA + BERT Model for WebAES</h5>
```
# To perform text pre-processing
import string
# Natural Language Toolkit
import ... | github_jupyter |
```
import numpy as np
import os
import matplotlib.pyplot as plt
%matplotlib inline
from PIL import Image
from scipy.stats import truncnorm
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
impo... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import math
from scipy import stats
import pickle
from causality.analysis.dataframe import CausalDataFrame
from sklearn.linear_model import LinearRegression
import datetime
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.font_manager as f... | github_jupyter |
# SUYASH PRATAP SINGH
# NUMPY OPERATIONS
```
# Import Numpy Library
import numpy as np
import warnings
warnings.filterwarnings("ignore")
from IPython.display import Image
```
# Numpy Array Creation
```
list1 = [10,20,30,40,50,60]
list1
# Display the type of an object
type(list1)
#Convert list to Numpy Array
arr1 = ... | github_jupyter |
```
from collections import Counter
from collections import defaultdict
import functools
from IPython.display import set_matplotlib_formats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import skimage
from skimage import io
%matplotlib inline
set_matplotlib_formats('svg'... | github_jupyter |
```
from database.strategy import Strategy
from database.sec import SEC
from database.market import Market
from transformer.model_transformer import ModelTransformer
from transformer.product_transformer import ProductTransformer
from transformer.predictor_transformer import PredictorTransformer
from preprocessor.model_... | github_jupyter |
# Data Processing and Analysis
Data Processing is the most important and most time consuming component of the overall lifecycle of any Machine Learning project.
In this notebook, we will analyze a dummy dataset to understand different issues we face with real world datasets and steps to handle the same.
## Utilitie... | github_jupyter |
```
# default_exp numpy
```
# 00_Numpy
> Building an example `Dataset` and `DataLoader` with `NumPy`
```
#hide
from nbdev.showdoc import *
#export
from fastai2.tabular.all import *
```
For our data we'll first utilize `TabularPandas` for pre-processing. One potential is to use `TabularPandas` for pre-processing onl... | github_jupyter |
# ะกะฟัะฐะฒะบะฐ
API Cross Web ะพัััะตััะฒะปัะตั ัะฐััะตั ะดะฐะฝะฝัั
ะฟัะพะตะบัะฐ Cross Web (ะธะทะผะตัะตะฝะธะต ะฐัะดะธัะพัะธะธ ะฒ ะธะฝัะตัะฝะตัะต).
ะะฑัะฐัะฐัััั ะบ API Cross Web ะฑัะดะตะผ ั ะฟะพะผะพััั Jupyter Notebook, ะดะปั ััะพะณะพ ะฝะตะพะฑั
ะพะดะธะผะพ ะฒะปะฐะดะตัั ะฝะตะบะพัะพััะผะธ ัะตัะผะธะฝะฐะผะธ.
ะะธะถะต ะพะฟะธัะฐะฝั ะพัะฝะพะฒะฝัะต ะธะท ะฝะธั
.
### usetype - ัะธะฟ ะฟะพะปัะทะพะฒะฐะฝะธั ะธะฝัะตัะฝะตัะพะผ
ะะพะทะผะพะถะฝัะต ะฒะฐัะธะฐะฝัั:
- 1 - ... | github_jupyter |
# Text in Python
Fรผr die computergestรผtzte Textanalyse sind Texte zunรคchst nur eine Aneinanderreihung von Buchstaben oder genauer: Zeichen. Diese Art von Text, die ohne Formatierung wie Schriftart, Schritgrรถรe oder Fettungen auskommt, wird als โplain textโ bezeichnet. Plain text erhรคlt man etwa, wenn man ein Word-Doku... | github_jupyter |
```
# Update sklearn to prevent version mismatches
# !conda install scikit-learn
# !conda update scikit-learn
# !conda install joblib
# !conda update joblib
import pandas as pd
import numpy as np
import pprint
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
```
# Read the CSV
```
df... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import h5py
def load_data():
train_dataset = h5py.File(r'./dataset/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:])
train_set_y_orig = np.array(train_dataset["train_set_y"][:])
test_dataset = h5py.File(r'.... | github_jupyter |
# First Study on Brazilian Cities Transparency Portal
In this dataset we have a population projection for each Brazilian city in the year of 2013.
```
import pandas as pd
import numpy as np
# We first collected the data with population estimatives,
# we can use it later to do some comparisions or to use it later
cit... | github_jupyter |
# ๅๅปบ่ชๅฎไน่ฝฌๆขๅจ
```
import nltk
nltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger'])
import re
import numpy as np
import pandas as pd
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer
from nltk import pos_tag
from sklearn.pipeline import Pipeline, FeatureUnion
from... | github_jupyter |
# Exercise Solutions
---
## Pythonic Exercises
1. Create a list of your favourite superheros and another list of their secret identities.
1. Convert your two lists into a dictionary. (Can you do it in one line?)
1. Remove one of your heroes and add a villain to your dictionary.
1. Add a character that ha... | github_jupyter |
[](https://pythonista.mx)
# Expresiones regulares.
Las expresiones regulares son parte de los lenguajes formales y corresponden a una secuencia de caracteres que definen un patrรณn.
