text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
<img src="logos/Icos_cp_Logo_RGB.svg" width="400" align="left"/>
<img src="logos/NOAA_logo.png" width="90" align="right"/>
<a id='introduction'></a>
<br>
# Curve fitting methods for CO$_2$ time series
This notebook includes examples of curve fitting methods for time series. For more detailed information regarding ... | github_jupyter |
```
# 3rd Party
from baybars.timber import get_logger
import numpy as np
import tensorflow as tf
LABEL_MAP = {
0: 'T-shirt/top',
1: 'Trouser',
2: 'Pullover',
3: 'Dress',
4: 'Coat',
5: 'Sandal',
6: 'Shirt',
7: 'Sneaker',
8: 'Bag',
9: 'Ankle boot',
}
class UnsupportedModeException(Exception):
p... | github_jupyter |
## _*BeH2 plots of various orbital reduction results*_
This notebook demonstrates using the Qiskit Aqua Chemistry to plot graphs of the ground state energy of the Beryllium Dihydride (BeH2) molecule over a range of inter-atomic distances using ExactEigensolver. Freeze core reduction is true and different virtual orbit... | github_jupyter |
# 第二讲 - 矩阵消元及其与矩阵乘法的关系
- 消元法解方程组
- 矩阵简化
- 反向替代
- 矩阵乘法
## 消元法解方程组
$$x+2y+z=2\quad(1)\\3x+8y+z=12\quad(2)\\4y+z=2\quad(3)$$
提取出矩阵：
$$A=\begin{bmatrix}1&2&1\\3&8&1\\0&4&1\end{bmatrix}$$
下面先做一些初始化工作：
```
import numpy as np
from sympy import *
init_printing()
x, y, z = symbols('x y z')
lhs = (x + 2*y + z, 3*x + 8*y +... | github_jupyter |
## PCA and other test on the computed Dataframe
```
import pandas as pd
import operator
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns; sns.set()
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.pr... | github_jupyter |
```
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), 'day 11'))
print(os.getcwd())
except:
pass
from computerrefractored import Computer
import matplotlib.pyplot as plt
from collections import defaultdict
import numpy as np
from collections import namedtuple
def dimensions(obj):
minim =... | github_jupyter |
```
# default_exp desc.stats
```
# Exploration Statistics
> This module comprises all the functions for calculating descriptive statistics.
```
!pip install dit
!pip install sentencepiece
# export
# Imports
from scipy.stats import sem, t, median_abs_deviation as mad
from statistics import mean, median, stdev
import ... | github_jupyter |
```
import pickle
import boto3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pyspark.sql import SparkSession
sc = spark.sparkContext
from pyspark.sql import SQLContext
from pyspark.sql import functions as F
from pyspark.sql.window import Window
from pyspark.sql.types import IntegerType, St... | github_jupyter |
```
# Copyright 2019 Google Inc. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import cPickle as pickle
import codecs
import skfuzzy as fuzz
import time
from matplotlib import pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os
import pickle
from glob import glob
import re
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import pairwise_distances
import settings as conf
output_dir = os.path.joi... | github_jupyter |
# Movie Ratings Network
This notebook is used to create the movie networks based on the ratings. It use the same approach as suggested in [[1](https://arxiv.org/pdf/1408.1717.pdf)]
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
DATA_PATH = '../data/ml-100k-convert/'
GENERATED_PATH = '../g... | github_jupyter |
# Setup
## Imports
```
import os.path
from glob import glob
from tqdm import tqdm_notebook
from sklearn.metrics import confusion_matrix
from vaiutils import path_consts, smooth_plot, plot_images
from vaidata import pickle_load, pickle_dump
from keras.preprocessing.text import Tokenizer
from keras.utils.np_utils impo... | github_jupyter |
# Models and Maps
## Models
Let's again consider the car dataset from second notebook.
In that notebook we plotted *qsec* as a function of *hp*. However we might be interested a better model. Let's load the data.
