code stringlengths 2.5k 6.36M | kind stringclasses 2
values | parsed_code stringlengths 0 404k | quality_prob float64 0 0.98 | learning_prob float64 0.03 1 |
|---|---|---|---|---|
# 1 Introducing 16S Microbiome Primary Analysis
Amanda Birmingham, CCBB, UCSD (abirmingham@ucsd.edu)
This document introduces a Standard Operating Procedure (SOP) that covers primary analysis of single-end, three-read, Golay-barcoded microbiome 16S sequencing data.
<a name = "table-of-contents"></a>
## Table of Con... | github_jupyter | # 1 Introducing 16S Microbiome Primary Analysis
Amanda Birmingham, CCBB, UCSD (abirmingham@ucsd.edu)
This document introduces a Standard Operating Procedure (SOP) that covers primary analysis of single-end, three-read, Golay-barcoded microbiome 16S sequencing data.
<a name = "table-of-contents"></a>
## Table of Con... | 0.87851 | 0.8586 |
# Coding Paradigms for Device Control
```
%serialconnect
```
## The Coding Challenge
PD control for a Ball on beam device. The device is to sense the position of a ball on a 50cm beam, compare to a setpoint, and adjust beam position with servo motor. The setpoint and control constant is to be given by the device use... | github_jupyter | %serialconnect
from machine import Pin, PWM
import time
class Servo(object):
def __init__(self, gpio, freq=50):
self.gpio = gpio
self.pwm = PWM(Pin(gpio, Pin.IN))
self.pwm.freq(freq)
self.pwm.duty_ns(0)
def set_value(self, value):
self.pulse_us = 500 + 20*max(0... | 0.331552 | 0.75037 |
```
#default_exp suite
```
# Model Suite
<br>
### Imports
```
#exports
import yaml
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold, train_test_split
from wpdhack import data, feature
from tqdm import tqdm
from random import randi... | github_jupyter | #default_exp suite
#exports
import yaml
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold, train_test_split
from wpdhack import data, feature
from tqdm import tqdm
from random import randint
from typing import Protocol
from importlib ... | 0.643329 | 0.734242 |
# **Image Recognition**: Neural Nets
Source: [https://github.com/d-insight/code-bank.git](https://github.com/d-insight/code-bank.git)
License: [MIT License](https://opensource.org/licenses/MIT). See open source [license](LICENSE) in the Code Bank repository.
-------------
## Overview
In this demo we will perform... | github_jupyter | # Put all import statements at the top of your notebook
import warnings
warnings.simplefilter('ignore')
# Standard imports
import pandas as pd
import numpy as np
import itertools
# Data science packages
from sklearn.model_selection import learning_curve, validation_curve, StratifiedShuffleSplit, train_test_split, St... | 0.839537 | 0.928991 |
# Gram-Schmidt process
## Instructions
In this assignment you will write a function to perform the Gram-Schmidt procedure, which takes a list of vectors and forms an orthonormal basis from this set.
As a corollary, the procedure allows us to determine the dimension of the space spanned by the basis vectors, which is e... | github_jupyter | A[0, 0] A[0, 1] A[0, 2] A[0, 3]
A[1, 0] A[1, 1] A[1, 2] A[1, 3]
A[2, 0] A[2, 1] A[2, 2] A[2, 3]
A[3, 0] A[3, 1] A[3, 2] A[3, 3]
A[n, m]
A[n]
A[:, m]
u @ v
# GRADED FUNCTION
import numpy as np
import numpy.linalg as la
verySmallNumber = 1e-14 # That's 1×10⁻¹⁴ = 0.00000000000001
# Our first function wi... | 0.579043 | 0.986455 |
# Contribute
Before we can accept contributions, you need to become a CLAed contributor.
E-mail a signed copy of the
[CLAI](https://github.com/openpifpaf/openpifpaf/blob/main/docs/CLAI.txt)
(and if applicable the
[CLAC](https://github.com/openpifpaf/openpifpaf/blob/main/docs/CLAC.txt))
as PDF file to research@svenkrei... | github_jupyter | pip3 install numpy cython
pip3 install --editable '.[dev,train,test]'
pylint openpifpaf
pycodestyle openpifpaf
pytest
cd guide
python download_data.py
pytest --nbval-lax --current-env *.ipynb
import sys
if sys.version_info >= (3, 8):
import importlib.metadata
extras = importlib.metadata.metadata('openpifpaf'... | 0.274449 | 0.779532 |
# Lecture 7: Vectorized Programming
CSCI 1360E: Foundations for Informatics and Analytics
## Overview and Objectives
We've covered loops and lists, and how to use them to perform some basic arithmetic calculations. In this lecture, we'll see how we can use an external library to make these computations much easier a... | github_jupyter | import random
x = [3, 7, 2, 9, 4]
print("Maximum: {}".format(max(x)))
print("Minimum: {}".format(min(x)))
import random # For generating random numbers, as we've seen.
import os # For interacting with the filesystem of your computer.
import re # For regular expressions. Unrelated: https://xkcd.com/1171/... | 0.45641 | 0.986165 |
# Quick Start
Below is a sample demo of interaction with the environment.
```
from maro.simulator import Env
from maro.simulator.scenarios.cim.common import Action, DecisionEvent
env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100)
metrics: object = None
decision_event: DecisionEvent... | github_jupyter | from maro.simulator import Env
from maro.simulator.scenarios.cim.common import Action, DecisionEvent
env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100)
metrics: object = None
decision_event: DecisionEvent = None
is_done: bool = False
while not is_done:
action: Action = None
... | 0.725357 | 0.918553 |
<a id='header'></a>
# Principal Component Analysis (PCA)
In this notebook we present PCA-related functionalities from the ``reduction`` module.
### PCA functionalities
- [**Section 1**](#global_local_pca): We present how *global and local PCA* can be performed using `PCA` class from the `reduction` module.
- [**Sect... | github_jupyter | save_plots = False
from PCAfold import preprocess
from PCAfold import reduction
from PCAfold import PCA
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
# Set some initial parameters:
global_color = '#6a6e7a'
k1_color = '#0e7da7'
k2_color = '#ceca70'
PC_color = '#000000'
data_point =... | 0.756717 | 0.944842 |
```
import nltk
from nltk.corpus import twitter_samples
nltk.download('twitter_samples')
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
from nltk.stem import Po... | github_jupyter | import nltk
from nltk.corpus import twitter_samples
nltk.download('twitter_samples')
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
from nltk.stem import Porter... | 0.291687 | 0.329014 |
# 4 Classes
### 4.1 The `class` Statement
The `class` statement starts a block of code and creates a new
namespace. All namespace changes in the block, e.g. simple
assignment and function definitions, are made in that new namespace.
Finally it adds the class name to the namespace where the class
statement appears.... | github_jupyter | class Number:
__version__ = '1.0'
def __init__(self, amount):
self.amount = amount
def add(self, value):
return self.amount + value
Number
Number.__name__
Number.__class__
Number.__version__
n1 = Number(1)
Number.add
n1.add
n1.add(2)
def init(self, amount):
self.amount = am... | 0.884757 | 0.879716 |
# Racial data vs. Congressional districts
We are now awash with data from different sources, but pulling it all together to gain insights can be difficult for many reasons. In this notebook we show how to combine data of very different types to show previously hidden relationships:
* **"Big data"**: 300 million poin... | github_jupyter | import holoviews as hv
from holoviews import opts
import geoviews as gv
import datashader as ds
import dask.dataframe as dd
from cartopy import crs
from holoviews.operation.datashader import datashade
hv.extension('bokeh', width=95)
opts.defaults(
opts.Points(apply_ranges=False, ),
opts.RGB(width=1200, heigh... | 0.364438 | 0.989213 |
# Tensorflow Image Recognition Tutorial
This tutorial shows how we can use MLDB's [TensorFlow](https://www.tensorflow.org) integration to do image recognition. TensorFlow is Google's open source deep learning library.