Mediante el uso de expresiones regulares, es posible buscar patrones dentro de un flujo de texto.
... | github_jupyter |
# Lasso Regression with StandardScaler
This Code template is for the regression analysis using a Lasso Regression and the feature rescaling technique StandardScaler in a pipeline.
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt ... | github_jupyter |
# Decision Analysis
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
```
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
imp... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# ๐ Exercise M6.01
The aim of this notebook is to investigate if we can tune the hyperparameters
of a bagging regressor and evaluate the gain obtained.
We will load the California housing dataset and split it into a training and
a testing set.
```
from sklearn.datasets import fetch_california_housing
from sklearn.m... | github_jupyter |
<a href="https://colab.research.google.com/github/microprediction/timemachines/blob/main/CompareToNaive.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install timemachines
!pip install --upgrade statsmodels
```
# Example of comparing agai... | github_jupyter |
```
import urllib
import json
import numpy as np
import itertools
import torch
from os import listdir
import os
from os.path import isfile, join
import time
import requests
import json
import pickle
import base64
from io import BytesIO
from PIL import Image
from tqdm import tqdm
import cv2
from matplotlib import pyplo... | github_jupyter |
```
import numpy
import numpy as np
import numpy.random as rn
import matplotlib.pyplot as plt # to plot
import matplotlib as mpl
from scipy import optimize # to compare
'''
DESCRIPTION
Calculates Annual Energy Production (AEP) of a Wind Farm
============================================================ ... | github_jupyter |
<a href="https://colab.research.google.com/github/Lindronics/honours_project/blob/master/notebooks/classification/Image_classification_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Image classification test 1
The purpose of this test is to:
*... | github_jupyter |
```
import sys, os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import bayes_mvs as bayesest
import time
sys.path.insert(0, '../../../PyEcoLib')
from PopSimulator import PopSimulator
from simulator import Simulator
%matplotlib inline
%matplotlib inline
mean_size = 3 # ... | github_jupyter |
**Hidden Markov models for cracking codes**
In this exercise you have to make a partially built HMM work and use it to solve some simple substitution ciphers. Plaintext data is provided in 'plaintext' directory. Encrypted data is in 'encrypted'. Some of the texts were originally English some of them were Russian; the ... | github_jupyter |
<a href="https://apssdc.in"><img src="https://camo.githubusercontent.com/e7501c5948d48f88dad8ab2ab6bd448e1cfd6c79/68747470733a2f2f64726976652e676f6f676c652e636f6d2f75633f6578706f72743d646f776e6c6f61642669643d3135414b51365f2d42697857344b366d4c36525070684635454b58715946327a6a" width="900" align="center"></a>
<h1><center... | github_jupyter |
# Training Entity Coding Models (SNOMED example)
```
import os
import json
import string
import numpy as np
import pandas as pd
import sparknlp
import sparknlp_jsl
from sparknlp.base import *
from sparknlp.util import *
from sparknlp.annotator imp... | github_jupyter |
# Happy Valentine's Day
<img src = 'Valentine.jpg' width="400">
<font color=red>**Let us celebrate Valentine's Day by doing something cool, e.g.,**</font>
# Lithofacies classification and prediction using support vector machines
In this exercise, we will train a support vector machine classifier to predict facies us... | github_jupyter |
# Time Series Forecasting in Python
https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
## Loading and Handling Time Series in Pandas
```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from statsmodels.tsa.stattools import adfuller
from statsm... | github_jupyter |
<a href="https://colab.research.google.com/github/cxbxmxcx/Evolutionary-Deep-Learning/blob/main/EDL_5_DE_HPO_PCA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Setup
```
#@title Install DEAP
!pip install deap --quiet
#@title Defining Imports
#nu... | github_jupyter |
# Now You Code 4: Sentiment v1.0
Let's write a basic sentiment analyzer in Python. Sentiment analysis is the act of extracting mood from text. It has practical applications in analyzing reactions in social media, product opinions, movie reviews and much more.
The 1.0 version of our sentiment analyzer will start with ... | github_jupyter |
# Heteroskedastic Likelihood and Multi-Latent GP
## Standard (Homoskedastic) Regression
In standard GP regression, the GP latent function is used to learn the location parameter of a likelihood distribution (usually a Gaussian) as a function of the input $x$, whereas the scale parameter is considered constant. This is... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#export
from fastai.data.all import *
from fastai.optimizer import *
from fastai.learner import *
#hide
from nbdev.showdoc import *
#default_exp metrics
# default_cls_lvl 3
```
# Metrics
> Definition of the metrics that can be use... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# TensorTrade - Renderers and Plotly Visualization Chart
## Data Loading Function
```
import pandas as pd
def load_csv(filename):
df = pd.read_csv('data/' + filename, skiprows=1, parse_dates=['date'])
df.drop(columns=['symbol', 'volume_btc'], inplace=True)
# Fix timestamp form "2019-10-17 09-AM" to "2019... | github_jupyter |
General outline:
I want to take in an array (later image) representation of a scrabble board/hand tiles, and output the best play.
Naive best play: the highest scoring play
Goal best play: the play that maximizes your winning chances (takes into account the opponent's potential score)
* You want to maximize how y... | github_jupyter |
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