```
library(tidyverse)
data(mtcars)
mtcars_tbl <- as_tibble(rownames_to_column(mtcars,var='model'))
... | github_jupyter |
```
import modin.pandas as pd
import nums
import nums.numpy as nps
nums.init()
```
# Preparation
### Load and preprocess dataset with Modin.
```
%%time
higgs_train = pd.read_csv("training.zip")
higgs_train.loc[higgs_train['Label'] == 'b', 'Label'] = 0
higgs_train.loc[higgs_train['Label'] == 's', 'Label'] = 1
higgs_t... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Use Azure Machine ... | github_jupyter |
```
import pandas as pd
import os
import pickle
import numpy as np
import scipy.sparse as sp
import scipy.io as spio
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import isolearn.io as isoio
import isolearn.keras as iso
import scipy.optimize as spopt
from scipy.stats import pearsonr
from analyze_rand... | github_jupyter |
## Batched example 2
This notebook is the second of a series that shows how [GSSHA_Workflow.ipynb](../GSSHA_Workflow.ipynb) can be parameterized at the command line that builds on [GSSHA_Workflow_Batched_Example1](GSSHA_Workflow_Batched_Example1.ipynb). This notebook uses the same principles as the first example but m... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# BlackHoles@Home Tutorial: Compiling the `BOINC` server on... | github_jupyter |
```
# Copyright 2020 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 writi... | github_jupyter |
# Calculate performance of signature
Gregory Way, 2021
I previously identified a series of morphology features that were significantly different between sensitive and resistant clones.
I also applied this signature to all profiles from training, testing, validation, and holdout sets.
Here, I evaluate the performance ... | github_jupyter |
# Parametrized Sequences
```
import numpy as np
import pulser
from pulser import Pulse, Sequence, Register
from pulser.waveforms import RampWaveform, BlackmanWaveform, CompositeWaveform
from pulser.devices import Chadoq2
```
From simple sweeps to variational quantum algorithms, it is often the case that one wants to ... | github_jupyter |
# Saddle plot
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import bioframe
import cooler
import cooltools
import cooltools.eigdecomp
import cooltools.expected
import cooltools.saddle
# download a Hi-C dataset from Schwarzer et.al. "Two independent modes of chromosome organization are r... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import argparse
import time
import os
#setup training parameters
parser = argparse.ArgumentParser(description='PyTorch MNIST Training')
parser.add_argument('--batch-size', typ... | github_jupyter |
# Evaluation
The evaluation strategy is as follows. There are 30 classes of images in the RSICD dataset. We construct a synthetic set of captions that use the pattern "An arial photograph of a `class_type`" for each of the 30 classes. We feed each image and the synthetic captions into the model under evaluation, and g... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.svm import SVC
from sklearn.linear_model... | github_jupyter |
```
# from utils import *
import os
os.chdir("../../scVI/")
os.getcwd()
import pickle
import numpy as np
import pandas as pd
from copy import deepcopy
save_path = '../CSF/Notebooks/'
celllabels = np.load(save_path + 'meta/celllabels.npy')
celltypes, labels = np.unique(celllabels,return_inverse=True)
# from numpy i... | github_jupyter |
## BiRNN Overview
<img src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/191dd7df9cb91ac22f56ed0dfa4a5651e8767a51/1-Figure2-1.png" alt="nn" style="width: 600px;"/>
References:
- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neur... | github_jupyter |
```
# connect to google colab
from google.colab import drive
drive.mount("/content/drive")
# base path
DATA_PATH = './drive/MyDrive/fyp-code/codes/data/emotion_classification/'
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix, cohen_kappa_score
import seaborn as sns
```
## Import the... | github_jupyter |
# test note
* jupyterはコンテナ起動すること
* テストベッド一式起動済みであること
```
!pip install --upgrade pip
!pip install --force-reinstall ../lib/ait_sdk-0.1.3-py3-none-any.whl
from pathlib import Path
import pprint
from ait_sdk.test.hepler import Helper
import json
# settings cell
# mounted dir
root_dir = Path('/workdir/root/ait')
ait_n... | github_jupyter |
# Relation extraction with BERT
---
The goal of this notebook is to show how to use [BERT](https://arxiv.org/abs/1810.04805)
to [extract relation](https://en.wikipedia.org/wiki/Relationship_extraction) from text.