We will load the [Inception-v3 model](http://arxiv.org/abs/1512.00567) to generate descriptive lab... | github_jupyter | from pymldb import Connection
mldb = Connection()
inceptionUrl = 'file://mldb/mldb_test_data/models/inception_dec_2015.zip'
print mldb.put('/v1/functions/fetch', {
"type": 'fetcher',
"params": {}
})
print mldb.put('/v1/functions/inception', {
"type": 'tensorflow.graph',
"params": {
"modelFile... | 0.345105 | 0.991836 |
## SVM model for 4class audio with 100ms frame size
## Important Libraries
```
import io
import time
from sklearn import metrics
from scipy.stats import zscore
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from keras.models import Sequential
from keras.layers.core impo... | github_jupyter | import io
import time
from sklearn import metrics
from scipy.stats import zscore
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.callbacks import EarlyStopping
import tensorflow ... | 0.587115 | 0.838878 |
### Import packages-libraries
```
import pandas as pd
import numpy as np
import urllib3
import requests
from PIL import Image
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
from sklearn.preprocessing impo... | github_jupyter | import pandas as pd
import numpy as np
import urllib3
import requests
from PIL import Image
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
from sklearn.preprocessing import StandardScaler
from sklearn.prep... | 0.331985 | 0.628322 |
```
from math import log
import matplotlib.pyplot as plt
%matplotlib inline
#定义文本框和箭头格式
decisionNode = dict(boxstyle="sawtooth", fc="0.8") #定义判断节点形态
leafNode = dict(boxstyle="round4", fc="0.8") #定义叶节点形态
arrow_args = dict(arrowstyle="<-") #定义箭头
#绘制带箭头的注解
#nodeTxt：节点的文字标注, centerPt：节点中心位置,
#parentPt：箭头... | github_jupyter | from math import log
import matplotlib.pyplot as plt
%matplotlib inline
#定义文本框和箭头格式
decisionNode = dict(boxstyle="sawtooth", fc="0.8") #定义判断节点形态
leafNode = dict(boxstyle="round4", fc="0.8") #定义叶节点形态
arrow_args = dict(arrowstyle="<-") #定义箭头
#绘制带箭头的注解
#nodeTxt：节点的文字标注, centerPt：节点中心位置,
#parentPt：箭头起点位置... | 0.307774 | 0.483587 |
<p style="z-index: 101;background: #fde073;text-align: center;line-height: 2.5;overflow: hidden;font-size:22px;">Please <a href="https://www.pycm.ir/doc/#Cite" target="_blank">cite us</a> if you use the software</p>
# Example-6 (Unbalanced data)
## Environment check
Checking that the notebook is running on Google C... | github_jupyter | import sys
try:
import google.colab
!{sys.executable} -m pip -q -q install pycm
except:
pass
from pycm import ConfusionMatrix
case1 = ConfusionMatrix(matrix={"Class1": {"Class1": 26900, "Class2":40}, "Class2": {"Class1": 25, "Class2": 500}})
case1.print_normalized_matrix()
print('ACC:',case1.ACC)
print('MCC:',c... | 0.170784 | 0.88573 |
Notebook for converting models to onnx
(Obsolete as this is now also implemented in the main codebase)
```
from transformers.convert_graph_to_onnx import convert
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from onnxruntime_tools import optimizer
from os import environ
from psutil import cpu_count
impor... | github_jupyter | from transformers.convert_graph_to_onnx import convert
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from onnxruntime_tools import optimizer
from os import environ
from psutil import cpu_count
import torch
import torch.nn.functional as F
import numpy as np
from src.data_utils import encode, decode
environ... | 0.669529 | 0.519521 |
Comparing GOES XRS 15 and 16 - 1s from fido/sunpy and direct download of avg1min
* 25-May-2020 IGH
```
import matplotlib
import matplotlib.pyplot as plt
from sunpy import timeseries as ts
from sunpy.net import Fido
from sunpy.net import attrs as a
# Just setup plot fonts
plt.rcParams.update({'font.size': 18,'font.f... | github_jupyter | import matplotlib
import matplotlib.pyplot as plt
from sunpy import timeseries as ts
from sunpy.net import Fido
from sunpy.net import attrs as a
# Just setup plot fonts
plt.rcParams.update({'font.size': 18,'font.family':"sans-serif",\
'font.sans-serif':"Arial",'mathtext.default':"regular"})
#... | 0.264833 | 0.674493 |
```
import sys,os
os.chdir('.\..\..')
import deep_nn.deep_nn_model as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from ... | github_jupyter | import sys,os
os.chdir('.\..\..')
import deep_nn.deep_nn_model as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from kera... | 0.681939 | 0.487429 |
RNN for text generation
We will create a language model based on Shakespear's writings, and use it to generate new text similar to that of Shakespear.
```
import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.cuda as cuda
import torch.optim as optim
from torch.autograd import Variable
impo... | github_jupyter | import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.cuda as cuda
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import os
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self... | 0.87834 | 0.81309 |
# Parse Tracefiles to characterise variance
You must change the location of the script dir if you want to use relative paths to the trace.
Or , set the absolute path to the location of the directory containing the tracefiles.
```
import logging
import pandas as pd
import cx_Oracle
import matplotlib.pyplot as plt
impor... | github_jupyter | import logging
import pandas as pd
import cx_Oracle
import matplotlib.pyplot as plt
import os
import re
import glob
#abspath = os.path.abspath(__file__)
#dname = os.path.dirname(abspath)
#os.chdir(f"{dname}/")
os.chdir("C:\\Users\\David Olivari\\Documents\\ONGOING DB WORK\\carsprd\\tracefiles_stuff\\bin")
trcs = glob.g... | 0.240418 | 0.636466 |
```
! pip install -U pip
! pip install -U torch==1.5.0
! pip install -U torchtext==0.6.0
! pip install -U matplotlib==3.2.1
! pip install -U trains>=0.15.0
! pip install -U tensorboard==2.2.1
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchtext
from torchtext.datase... | github_jupyter | ! pip install -U pip
! pip install -U torch==1.5.0
! pip install -U torchtext==0.6.0
! pip install -U matplotlib==3.2.1
! pip install -U trains>=0.15.0
! pip install -U tensorboard==2.2.1
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchtext
from torchtext.datasets i... | 0.846895 | 0.471649 |
# Canary Rollout with Seldon and Ambassador
## Setup Seldon Core
Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Setup-Cluster) with [Ambassador Ingress](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.htm... | github_jupyter | !kubectl create namespace seldon
!kubectl config set-context $(kubectl config current-context) --namespace=seldon
from IPython.core.magic import register_line_cell_magic
@register_line_cell_magic
def writetemplate(line, cell):
with open(line, "w") as f:
f.write(cell.format(**globals()))
VERSION=!cat ../..... | 0.432063 | 0.941331 |
# Linear Systems
Solving linear systems of the form
$$
A \mathbf{x} = \mathbf{b}
$$
where $A$ is symmetric positive definite is arguably one of the most fundamental computations in statistics, machine learning and scientific computation at large. Many problems can be reduced to the solution of one or many (large-scal... | github_jupyter | # Make inline plots vector graphics instead of raster graphics
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
# Plotting
import matplotlib.pyplot as plt
plt.style.use('../probnum.mplstyle')
import numpy as np
from scipy.sparse import diags
# Random linear s... | 0.735642 | 0.977543 |
<a href="https://colab.research.google.com/github/krakowiakpawel9/machine-learning-bootcamp/blob/master/unsupervised/01_clustering/06_clustering_comparison.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
* @author: krakowiakpawel9@gmail.com
* @sit... | github_jupyter | !pip install scikit-learn
!pip install --upgrade scikit-learn
import numpy as np
import pandas as pd
import plotly.express as px
from sklearn.datasets import make_blobs
blobs_data = make_blobs(n_samples=1000, cluster_std=0.7, random_state=24, center_box=(-4.0, 4.0))[0]
blobs = pd.DataFrame(blobs_data, columns=['x1'... | 0.777046 | 0.960361 |
```
cuse = spark.read.csv('data/cuse_binary.csv', header=True, inferSchema=True)
cuse.show(5)
cuse.columns[0:3]
# cuse.select('age').distinct().show()
cuse.select('age').rdd.countByValue()
# cuse.select('education').rdd.countByValue()
# string index each categorical string columns
from pyspark.ml.feature import StringI... | github_jupyter | cuse = spark.read.csv('data/cuse_binary.csv', header=True, inferSchema=True)
cuse.show(5)
cuse.columns[0:3]
# cuse.select('age').distinct().show()
cuse.select('age').rdd.countByValue()
# cuse.select('education').rdd.countByValue()
# string index each categorical string columns
from pyspark.ml.feature import StringIndex... | 0.558809 | 0.641099 |
```
import panel as pn
pn.extension('plotly')
```
The ``Plotly`` pane renders Plotly plots inside a panel. It optimizes the plot rendering by using binary serialization for any array data found on the Plotly object, providing efficient updates. Note that to use the Plotly pane in a Jupyter notebook, the Panel extensio... | github_jupyter | import panel as pn
pn.extension('plotly')
import numpy as np
import plotly.graph_objs as go
xx = np.linspace(-3.5, 3.5, 100)
yy = np.linspace(-3.5, 3.5, 100)
x, y = np.meshgrid(xx, yy)
z = np.exp(-(x-1)**2-y**2)-(x**3+y**4-x/5)*np.exp(-(x**2+y**2))
surface = go.Surface(z=z)
layout = go.Layout(
title='Plotly 3D P... | 0.532182 | 0.950824 |
# Allowing storage of yaml file
Here we will correct the model so that it can be stored in `.yml` format, and do some tests to check all is in place.