Used libraries:
- [PyTorch](https://pytorch.org/)
- [PyTorch-Lightning](https://pytorch-lightning.readth... | github_jupyter |
```
import pandas as pd
import numpy as np
import nltk
import json
import re
from sentence_transformers import SentenceTransformer
from itertools import islice, cycle
from pynndescent import NNDescent
from collections import Counter
from functools import reduce
nltk.download('stopwords')
nltk.download('punkt')
item_da... | github_jupyter |
# Part 12.2: Introduction to Q-Learning
Q-Learning is a foundational technique upon which deep reinforcement learning is based. Before we explore deep reinforcement learning, it is essential to understand Q-Learning. Several components make up any Q-Learning system.
* **Agent** - The agent is an entity that exists ... | github_jupyter |
# Groupby と Resample
- 参照
- [Group by: split-apply-combine — pandas 1.4.1 documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine)
- [Resampling — pandas 1.4.1 documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resamp... | github_jupyter |
# Python Tutorial for Data Science
## Introduction to Machine Learning: Classification with k-Nearest Neighbors
#### (Adapted from Data 8 Fall 2017 Project 3)
#### Patrick Chao 1/21/18
# Introduction
The purpose of this notebook is to serve as an elementary python tutorial introducing fundamental data science concept... | github_jupyter |
# Python Basics
Prepared by: Nickolas K. Freeman, Ph.D.
This notebook provides a very basic introduction to the Python programming language. The following description of the Python language was taken from https://en.wikipedia.org/wiki/Python_(programming_language) on 1/6/2018, and serves as a good introduction to the ... | github_jupyter |
# T2 - Calibration
Models are simplifications of the real world, and quantities in the model (like the force of infection) represent the aggregation of many different factors. As a result, there can be uncertainty as to what value of the parameters most accurately reflects the real world - for instance, the population... | github_jupyter |
# Module 1: Introduction to Exploratory Analysis
<a href="https://drive.google.com/file/d/1r4SBY6Dm6xjFqLH12tFb-Bf7wbvoIN_C/view" target="_blank">
<img src="http://www.deltanalytics.org/uploads/2/6/1/4/26140521/screen-shot-2019-01-05-at-4-48-15-pm_orig.png" width="500" height="400">
</a>
[(Page 17)](https://driv... | github_jupyter |
# Pseudomonas experiment level analysis
Main notebook to run experiment-level simulation experiment using *P. aeruginosa* gene expression data.
```
%load_ext autoreload
%autoreload 2
import os
import sys
import ast
import pandas as pd
import numpy as np
import random
from plotnine import (ggplot,
... | github_jupyter |
```
!pip install -Uq catalyst gym
```
# Seminar. RL, DQN.
Hi! In the first part of the seminar, we are going to introduce one of the main algorithm in the Reinforcment Learning domain. Deep Q-Network is the pioneer algorithm, that amalmagates Q-Learning and Deep Neural Networks. And there is small review on gym envir... | github_jupyter |
<a href="https://colab.research.google.com/github/Espanta/handson-ml/blob/master/Learning3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Your name:
<pre> Your Name </pre>
### Collaborators:
<pre> Collaborators </pre>
```
import numpy as np... | github_jupyter |
# Parameter plotting with LiionDB
In this notebook we will show how to plot and compare parameters in a loop.