Benjamín J. Sánchez, 2020-05-06
## 1. Non-compliant notes
```
import cobra
model = cobra.io.read_sbml_model("../model/p-thermo.xml")
cobra.io.save_yaml_model(model,".... | github_jupyter | import cobra
model = cobra.io.read_sbml_model("../model/p-thermo.xml")
cobra.io.save_yaml_model(model,"../model/p-thermo.yml")
model = cobra.io.read_sbml_model("../model/p-thermo.xml")
cobra.io.save_yaml_model(model,"../model/p-thermo.yml")
model.metabolites.pydx5p_c.notes
model = cobra.io.read_sbml_model("../model/... | 0.19046 | 0.788217 |
```
from iobjectspy import (Point2D,
QueryParameter,
open_datasource,
create_datasource,
SpatialQueryMode)
import os
# 设置示例数据路径
example_data_dir = ''
# 设置结果输出路径
out_dir = os.path.join(example_data_dir, 'out')
if not os.pat... | github_jupyter | from iobjectspy import (Point2D,
QueryParameter,
open_datasource,
create_datasource,
SpatialQueryMode)
import os
# 设置示例数据路径
example_data_dir = ''
# 设置结果输出路径
out_dir = os.path.join(example_data_dir, 'out')
if not os.path.ex... | 0.283881 | 0.399665 |
# Auto-Generated Altair Examples
All the following notebooks are auto-generated from the Python examples
in the Altair source code repository here:
https://github.com/altair-viz/altair/tree/master/altair/vegalite/v2/examples
- [Aggregate Bar Chart](aggregate_bar_chart.ipynb)
- [Airports](airports.ipynb)
- [Anscombe ... | github_jupyter | # Auto-Generated Altair Examples
All the following notebooks are auto-generated from the Python examples
in the Altair source code repository here:
https://github.com/altair-viz/altair/tree/master/altair/vegalite/v2/examples
- [Aggregate Bar Chart](aggregate_bar_chart.ipynb)
- [Airports](airports.ipynb)
- [Anscombe ... | 0.820218 | 0.905907 |
```
import numpy as np
import panel as pn
import xarray as xr
import holoviews as hv
import geoviews as gv
import cartopy.crs as ccrs
from earthsim.annotators import PolyAnnotator, PolyExporter, paths_to_polys
from earthsim.grabcut import GrabCutPanel, SelectRegionPanel
gv.extension('bokeh')
```
The GrabCut algorithm... | github_jupyter | import numpy as np
import panel as pn
import xarray as xr
import holoviews as hv
import geoviews as gv
import cartopy.crs as ccrs
from earthsim.annotators import PolyAnnotator, PolyExporter, paths_to_polys
from earthsim.grabcut import GrabCutPanel, SelectRegionPanel
gv.extension('bokeh')
select_region = SelectRegionP... | 0.400163 | 0.951414 |
# Linear Regression
```
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.read_csv('../data/weight-height.csv')
df.head()
df.plot(kind='scatter',
x='Height',
y='Weight',
title='Weight and Height in adults')
df.plot(kind='scatter',
x='Heigh... | github_jupyter | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.read_csv('../data/weight-height.csv')
df.head()
df.plot(kind='scatter',
x='Height',
y='Weight',
title='Weight and Height in adults')
df.plot(kind='scatter',
x='Height',
y='Weight',
... | 0.722918 | 0.908658 |
```
%%capture
import os
import site
os.sys.path.insert(0, '/home/schirrmr/code/reversible/reversible2/')
os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')
os.sys.path.insert(0, '/home/schirrmr/code/explaining/reversible//')
%cd /home/schirrmr/
%load_ext autoreload
%autoreload 2
import numpy as np
... | github_jupyter | %%capture
import os
import site
os.sys.path.insert(0, '/home/schirrmr/code/reversible/reversible2/')
os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')
os.sys.path.insert(0, '/home/schirrmr/code/explaining/reversible//')
%cd /home/schirrmr/
%load_ext autoreload
%autoreload 2
import numpy as np
impo... | 0.523908 | 0.273993 |
```
import sys
from pathlib import Path
portfolio_management_path = Path.cwd().parent
sys.path.insert(0, str(portfolio_management_path))
import xarray as xr
import numpy as np
import pandas as pd
from portfolio_management.database.manager import Manager
from portfolio_management.database.retrieve import get_dataframe
... | github_jupyter | import sys
from pathlib import Path
portfolio_management_path = Path.cwd().parent
sys.path.insert(0, str(portfolio_management_path))
import xarray as xr
import numpy as np
import pandas as pd
from portfolio_management.database.manager import Manager
from portfolio_management.database.retrieve import get_dataframe
data... | 0.391755 | 0.293664 |
```
import pandas as pd
import os
import re
import json
base_dir = 'data/mgnify/studies'
def gen_study_dir_contents():
"""Iterate over every study directory and yield all file paths within each one."""
for name in os.listdir(base_dir):
study_dir = os.path.join(base_dir, name)
file_paths = []
... | github_jupyter | import pandas as pd
import os
import re
import json
base_dir = 'data/mgnify/studies'
def gen_study_dir_contents():
"""Iterate over every study directory and yield all file paths within each one."""
for name in os.listdir(base_dir):
study_dir = os.path.join(base_dir, name)
file_paths = []
... | 0.31542 | 0.162679 |
```
import csv
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
%load_ext autoreload
%autoreload 2
from scipy.optimize import least_squares
from scipy.stats import expon
from scipy.stats import weibull_min as weibull
# cdf(x, c, loc=0, scale=1)
week_... | github_jupyter | import csv
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
%load_ext autoreload
%autoreload 2
from scipy.optimize import least_squares
from scipy.stats import expon
from scipy.stats import weibull_min as weibull
# cdf(x, c, loc=0, scale=1)
week_rang... | 0.163713 | 0.726256 |
```
#Import libraries
import pandas as pd
import matplotlib.pyplot as plt
```
# Domain Specific Dataset Analysis
## 1. Domain: COVID-19 Research Article Abstracts
Source: COVID-19 Open Research Dataset Challenge ([CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)).