A simplified interactive GUI is available online at [**www.liiondb.com**](www.liiondb.com)
---
* LiionDB is a database of DFN-type battery model parameters that accompanies the review manuscript: [**Parameter... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
i... | github_jupyter |
```
import os
import time
import tensorflow as tf
import numpy as np
from glob import glob
import datetime
import random
from PIL import Image
import matplotlib.pyplot as plt
from numpy import savetxt
import pandas as pd
import sys
%matplotlib inline
array_sum = []
from google.colab import drive
drive.mount('/content/d... | github_jupyter |
# Tutorial 7: Estimator
## Overview
In this tutorial, we will talk about:
* [Estimator API](#t07estimator)
* [Reducing the number of training steps per epoch](#t07train)
* [Reducing the number of evaluation steps per epoch](#t07eval)
* [Changing logging behavior](#t07logging)
* [Monitoring intermediate... | github_jupyter |
# PyAutoGUI——让所有GUI都自动化
本教程译自大神[Al Sweigart](http://inventwithpython.com/)的[PyAutoGUI](https://pyautogui.readthedocs.org/)项目，Python自动化工具，更适合处理GUI任务，网页任务推荐：
- [Selenium](https://selenium-python.readthedocs.org/)+Firefox记录（Chromedriver和Phantomjs也很给力，Phantomjs虽然是无头浏览器，但有时定位不准），然后用Python写单元测试
- [request](http://www.python... | github_jupyter |
```
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.utils import np_utils
batch_size = 128
num_classes = 10
epochs = 10
# the data, shuffled and split between train and test sets
(x_train, y_trai... | github_jupyter |
# Creating a Sampled Dataset
**Learning Objectives**
- Sample the natality dataset to create train/eval/test sets
- Preprocess the data in Pandas dataframe
## Introduction
In this notebook we'll read data from BigQuery into our notebook to preprocess the data within a Pandas dataframe.
```
PROJECT = "cloud-training... | github_jupyter |
# Étiquetage morpho-syntaxique
## Définition
Opération par laquelle un programme associe automatiquement à un mot des étiquettes grammaticales, comme :
- le genre
- le nombre
- la partie du discours (catégorie)
- …
Elle intervient après celle de segmentation en mots et se positionne comme pré-requis pour l’analyse s... | github_jupyter |
# Initial Setup
```
import pyspark
import pandas as pd
import numpy as np
from pyspark.ml.recommendation import ALSModel, ALS
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from sklearn.preprocessing import OneHotEncoder, StandardScaler
spark = pyspark.sql.Spar... | github_jupyter |
```
import numpy as np
import pandas as pd
class PastSampler:
'''
Forms training samples for predicting future values from past value
'''
def __init__(self, N, K, sliding_window = True):
'''
Predict K future sample using N previous samples
'''
self.K = K
s... | github_jupyter |
## Using Random EMA to check End-of-Day: Exploratory Data Analysis
- This notebook is dedicated to understanding End-of-Day EMA using Random EMA
- For every Random EMA where the response is 'Yes', check to see
+ What is the fraction where the user clicked correct hour
+ What is the fraction where the user clic... | github_jupyter |
<img src="http://akhavanpour.ir/notebook/images/srttu.gif" alt="SRTTU" style="width: 150px;"/>
[](https://notebooks.azure.com/import/gh/Alireza-Akhavan/class.vision)
# <div style="direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma">عملیات بی... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import scipy
from scipy import ndimage
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
x = mnist.data
y = mnist.target
e_k = np.zeros_like(x)
s_k = np.zeros_like(x)
n_k = np.zeros_like(x)
nw_k = np.zeros_like(x)
ne_k = np.zeros_like(x)
s... | github_jupyter |
```
#notebook to fetch reanalysis used in example
import cdsapi
import pyart
import os
import sys
import netCDF4
import xarray as xr
from matplotlib import pyplot as plt
%matplotlib inline
#NOTE.. you need a key from ECMWF
#populate ~/.cdsapirc with
#url: https://cds.climate.copernicus.eu/api/v2
#key: YOURKEYHASH
de... | github_jupyter |
### 1. Setting up the meta-BO environment
```
from matplotlib import pyplot as plt
from meta_bo.meta_environment import RandomMixtureMetaEnv
import numpy as np
# setup meta-learning / meta-bo environment
rds = np.random.RandomState(456)
meta_env = RandomMixtureMetaEnv(random_state=rds)
# sample functions / BO tasks ... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm as tqdm
%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
# from google.co... | github_jupyter |
# 3M1 Introduction to optimization
Luca Magri (lm547@cam.ac.uk), office ISO-44, Hopkinson Lab.
(With many thanks to Professor Gábor Csányi.)