CORD-19 is a reso... | github_jupyter | #Import libraries
import pandas as pd
import matplotlib.pyplot as plt
df1 = pd.read_csv('../datasets/CORD19/metadata_sample.csv', dtype=str)
df1.head(2).abstract.values #sample
import nltk
nltk.download('punkt')
word_tokens = nltk.word_tokenize(df1.iloc[0].abstract)
'|'.join(word_tokens)
from nltk.stem.snowball impo... | 0.291384 | 0.88573 |
# OpenMP* Device Parallelism (Fortran)
#### Sections
- [Learning Objectives](#Learning-Objectives)
- [Device Parallelism](#Device-Parallelism)
- [GPU Architecture](#GPU-Architecture)
- ["Normal" OpenMP constructs](#"Normal"-OpenMP-constructs)
- [League of Teams](#League-of-Teams)
- [Worksharing with Teams](#Worksharin... | github_jupyter | subroutine saxpy(a, x, y, sz)
! Declarations Omitted
!$omp target map(to:x(1:sz)) map(tofrom(y(1:sz))
!$omp parallel do simd
do i=1,sz
y(i) = a * x(i) + y(i);
end do
!$omp end target
end subroutine
subroutine saxpy(a, x, y, sz)
! Declarations Omitted
!$omp target teams distribut... | 0.292393 | 0.914787 |
# DCGAN on MNIST Digits dataset
Following the original GAN [1], Deep Convolutional Generative Adversarial Network (DCGAN) [2] is replacing some of the layers with convolutional layers.
The result is similar but taking advantage of the properties of the convolutional layers: less parameters to train, space invariance.... | github_jupyter | COLAB = True
if COLAB:
from google.colab import drive
drive.mount('/content/drive')
!pip install tensorview
import sys
import tensorflow as tf
import numpy as np
from tensorflow.keras import models, layers, losses, optimizers, metrics
import tensorflow_datasets as tf_ds
import tensorview as tv
import matplotlib.py... | 0.718199 | 0.943971 |
# Finding MetaCharacters
Here’s a complete list of the metacharacters used in regular expressions:
```python
. ^ $ * + ? { } [ ] \ | ( )
```
As we mentioned in the previous lesson, these metacharacters are used to give special instructions and can't be searched for directly. If we want to search for these metacharac... | github_jupyter | . ^ $ * + ? { } [ ] \ | ( )
# Import re module
import re
# Sample text
sample_text = 'Alice and Walter are walking to the store.'
# Create a regular expression object with the regular expression '\.'
regex = re.compile(r'\.')
# Search the sample_text for the regular expression
matches = regex.finditer(sample_text)
... | 0.498779 | 0.964689 |
```
import graphlab
products = graphlab.SFrame('Amazon_baby.sframe/')
selected_words = ['awesome', 'great', 'fantastic', 'amazing', 'love', 'horrible', 'bad', 'terrible', 'awful', 'wow', 'hate']
products['words_count'] = graphlab.text_analytics.count_words(products['review'])
def count_word(words_count,word):
if wo... | github_jupyter | import graphlab
products = graphlab.SFrame('Amazon_baby.sframe/')
selected_words = ['awesome', 'great', 'fantastic', 'amazing', 'love', 'horrible', 'bad', 'terrible', 'awful', 'wow', 'hate']
products['words_count'] = graphlab.text_analytics.count_words(products['review'])
def count_word(words_count,word):
if word i... | 0.182717 | 0.487063 |
<!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;"></a>
*This notebook contains an ex... | github_jupyter | <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;"></a>
*This notebook contains an ex... | 0.873498 | 0.877056 |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#read the file
df = pd.read_json (r'VZ.json')
#print the head
df
df['t'] = pd.to_datetime(df['t'], unit='s')
df = df.rename(columns={'c': 'Close', 'h': 'High', 'l':'Low', 'o': 'Open', 's': 'Status', 't': 'Date', 'v': 'Volume'})
df.head()
```
... | github_jupyter | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#read the file
df = pd.read_json (r'VZ.json')
#print the head
df
df['t'] = pd.to_datetime(df['t'], unit='s')
df = df.rename(columns={'c': 'Close', 'h': 'High', 'l':'Low', 'o': 'Open', 's': 'Status', 't': 'Date', 'v': 'Volume'})
df.head()
#read th... | 0.417509 | 0.773281 |
```
import easyocr
import onnxruntime
import os
import string
from matplotlib import pyplot as plt
import difflib
import sys
sys.path.append('/home/tandonsa/PycharmProjects/side_project/ocr_mawaqif/')
from src.utils import infer_utils
# add NLP Models
en_model = easyocr.Reader(['en'])
ar_model = easyocr.Reader(['ar'])... | github_jupyter | import easyocr
import onnxruntime
import os
import string
from matplotlib import pyplot as plt
import difflib
import sys
sys.path.append('/home/tandonsa/PycharmProjects/side_project/ocr_mawaqif/')
from src.utils import infer_utils
# add NLP Models
en_model = easyocr.Reader(['en'])
ar_model = easyocr.Reader(['ar'])
nlp... | 0.447702 | 0.209227 |
### ***Goal of this notebook:***
#### The purpose of this notebook is to show the different ways implemented to associate cluster catalogs. It is designed to associate the halos in cosmoDC2 and the clusters detected by redMaPPer in cosmoDC2, but can be tuned to work with other catalogues.
### ***Rationale:***
#### A... | github_jupyter | import GCRCatalogs
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy import units as u
from astropy.cosmology import FlatLambdaCDM
from cluster_validation.opening_catalogs_functions import *
from cluster_validation.association_meth... | 0.441432 | 0.953232 |
```
from rfm_deployment.rfm_model_V2_com import *
from itertools import product
import cx_Oracle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import calendar
import datetime
import cairo
import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gtk
%matplotlib... | github_jupyter | from rfm_deployment.rfm_model_V2_com import *
from itertools import product
import cx_Oracle
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import calendar
import datetime
import cairo
import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gtk
%matplotlib inl... | 0.118793 | 0.205575 |
# Training a CNN in Keras with Real-Time Data Augmentation
## Initial Setup
```
from __future__ import division
from PIL import Image
import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
## Load Images into a Matrix
```
base_dir = 'square_images128'... | github_jupyter | from __future__ import division
from PIL import Image
import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
base_dir = 'square_images128'
image_width = 128
image_height = 128
classes = ['daffodil', 'snowdrop', 'lily_valley', 'bluebell', 'crocus', 'iris', ... | 0.593138 | 0.771413 |
# A. **Simple Regresi Linier**
Teknik ini digunakan untuk menyelesaikan permasalahan hubungan sebab akibat antara 2 variable. 2 variable itu adalah :
1. Variable Faktor Penyebab - biasanya disimbolkan dengan X. Ini disebut `predictor`.
2. Variable Akibat - biasanya disimbolkan dengan Y. Ini disebut `response`.
Untuk... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
data = pd.read_csv('Salary_Data.csv')
data
data.keys()
data.shape
mydata = pd.DataFrame(data)
mydata.head()
mydata.tail()
mydata.info()
# slice from the beginning to 'Salary'
data.loc[:, :'Salary']
x = data.il... | 0.501465 | 0.946843 |
# Convolutional Layer
In this notebook, we visualize four filtered outputs (a.k.a. activation maps) of a convolutional layer.
In this example, *we* are defining four filters that are applied to an input image by initializing the **weights** of a convolutional layer, but a trained CNN will learn the values of these w... | github_jupyter | import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# TODO: Feel free to try out your own images here by changing img_path
# to a file path to another image on your computer!
img_path = 'data/udacity_sdc.png'
# load color image
bgr_img = cv2.imread(img_path)
# convert to grayscale
gray_img = cv2.cvtColor(b... | 0.626238 | 0.987092 |
# Practical use of HH-suite3 on the command line in Jupyter via MyBinder.org: Basics
Run this in sessions launched from [my HH-suite3-binder repo](https://github.com/fomightez/hhsuite3-binder) because the software is already installed.
This is the first notebook in my series of notebooks convering use if HH-suite3 ... | github_jupyter | !hhblits
!hhsearch
!hhmake
%%bash
cd ../../..
find . -type f -name "hhmakemodel.*"
%run /srv/conda/envs/notebook/scripts/hhmakemodel.py
%run /srv/conda/envs/notebook/scripts/hhsuitedb.py
s='''>TvLDH
MSEAAHVLITGAAGQIGYILSHWIASGELYGDRQVYLHLLDIPPAMNRLTALTMELEDCAFPHLAGFVATTDP
KAAFKDIDCAFLVASMPLKPGQVRADLISSNSVIFKNTGEYLS... | 0.162613 | 0.913599 |
# Build the speech model
Now that we have created the spectrogram images its time to build the computer vision model. If you are following along with the learning path then you already created a computer vision model in the second module in this path. We will be using the [torchvision](https://pypi.org/project/torchvi... | github_jupyter | from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision impor... | 0.884776 | 0.98191 |
<div style="display: flex; background-color: #3F579F;">
<h1 style="margin: auto; font-weight: bold; padding: 30px 30px 0px 30px; color:#fff;" align="center">Automatically classify consumer goods - P6</h1>
</div>
<div style="display: flex; background-color: #3F579F; margin: auto; padding: 5px 30px 0px 30px;" >
<... | github_jupyter | ## General
import os
import pandas as pd
import numpy as np
## TensorFlow
import tensorflow as tf
from tensorboard.plugins import projector
## Own specific functions
from functions import *
%load_ext tensorboard
# Path to save the embedding and checkpoints generated
LOG_DIR = "./logs/projections/"
df_text = pd.re... | 0.488527 | 0.875574 |
# c05-???
*Purpose*: (Apply control charts, Pr modeling techniques)
```
import grama as gr
import numpy as np
import pandas as pd
import time
DF = gr.Intention()
%matplotlib inline
filename_data = "./data/c05-data.csv"
```
# Stang
```
from grama.data import df_stang
df_stang.head()
(
df_stang
>> gr.pt_xbs(... | github_jupyter | import grama as gr
import numpy as np
import pandas as pd
import time
DF = gr.Intention()
%matplotlib inline
filename_data = "./data/c05-data.csv"
from grama.data import df_stang
df_stang.head()
(
df_stang
>> gr.pt_xbs(group="thick", var="E")
)
from grama.models import make_plate_buckle
md_plate = make_plate... | 0.445771 | 0.820937 |
<a href="https://colab.research.google.com/github/Abhishek1236/computer-vision-models/blob/main/Alexnet_practice.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#importing the libraries
import keras
from keras.models import Sequential
from ke... | github_jupyter | #importing the libraries
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,\
Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
import tflearn.datasets.oxflower17 as oxflower17
import tensorflow as tf
... | 0.759047 | 0.831759 |
# Time normalization of data
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
Time normalization is usually employed for the temporal alignment of cyclic data obtained from different trials with different duration (number... | github_jupyter | yn, tn, indie = tnorma(y, axis=0, step=1, k=3, smooth=0, mask=None,
nan_at_ext='delete', show=False, ax=None)
pip install tnorma
conda install -c duartexyz tnorma
# Import the necessary libraries
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
y = [5, 4, 10, 8, 1, 10,... | 0.667906 | 0.988154 |
### SET DATA Structure
```
st = set()
print(type(st))
# creating a simple set
st= {'pumar','kris','krish','bara','bara'}
print(type(st))
print(st) # eliminated the duplicate values
# add elemets to the set
st.add('Gommu')
print(st)
# remove element from the set
st.remove('Gommu')
print(st)
# Adding more than one or... | github_jupyter | st = set()
print(type(st))
# creating a simple set
st= {'pumar','kris','krish','bara','bara'}
print(type(st))
print(st) # eliminated the duplicate values
# add elemets to the set
st.add('Gommu')
print(st)
# remove element from the set
st.remove('Gommu')
print(st)
# Adding more than one or more values in the set simu... | 0.429669 | 0.800185 |
```
%run data.py
```
### Read the zipcode data.
```
zipcode_data = fetchData(nyc_zcta_url)
zipcode_data.head(10)
zipcode_old_df = spark.createDataFrame(zipcode_data)
zipcode_df = zipcode_old_df.select(zipcode_old_df.MODZCTA.cast("integer").alias("zipcode"),
zipcode_old_df["Positive"],... | github_jupyter | %run data.py
zipcode_data = fetchData(nyc_zcta_url)
zipcode_data.head(10)
zipcode_old_df = spark.createDataFrame(zipcode_data)
zipcode_df = zipcode_old_df.select(zipcode_old_df.MODZCTA.cast("integer").alias("zipcode"),
zipcode_old_df["Positive"], zipcode_old_df["Total"])
zipcode_df = ... | 0.379608 | 0.766031 |
<a href="https://colab.research.google.com/github/BandaruDheeraj/TTSModel/blob/main/waveglow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### This notebook requires a GPU runtime to run.
### Please select the menu option "Runtime" -> "Change runt... | github_jupyter | %%bash
pip install numpy scipy librosa unidecode inflect librosa
apt-get update
apt-get install -y libsndfile1
import torch
waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')
waveglow = waveglow.remove_weightnorm(waveglow)
waveglow = waveglow.to('cuda')
waveglow.ev... | 0.580114 | 0.982237 |
#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 | !pip install git+https://github.com/google/starthinker
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
CLIENT_CREDENTIALS = 'PASTE CREDENTIALS HERE'
print("Client Credentials Set To: %s" % CLIENT_CREDENTIALS)
FIELDS = {
'auth_read': 'user', # Credentials used for readin... | 0.371935 | 0.715349 |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter | # Imports here
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
import numpy as np
from PIL import Image
data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
# TO... | 0.665628 | 0.969957 |
# <a name="0">Machine Learning Accelerator - Natural Language Processing - Lecture 3</a>
## Neural Networks with PyTorch
In this notebook, we will build, train and validate a Neural Network using PyTorch.
1. <a href="#1">Implementing a neural network with PyTorch</a>
2. <a href="#2"... | github_jupyter | import torch
from torch import nn
net = nn.Sequential(
nn.Linear(in_features=3, # Input size of 3 is expected
out_features=64), # Linear layer-1 with 64 units
nn.Tanh(), # Tanh activation is applied
nn.Dropout(p=.4), # Apply random 40%... | 0.919004 | 0.987935 |
### Note
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.
```
# Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
file_to_load = "Resources/purchase_data.csv"
# Read Purchasing Fil... | github_jupyter | # Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
file_to_load = "Resources/purchase_data.csv"
# Read Purchasing File and store into Pandas data frame
purchase_df = pd.read_csv(file_to_load)
purchase_df
age_df = pd.read_csv(file_to_load)
purchase_df
#locates the three columns fr... | 0.48438 | 0.832849 |
# Fashion MNIST mit Neuronalen Netzen
In diesem Arbeitsblatt wollen wir uns erneut den Fashion MNIST Datensatz vornehmen, den wir schon aus dem 7. Arbeitsblatt kennen.
Dort haben wir das Problem mit Multinomialer Logistischer Regression gelöst.
Hier wollen wir ein Multi-Layer Perceptron einsetzen, also ein recht einfa... | github_jupyter | import tensorflow as tf
#Datensatz aus Keras laden
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
#Pixelwerte nach [0,1] skalieren
X_train = X_train / 255.0
X_test = X_test / 255.0
# Mache aus den 2D Bildern 1D Vektoren
X_train = X_train.reshape(-1,28*28,1)[:,:,0]
X_test = X_test.... | 0.680772 | 0.92617 |
# Why Fugue Does NOT Want To Be Another Pandas-Like Framework
Fugue fully utilizes Pandas for computing tasks, but **Fugue is NOT a Pandas-like computing framework, and it never wants to be.** In this article we are going to explain the reason for this critical design decision.