[Booklist](https://www.vle.cam.ac.uk/mod/book/view.php?id=364091&chapterid=49051):
- Antoniou, A. & Lu, W.-S. Practical Optimization: Algorithms and Engineering Applications, ... | github_jupyter |
# Taxonomy of time series learning tasks
* What is machine learning with time series?
* How is it different from standard machine learning?
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
```
## Learning objectives
You'll learn about
* different time series learning t... | github_jupyter |
# Lesson 5: the trouble with slope area
*This lesson has been written by Simon M. Mudd at the University of Edinburgh*
*Last update 30/09/2021*
In the past few lessons, we have learned:
* Channels tend to have a higher gradient near their headwaters (i.e., parts of the network with low drainage area).
* If the ... | github_jupyter |
```
# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import sys
sys.path.append('..')
import pdb, sys, inspect
from enum import Enum
import pandas as pd
import torch
from transformers import *
from fastai2.text.all import *
torch.cuda.set_device(1)
print(f'Using GPU #{torch.cuda.current_device()}: {torch.cuda... | github_jupyter |
<a name="top"></a>
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png" alt="Unidata Logo" style="height: 98px;">
</div>
<h1>Plotting on a Map with CartoPy</h1>
<h3>Unidata Python ... | github_jupyter |
```
%use dataframe, khttp
// to see autogenerated code, uncomment the line below:
//%trackExecution -generated
```
## Get Data
```
val response = khttp.get("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")
val cleanedText = response.text.replace("\"Molly\"", "Molly").replace("row.names", "row").r... | github_jupyter |
# Automating GIS-processes - Final work
**Aim of the work:**
Aim of the final assignment is to apply the programming techniques and skills that we have learned during the course and create a GIS tool called *AccessHandler* (see below instructions). You can choose yourself what tools / techniques / modules you want to... | github_jupyter |
```
%matplotlib widget
import glob
import os
from mpl_toolkits.axes_grid1 import make_axes_locatable
from astropy.io import fits
from astropy.stats import sigma_clipped_stats
from astropy.table import Table
from astropy.visualization import ImageNormalize, SqrtStretch, LogStretch, LinearStretch, ZScaleInterval, Manual... | github_jupyter |
```
import collections as cl
import faiss
import numpy as np
import torch as th
from misc import load_sift, save_sift
```
### Load vectors extracted from fasttext
```
xq = load_sift('../data/siftLSHTC/predictions.hid.fvecs', dtype=np.float32)
xb = load_sift('../data/siftLSHTC/predictions.wo.fvecs', dtype=np.float32... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import glob
import numpy as np
from collections import defaultdict
import pickle
import os
dataset_name = 'fma_small'
folder = "../exp/" + dataset_name
selected = {'ytc': 5.5, 'fma_small': 7, 'gtzan': 5.8}[dataset_name]
df = pd.read_csv(os.path.join(folder, "tf.cs... | github_jupyter |
# Particle Swarm Optimization Algorithm (explained with Python!)
[SPOILER] We will be using the [Particle Swarm Optimization algorithm](https://en.wikipedia.org/wiki/Particle_swarm_optimization) to obtain the minumum of some test functions
First of all, let's import the libraries we'll... | github_jupyter |
### Evaluating Used Cars with Classification
#### Introduction
In recent years, used car market is getting larger and larger. Many people begin purchasing used cars instead of new cars, since used cars are always cheaper than new cars, and a lot of used cars really have good reliability. However, there are still a bun... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0";
```
# QA-Based Information Extraction
As of v0.28.x, **ktrain** now includes a “universal” information extractor, which uses a Question-Answering model to extrac... | github_jupyter |
```
import sys, os
if 'google.colab' in sys.modules:
# https://github.com/yandexdataschool/Practical_RL/issues/256
!pip uninstall tensorflow --yes
!pip uninstall keras --yes
!pip install tensorflow-gpu==1.13.1
!pip install keras==2.2.4
if not os.path.exists('.setup_complete'):
!wget... | github_jupyter |
```
from tensorflow.keras.layers import Dense
Dense(10, activation="relu", kernel_initializer="he_normal")
from tensorflow.keras.initializers import VarianceScaling
from tensorflow.keras.layers import Dense
he_avg_init = VarianceScaling(scale=2., mode='fan_avg',
distribution='uniform')
D... | github_jupyter |
# Transfer Learning
A Convolutional Neural Network (CNN) for image classification is made up of multiple layers that extract features, such as edges, corners, etc; and then use a final fully-connected layer to classify objects based on these features. You can visualize this like this:
<table>
<tr><td rowspan=2 st... | github_jupyter |
# Kernel PCA
## Importing the libraries
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## Importing the dataset
```
dataset = pd.read_csv('Wine.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
```
## Splitting the dataset into the Training set and Test set
```
f... | github_jupyter |
# Metacells Vignette
This vignette demonstrates step-by-step use of the metacells package to analyze scRNA-seq data. The latest version of this vignette is available in [Github](https://github.com/tanaylab/metacells/blob/master/sphinx/Manual_Analysis.rst).