## Benchmarking PySpark Pandas (Koalas)... | github_jupyter | def gen(n):
np.random.seed(0)
return pd.DataFrame(dict(
a=np.random.choice(["aa","abcd","xyzzzz","tttfs"],n),
b=np.random.randint(0,100,n),
c=np.random.choice(["aa","abcd","xyzzzz","tttfs"],n),
d=np.random.randint(0,10000,n),
))
df.sort_values(["a", "b", "c", "d"]).drop_dupl... | 0.202325 | 0.965446 |
# Amazon SageMaker Multi-Model Endpoints using XGBoost
With [Amazon SageMaker multi-model endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html), customers can create an endpoint that seamlessly hosts up to thousands of models. These endpoints are well suited to use cases where any one o... | github_jupyter | import numpy as np
import pandas as pd
import time
NUM_HOUSES_PER_LOCATION = 1000
LOCATIONS = ['NewYork_NY', 'LosAngeles_CA', 'Chicago_IL', 'Houston_TX', 'Dallas_TX',
'Phoenix_AZ', 'Philadelphia_PA', 'SanAntonio_TX', 'SanDiego_CA', 'SanFrancisco_CA']
PARALLEL_TRAINING_JOBS = 4 # len(LOCATIO... | 0.42477 | 0.956063 |
# Train a gesture recognition model for microcontroller use
This notebook demonstrates how to train a 20kb gesture recognition model for [TensorFlow Lite for Microcontrollers](https://tensorflow.org/lite/microcontrollers/overview). It will produce the same model used in the [magic_wand](https://github.com/tensorflow/t... | github_jupyter | %tensorflow_version 2.x
# Clone the repository from GitHub
!git clone --depth 1 -q https://github.com/tensorflow/tensorflow
# Copy the training scripts into our workspace
!cp -r tensorflow/tensorflow/lite/experimental/micro/examples/magic_wand/train train
# Download the data we will use to train the model
!wget http:... | 0.657098 | 0.987508 |
# Project summary
In this project, I used machine learning to predicting the operating conditions of a waterpoint using data from the Tanzanian Ministry of Water. The algorithm used was Random Forest for multiclassification between three outcomes: "Functional", "Functional needs repair", and "Nonfunctional".
# Data i... | github_jupyter | #Importing external libraries
import math
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
from collections import OrderedDict
from scipy.stats import chi2_contingency
from scipy.stats import chi2
import statsmodels.api as sm
from statsmodels.formula.api import ols
... | 0.398406 | 0.927429 |
# Concluding Thoughts
Congratulations! You've made it! If you have worked through all of the notebooks to this point, then you have joined the small, but growing group of people that are able to harness the power of deep learning to solve real problems. You may not feel that way yet—in fact you probably don't. We have... | github_jupyter | # Concluding Thoughts
Congratulations! You've made it! If you have worked through all of the notebooks to this point, then you have joined the small, but growing group of people that are able to harness the power of deep learning to solve real problems. You may not feel that way yet—in fact you probably don't. We have... | 0.368633 | 0.620507 |
# What's New - Internal
## Summary
This new edition of `made-with-gs-quant` is tailored exclusively for our internal users and showcases some of the latest features of the internal gs-quant toolkit.
In this notebook we find solutions for some the most popular questions we get such as:
+ How do I generate an FX dual ... | github_jupyter | from gs_quant.session import GsSession
GsSession.use()
import gs_quant_internal.tdapi as tdapi
from IPython.display import Image
Image(filename='images/tdapi_package.png')
from gs_quant.markets.portfolio import Portfolio
def FXDualBinaryOption(pair_1, pair_2, strikes_1, strikes_2, expiry, size):
portfolio = Port... | 0.441191 | 0.949763 |
<a href="https://colab.research.google.com/github/ayulockin/LossLandscape/blob/master/Visualizing_Function_Space_Similarity_SmallCNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Setups, Imports and Installations
```
## This is so that I can sa... | github_jupyter | ## This is so that I can save my models.
from google.colab import drive
drive.mount('gdrive')
%%capture
!pip install wandb
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.applications import resnet50
import os
os.environ["TF_DETERMINISTIC_OPS"] =... | 0.602763 | 0.850096 |
# Star with toruses
This is an example of a synthesized three dimensional volume
that is not easy to visualize using only two dimenaional projections.
The calculation to generate the volume array is not optimized and it takes a while to complete.
```
import numpy as np
from numpy.linalg import norm
def vec(*args):
... | github_jupyter | import numpy as np
from numpy.linalg import norm
def vec(*args):
return np.array(args, dtype=np.float)
def normalize(V, epsilon=1e-12):
nm = norm(V)
if nm < epsilon:
return vec(1, 0, 0) # whatever
return (1.0 / nm) * V
def point_segment_distance(P, segment, epsilon=1e-4):
A = segment[0]
... | 0.452778 | 0.93337 |
```
# Load dependencies
import numpy as np
import pandas as pd
pd.options.display.float_format = '{:,.1e}'.format
import sys
sys.path.insert(0, '../../statistics_helper')
from CI_helper import *
from excel_utils import *
```
# Estimating the total biomass of terrestrial deep subsurface archaea and bacteria
We use our... | github_jupyter | # Load dependencies
import numpy as np
import pandas as pd
pd.options.display.float_format = '{:,.1e}'.format
import sys
sys.path.insert(0, '../../statistics_helper')
from CI_helper import *
from excel_utils import *
results = pd.read_excel('terrestrial_deep_subsurface_prok_biomass_estimate.xlsx')
results
# Calculate... | 0.49585 | 0.839603 |
```
%matplotlib inline
```
파이프라인 병렬화로 트랜스포머 모델 학습시키기
==============================================
**Author**: `Pritam Damania <https://github.com/pritamdamania87>`_
**번역**: `백선희 <https://github.com/spongebob03>`_
이 튜토리얼은 파이프라인(pipeline) 병렬화(parallelism)를 사용하여 여러 GPU에 걸친 거대한 트랜스포머(transformer)
모델을 어떻게 학습시키는지 보여줍... | github_jupyter | %matplotlib inline
import sys
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import tempfile
from torch.nn import TransformerEncoder, TransformerEncoderLayer
if sys.platform == 'win32':
print('Windows platform is not supported for pipeline parallelism')
sys.exit(0)
if torch.cud... | 0.611962 | 0.958402 |
# Tutorial: topic modeling to analyze the EGC conference
EGC is a French-speaking conference on knowledge discovery in databases (KDD). In this notebook we show how to use TOM for inferring latent topics that pervade the corpus of articles published at EGC between 2004 and 2015 using non-negative matrix factorization.... | github_jupyter | from tom_lib.structure.corpus import Corpus
from tom_lib.visualization.visualization import Visualization
corpus = Corpus(source_file_path='input/egc_lemmatized.csv',
language='french',
vectorization='tfidf',
max_relative_frequency=0.8,
min_absolute_frequ... | 0.590307 | 0.983375 |
<a href="https://colab.research.google.com/github/Lilchoto3/DS-Unit-2-Linear-Models/blob/master/module3-ridge-regression/LS_DS_213.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 1, Module 3*
---
# Ridge... | github_jupyter | %%capture
import sys
# If you're on Colab:
if 'google.colab' in sys.modules:
DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/'
!pip install category_encoders==2.*
# If you're working locally:
else:
DATA_PATH = '../data/'
import numpy as np
import pandas ... | 0.243283 | 0.987104 |
# Modelagem de Hiperparâmetros
```
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import clf_vAngra_lib as vCLF
from astropy.stats import mad_std
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import KF... | github_jupyter | import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import clf_vAngra_lib as vCLF
from astropy.stats import mad_std
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import KFold
from sklearn.model_selection imp... | 0.469763 | 0.832747 |
# LNet in TF
#### Dependencies
```
import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
```
#### Set hyperparameters
```
display_progress = 40
epochs = 10
batch_s... | github_jupyter | import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
display_progress = 40
epochs = 10
batch_size = 128
wt_init = tf.contrib.layers.xavier_initializer()
# input laye... | 0.76769 | 0.919462 |
```
import sys
sys.path.append('..')
import torch
import pandas as pd
import numpy as np
import pickle
import argparse
import networkx as nx
from torch_geometric.utils import dense_to_sparse, degree
import matplotlib.pyplot as plt
from src.gcn import GCNSynthetic
from src.utils.utils import normalize_adj, get_neighbour... | github_jupyter | import sys
sys.path.append('..')
import torch
import pandas as pd
import numpy as np
import pickle
import argparse
import networkx as nx
from torch_geometric.utils import dense_to_sparse, degree
import matplotlib.pyplot as plt
from src.gcn import GCNSynthetic
from src.utils.utils import normalize_adj, get_neighbourhood... | 0.561575 | 0.683672 |
```
import numpy as np
import cv2 as cv
img = cv.imread('data_hierarchy2.png')
img_white_bg = cv.imread('data_hierarchy4.png')
"""缩小图像,方便看效果
"""
def resizeImg(src):
height, width = src.shape[:2]
size = (int(width * 0.3), int(height * 0.3)) # bgr
img = cv.resize(src, size, interpolation=cv.INTER_AREA)
... | github_jupyter | import numpy as np
import cv2 as cv
img = cv.imread('data_hierarchy2.png')
img_white_bg = cv.imread('data_hierarchy4.png')
"""缩小图像,方便看效果
"""
def resizeImg(src):
height, width = src.shape[:2]
size = (int(width * 0.3), int(height * 0.3)) # bgr
img = cv.resize(src, size, interpolation=cv.INTER_AREA)
re... | 0.162979 | 0.363252 |
If you're opening this Notebook on colab, you will probably need to install the most recent versions of 🤗 Transformers and 🤗 Datasets. We will also need `scipy` and `scikit-learn` for some of the metrics. Uncomment the following cell and run it.
We will also need `scipy` and `scikit-learn` for some of the metrics. W... | github_jupyter | ! pip install transformers
! pip install datasets
! pip install huggingface-hub
! pip install wandb
import wandb
# Log in to your W&B account
wandb.login()
task = "sst2"
model_checkpoint = "distilbert-base-uncased"
batch_size = 16
from datasets import load_dataset
dataset = load_dataset("glue", "sst2")
from transfo... | 0.732113 | 0.975296 |
# dislib tutorial
This tutorial will show the basics of using [dislib](https://dislib.bsc.es).
## Requirements
Apart from dislib, this notebook requires [PyCOMPSs 2.5](https://www.bsc.es/research-and-development/software-and-apps/software-list/comp-superscalar/).
## Setup
First, we need to start an interactive P... | github_jupyter | import pycompss.interactive as ipycompss
ipycompss.start(graph=True, monitor=1000)
import dislib as ds
x = ds.random_array(shape=(500, 500), block_size=(100, 100))
print(x.shape)
x
x._blocks[0][0]
x.collect()
x1 = ds.array([[1, 2, 3], [4, 5, 6]], block_size=(1, 3))
x1
from scipy.sparse import csr_matrix
sp = csr... | 0.46393 | 0.991263 |
```
!pip install wget
!pip install --upgrade scikit-learn
import util
X1, y1, X2, y2 = util.mnist_init('small')
util.image_peek(X1[1], 5 if y1[1] == 1 else 8)
util.image_peek(X1[3], 5 if y1[3] == 1 else 8)
import numpy as np
class Perceptron(object):
def __init__(self, dataset, labels, max_iter, lr):
sel... | github_jupyter | !pip install wget
!pip install --upgrade scikit-learn
import util
X1, y1, X2, y2 = util.mnist_init('small')
util.image_peek(X1[1], 5 if y1[1] == 1 else 8)
util.image_peek(X1[3], 5 if y1[3] == 1 else 8)
import numpy as np
class Perceptron(object):
def __init__(self, dataset, labels, max_iter, lr):
self.b ... | 0.472927 | 0.597549 |
<a href="https://colab.research.google.com/github/ren1406/startbootstrap-freelancer/blob/master/08_sentiment_analysis_with_bert.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Sentiment Analysis with BERT
> TL;DR In this tutorial, you'll learn ho... | github_jupyter | #@title Watch the video tutorial
from IPython.display import YouTubeVideo
YouTubeVideo('8N-nM3QW7O0', width=720, height=420)
!nvidia-smi
!pip install -q -U watermark
!pip install -qq transformers
%reload_ext watermark
%watermark -v -p numpy,pandas,torch,transformers
#@title Setup & Config
import transformers
from tra... | 0.819533 | 0.986258 |
<a href="https://colab.research.google.com/github/pedroescobedob/DS-Unit-2-Linear-Models/blob/master/Pedro_Escobedo_assignment_regression_classification_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 1,... | github_jupyter | %%capture
import sys
# If you're on Colab:
if 'google.colab' in sys.modules:
DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/'
!pip install category_encoders==2.*
# If you're working locally:
else:
DATA_PATH = '../data/'
# Ignore this Numpy warning w... | 0.390127 | 0.974018 |
<a href="https://colab.research.google.com/github/falconlee236/handson-ml2/blob/master/chapter3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
X... | github_jupyter | from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
X, y = mnist['data'], mnist['target']
X.shape
y.shape
import matplotlib as mpl
import matplotlib.pyplot as plt
some_digit = X[0]
some_digit_image = some_digit.reshape(28, 28)
plt.imshow(some_digit_image, cmap='binary')... | 0.850407 | 0.870652 |
```
import os
import numpy as np
import pandas as pd
home_folder = os.path.expanduser("~")
data_folder = os.path.join(home_folder, "Data", "basketball")
data_filename = os.path.join(data_folder, "leagues_NBA_2014_games_games.csv")
results = pd.read_csv(data_filename)
results.ix[:5]
# Don't read the first row, as it is ... | github_jupyter | import os
import numpy as np
import pandas as pd
home_folder = os.path.expanduser("~")
data_folder = os.path.join(home_folder, "Data", "basketball")
data_filename = os.path.join(data_folder, "leagues_NBA_2014_games_games.csv")
results = pd.read_csv(data_filename)
results.ix[:5]
# Don't read the first row, as it is blan... | 0.523177 | 0.28613 |
# Weakly imposing a Dirichlet boundary condition
This tutorial shows how to implement the weak imposition of a Dirichlet boundary condition, as proposed in the paper <a href='https://bempp.com/publications.html#Betcke2019'>Boundary Element Methods with Weakly Imposed Boundary Conditions (2019)</a>.