## Preparation
First, let's import the Python packages we'll... | github_jupyter |
## cloudFPGA Studio
### Case study: Harris Corner Detector (Computer Vision) - NumpPy version with camera loop
### You don't need FPGA knowledge, just basic Python syntax !!!
Note: Assuming that the FPGA is already flashed
Configure the Python path to look for FPGA aceleration library
```
import time
import sys
impo... | github_jupyter |
<a href="https://colab.research.google.com/github/maxigaarp/Gestion-De-Datos-en-R/blob/main/Clase_7_y_8_Depuracion_en_SQL.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
system("gdown https://drive.google.com/uc?id=1q089qSqKr7Ak29lUkzKSWjm2pcb_j... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
Training and Testing Data
=====================================
To evaluate how well our supervised models generalize, we can split our data into a training and a test set:
<img src="figures/train_test_split_matrix.svg" width="100%">
```
... | github_jupyter |
# Introduction
In this notebook, we'll assign documents to domains in RDoC with the highest Dice similarity of their brain structures and mental function terms.
# Load the data
```
import pandas as pd
import numpy as np
import sys
sys.path.append("..")
import utilities, partition
framework = "rdoc"
```
## Brain ac... | github_jupyter |
# IBM Db2 Event Store - Data Analytics using Python API
IBM Db2 Event Store is a hybrid transactional/analytical processing (HTAP) system. It extends the Spark SQL interface to accelerate analytics queries.
This notebook illustrates how the IBM Db2 Event Store can be integrated with multiple popular scientific tool... | github_jupyter |
<img style="float: center;" src="images/CI_horizontal.png" width="600">
<center>
<span style="font-size: 1.5em;">
<a href='https://www.coleridgeinitiative.org'>Website</a>
</span>
</center>
Ghani, Rayid, Frauke Kreuter, Julia Lane, Adrianne Bradford, Alex Engler, Nicolas Guetta Jeanrenaud, Graham Henke... | github_jupyter |
```
##### from collections import OrderedDict
## Pandas
import pandas as pd
from IPython.display import display
from IPython.display import HTML
from pandas.io.json import json_normalize
pd.set_option('max_colwidth',255)
pd.set_option('max_columns',10)
#### Prep for the presentation
### Authenticate to Ambari
#### Py... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt. clf()
plt.figure(figsize=(15,10))
meanViola1 = np.array([76.712204564012, 271.962595069704, 104.464056106481])
medianViola1 = np.array([87.2101204224871, 267.298392954475, 73.4574594321263])
firstQtViola1 = np.array([66.392424612713, 188.28... | github_jupyter |
# Assignment 3
All questions are weighted the same in this assignment. This assignment requires more individual learning then the last one did - you are encouraged to check out the [pandas documentation](http://pandas.pydata.org/pandas-docs/stable/) to find functions or methods you might not have used yet, or ask quest... | github_jupyter |
```
import tabint
from tabint.utils import *
from tabint.dataset import *
from tabint.feature import *
from tabint.pre_processing import *
from tabint.visual import *
from tabint.learner import *
from tabint.interpretation import *
from tabint.inference import *
from tabint.model_performance import *
data = pd.read_csv... | github_jupyter |
# Práctico 2: Recomendación de videojuegos
En este práctico trabajaremos con un subconjunto de datos sobre [videojuegos de Steam](http://cseweb.ucsd.edu/~jmcauley/datasets.html#steam_data). Para facilitar un poco el práctico, se les dará el conjunto de datos previamente procesado. En este mismo notebook mostraremos el... | github_jupyter |
<!--NAVIGATION-->
<| [Main Contents](Index.ipynb) |>
# Appendix: The computing Miniproject <span class="tocSkip"><a name="Apx:Miniproj"></a>
<h1>Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Objectives" data-toc-modified-id="Objectives-1">Objectives</a></span></... | github_jupyter |
<div class="contentcontainer med left" style="margin-left: -50px;">
<dl class="dl-horizontal">
<dt>Title</dt> <dd> Path Element</dd>
<dt>Dependencies</dt> <dd>Matplotlib</dd>
<dt>Backends</dt> <dd><a href='./Path.ipynb'>Matplotlib</a></dd> <dd><a href='../bokeh/Path.ipynb'>Bokeh</a></dd>
</dl>
</div>
```
import ... | github_jupyter |
<h1 align="center">TensorFlow Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from *Introduction to TensorFlow* to label images of English letters! The data you are using, <a href="http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html">notMNIST</a>, consi... | github_jupyter |
```
from jupyter_innotater import *
import numpy as np, os
```
## Save button calls your supplied Python function
```
foodfns = sorted(os.listdir('./foods/'))
targets = np.zeros((len(foodfns), 4), dtype='int') # (x,y,w,h) for each data row
def my_save_hook(uindexes):
np.savetxt("foodboxes.csv", targets, delimite... | github_jupyter |
## Plots comparison of interpretability performance for CNNs with log-based activations
Figures generated in this notebook:
- Supplementary Fig. 11
```
import os
import numpy as np
from six.moves import cPickle
import matplotlib.pyplot as plt
import helper
from tfomics import utils
results_path = os.path.join('../re... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from synthpop.census_helpers import Census
from synthpop import categorizer as cat
import pandas as pd
import numpy as np
import os
pd.set_option('display.max_columns', 500)
```
## The census api needs a key - you can register for can sign up
### http://api.census.gov/data/key_s... | github_jupyter |
#### Copyright 2017 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 writin... | github_jupyter |
```
import pandas
import numpy as np
import matplotlib.pyplot as plt; plt.rcdefaults()
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
from collections import defaultdict, Counter, OrderedDict
from operator import itemgetter
import codecs
import csv
import itertools... | github_jupyter |
<small><small><i>
All the IPython Notebooks in **Python Introduction** lecture series by Dr. Milaan Parmar are available @ **[GitHub](https://github.com/milaan9/01_Python_Introduction)**
</i></small></small>
# Python Variables and Constants
In this class, you will learn about Python variables, constants, literals and... | github_jupyter |
# Mini-batching
In its purest form, online machine learning encompasses models which learn with one sample at a time. This is the design which is used in `river`.
The main downside of single-instance processing is that it doesn't scale to big data, at least not in the sense of traditional batch learning. Indeed, proc... | github_jupyter |
```
from models.DistMult import DistMult_Lite
from models.Complex import Complex
from models.ConvE import ConvE, ConvE_args
from utils.loaders import load_data, get_onehots
from utils.evaluation_metrics import SRR, auprc_auroc_ap
import torch
import numpy as np
from sklearn.utils import shuffle
from tqdm import tqdm
... | github_jupyter |
# Quick Start Tutorial
The GluonTS toolkit contains components and tools for building time series models using MXNet. The models that are currently included are forecasting models but the components also support other time series use cases, such as classification or anomaly detection.
* 基于MXNet
* 包含了预测模型，也支持其他类型的时序预测... | github_jupyter |
# Mixup data augmentation
```
from fastai.gen_doc.nbdoc import *
from fastai.callbacks.mixup import *
from fastai.vision import *
from fastai import *
```
## What is Mixup?
This module contains the implementation of a data augmentation technique called [Mixup](https://arxiv.org/abs/1710.09412). It is extremely effic... | github_jupyter |
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