First, we import B... | github_jupyter | import bempp.api
import numpy as np
h = 0.3
grid = bempp.api.shapes.sphere(h=h)
p1 = bempp.api.function_space(grid, "P", 1)
dual0 = bempp.api.function_space(grid, "DUAL", 0)
beta = 0.1
multi = bempp.api.BlockedOperator(2,2)
multi[0,0] = -bempp.api.operators.boundary.laplace.double_layer(p1, p1, dual0, assembler="fmm"... | 0.282295 | 0.980581 |
# Traitement de signal
## Atelier \#1 : Initiation à Jupyter
### Support de cours disponible à l'adresse : [https://www.github.com/a-mhamdi/isetbz](https://www.github.com/a-mhamdi/isetbz)
---
**Objectifs**
1. Apprendre à programmer en **Python**;
2. Se servir de l'environnement **Jupyter Notebook**;
3. Utiliser les ... | github_jupyter | a = 1 # Un entier
print('La variable a = {} est de type {}'.format(a, type(a)))
b = -1.25 # Un nombre réel
print('La variable b = {} est de type {}'.format(b, type(b)))
c = 1+0.5j # Un nombre complexe
print('La variable c = {} est de type {}'.format(c, type(c)))
msg = "Mon Premier TP !"
print(msg, type(msg), sep = '\n... | 0.121751 | 0.913368 |
# Strings
In previous lectures we have seen strings being used numerous times. Today we are going to go into a bit more detail. First, some terminology:
* 'single-quote character' refers to unicode character 34, --> { ' }
* 'double-quote character' refers to unicode character 39, --> { " }
* and if I say 'quote-cha... | github_jupyter | # wrapping text with double quotes...
cool_story_bro = ""Ahhh!!!! spiders!", cried the monster. "Do not worry" said our hero, "I have a sharp spoon"."
print(cool_story_bro)
# wrapping text with single quotes...
cool_story_bro = '"Ahhh!!!! spiders!", cried the monster."Do not worry" said our hero, "I have a sharp spoon"... | 0.227555 | 0.910147 |
```
from pomegranate import *
import seaborn
%pylab inline
seaborn.set_style('whitegrid')
numpy.set_printoptions(suppress=True)
```
# Naive Bayes and Bayes Classifiers: A Tutorial
author: Jacob Schreiber <br>
contact: jmschreiber91@gmail.com
Bayes classifiers are some of the simplest machine learning models that exi... | github_jupyter | from pomegranate import *
import seaborn
%pylab inline
seaborn.set_style('whitegrid')
numpy.set_printoptions(suppress=True)
X = numpy.concatenate((numpy.random.normal(3, 1, 200), numpy.random.normal(10, 2, 1000)))
y = numpy.concatenate((numpy.zeros(200), numpy.ones(1000)))
x1 = X[:200]
x2 = X[200:]
plt.figure(figsiz... | 0.562417 | 0.957118 |
# Time sequence primer using Pytorch
```
import torch
torch.__version__
from torch.utils import data
import torch.nn as nn
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
from torch.utils.tensorboard import SummaryWriter
from IPython import embed
import pandas as pd
```
## Time varying sign... | github_jupyter | import torch
torch.__version__
from torch.utils import data
import torch.nn as nn
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
from torch.utils.tensorboard import SummaryWriter
from IPython import embed
import pandas as pd
N = 1000
t = np.arange(N)
x = np.sin(0.01*t) + 0.2 * np.random.nor... | 0.878705 | 0.943504 |
# 上下文无关文法分析
**分析器**根据文法产生式处理输入的句子，并建立一个或多个符合文法的组成结构。文法是一个格式良好的声明规范，它实际上只是一个字符串，而不是程序。分析器是文法的解释程序，它搜索符合文法的所有树的空间找出一棵边缘有所需句子的树。
在本节中，我们将看到两个简单的分析算法，一种自上而下的方法成为递归下降分析，一种自下而上的方法成为移进-规约分析。我们也将看到一些更复杂的算法，一种带自下而上过滤的自上而下的方法称为左角落分析，一种动态规划技术称为图表分析。
## 递归下降分析
一种最简单的分析器将一个文法作为如何将一个高层次的目标分解成几个低层次的子目标的规范来解释。顶层的目标是找到一个 S，S -> NP ... | github_jupyter | import nltk
grammar1 = nltk.CFG.fromstring("""
S -> NP VP
VP -> V NP | V NP PP
PP -> P NP
V -> "saw" | "ate" | "walked"
NP -> "John" | "Mary" | "Bob" | Det N | Det N PP
Det -> "a" | "an" | "the" | "my"
N -> "man" | "dog" | "cat" | "telescope" | "park"
P -> "in" | "on" | "by" | "with"
""... | 0.272702 | 0.777596 |
# 批量规范化
:label:`sec_batch_norm`
训练深层神经网络是十分困难的，特别是在较短的时间内使他们收敛更加棘手。
在本节中，我们将介绍*批量规范化*（batch normalization） :cite:`Ioffe.Szegedy.2015`，这是一种流行且有效的技术，可持续加速深层网络的收敛速度。
再结合在 :numref:`sec_resnet`中将介绍的残差块，批量规范化使得研究人员能够训练100层以上的网络。
## 训练深层网络
为什么需要批量规范化层呢？让我们来回顾一下训练神经网络时出现的一些实际挑战。
首先，数据预处理的方式通常会对最终结果产生巨大影响。
回想一下我们应用多层感知机来预测房... | github_jupyter | import tensorflow as tf
from d2l import tensorflow as d2l
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps):
# 计算移动方差元平方根的倒数
inv = tf.cast(tf.math.rsqrt(moving_var + eps), X.dtype)
# 缩放和移位
inv *= gamma
Y = X * inv + (beta - moving_mean * inv)
return Y
class BatchNorm(tf.keras.layer... | 0.754192 | 0.769946 |
# Naive Bayes model
Naive Bayes is a classification technique used to build classifier using the Bayes Theorem. It assumes that predictors are different. In simple word sit assumes that the presence of a particular feature is not related in any way to the presence of another.
There are 3 type sof Naive Bayes models
... | github_jupyter | from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
label_names = data['target_names']
labels = data['target']
feature_names = data['feature_names']
features = data['data']
from sklearn.model_selection import train_test_split
train_data, test_data, train_label, test_label = train_test_split... | 0.615435 | 0.992647 |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
```
### LSTMs for Human Activity Recognition
Human Activity Recognition (HAR) using smartphones dataset and... | github_jupyter | from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# Activities are the class labels
# It is a 6 class classification
ACTIVITIES = {
0: 'WALKING',
1: 'WALK... | 0.690142 | 0.921287 |
<h1><center>Assignment 3</center></h1>
<h1><center>Data Classification</center></h1>
<br><br><br><br>
## Names:
### 1. Amr Hendy (46)
### 2. Abdelrhman Yasser (37)
## Introduction to MAGIC Gamma Telescope DataSet
The data are MC generated to simulate registration of high energy
gamma particles in a ground-based at... | github_jupyter | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from time import time
import matplotlib.patches as mpatches
def load_dataset():
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data'
attribute_names = ['fLength', 'fWidth', 'fSize', 'fConc', 'fConc1', 'fAsym', 'f... | 0.59749 | 0.987326 |
4 fold * 6 epochs
----
I was trying to clean some of my code so I can add more models. However, this can never happen without the awesome kernels from other talented Kagglers. Forgive me if I missed any.
* Based on SRK's kernel: https://www.kaggle.com/sudalairajkumar/a-look-at-different-embeddings
* Vladimir Demido... | github_jupyter | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | 0.767777 | 0.599749 |
# Simulación Montecarlo
> El método de Montecarlo es un método no determinista o estadístico numérico, usado para aproximar expresiones matemáticas complejas y costosas de evaluar con exactitud. El método se llamó así en referencia al Casino de Montecarlo (Mónaco) por ser “la capital del juego de azar”, al ser la rulet... | github_jupyter | from IPython.display import YouTubeVideo
YouTubeVideo('Y77WnkLbT2Q')
# Importar librería random
import random
help(random.choice)
# Escribir una función que genere el resultado
# de una caminata aleatoria de N pasos
def caminata_aleatoria(N):
s = [0]
for i in range(N):
Z = random.choice((-1,1))
... | 0.135032 | 0.980618 |
```
%load_ext autoreload
%autoreload 2
import tensorflow as tf
import numpy as np
import random
from tensorflow import keras
from tensorflow.keras import utils as np_utils
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, Dropout, ... | github_jupyter | %load_ext autoreload
%autoreload 2
import tensorflow as tf
import numpy as np
import random
from tensorflow import keras
from tensorflow.keras import utils as np_utils
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, Dropout, Batc... | 0.795181 | 0.586079 |
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