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700 | <SYSTEM_TASK:>
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Description:
CIFAR 10
Wayne Nixalo
2018/2/20-21
A redo of the Fast.ai DL1L7 cifar10 notebook for some PyTorch practice.
Data
Step1: Something changed, or I forgot ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.conv_learner import *
PATH = 'data/cifar10/'
os.makedirs(PATH, exist_ok=True)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats = (np.array([ 0.4914 , 0.48216, 0.44653]), n... |
701 | <SYSTEM_TASK:>
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Description:
This is the second part in a series of notes on my exploration of the recently released Google QuickDraw dataset, using the concurrently released Sketc... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import tensorflow as tf
from matplotlib.animation import FuncAnimation
from matplotlib.path import Path... |
702 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Generative Adversarial Networks
Generative Adversarial Networks are invented by Ian Goodfellow (https
Step1: MNIST database
The MNIST database (Modifi... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from keras.datasets import mnist
import keras
from keras.layers import Input, UpSampling2D, Conv2DTranspose, Conv2D, LeakyReLU
from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten
from keras.models import Sequential
from keras.optimizers import RMSprop... |
703 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-3', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: BCC
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics... |
704 | <SYSTEM_TASK:>
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Description:
Generalized Watts-Strogatz Graph Experiments Output Visualization
Step1: Parse results
Step2: PageRank Seeds Percentage
How many times the "Top X" no... | <ASSISTANT_TASK:>
Python Code:
#!/usr/bin/python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from stats import parse_results, get_percentage, get_avg_per_seed, draw_pie, draw_bars, draw_bars_comparison, draw_avgs
Explanation: Generalized Watts-Strogatz Graph Experiments Output Visualization
En... |
705 | <SYSTEM_TASK:>
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Description:
Step1: Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation ... | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver impo... |
706 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Automated ML for time series predicion
We used one of the dataset in Numenta Anomaly Benchmark (NAB) for demo, i.e. NYC taxi passengers dataset, which ... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
Explanation: Automated ML for time series predicion
We used one of the dataset in Numenta Anomaly Benchmark (NAB) for demo, i.e. NYC taxi passengers dataset, which contains 10320 records, each indicating the total number of taxi passengers in NYC at a co... |
707 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This notebook was created by Sergey Tomin for Workshop
Step1: Change RF parameters for the comparison with ASTRA
Step2: Initializing SpaceCharge
Step... | <ASSISTANT_TASK:>
Python Code:
# the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
from time import time
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import deepcopy
# import ... |
708 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Lecture 2 - Logic, Loops, and Arrays
This iPython notebook covers some of the most important aspects of the Python language that is used daily by real ... | <ASSISTANT_TASK:>
Python Code:
#Example conditional statements
x = 1
y = 2
x<y #x is less than y
#x is greater than y
x>y
#x is less-than or equal to y
x<=y
#x is greater-than or equal to y
x>=y
Explanation: Lecture 2 - Logic, Loops, and Arrays
This iPython notebook covers some of the most important aspects of the Pyth... |
709 | <SYSTEM_TASK:>
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Description:
The simplest native coroutine demo
(that I could imagine)
Step1: The driving code starts here
Step2: A slightly more interesting demo
Now the generat... | <ASSISTANT_TASK:>
Python Code:
import types
@types.coroutine
def gen():
yield 42
async def delegating():
await gen()
Explanation: The simplest native coroutine demo
(that I could imagine)
End of explanation
coro = delegating()
coro
coro.send(None)
# coro.send(None) # --> StopIteration
Explanation: The driving ... |
710 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2019 The TensorFlow Authors.
Step1: 深度卷积生成对抗网络
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
S... | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... |
711 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
EventVestor
Step1: Let's go over the columns
Step2: <a id='pipeline'></a>
Pipeline Overview
Accessing the data in your algorithms & research
The only... | <ASSISTANT_TASK:>
Python Code:
# import the dataset
from quantopian.interactive.data.eventvestor import mergers_and_acquisitions_free as dataset
# or if you want to import the free dataset, use:
#from quantopian.data.eventvestor import buyback_auth_free
# import data operations
from odo import odo
# import other librar... |
712 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
hot-CNO and breakout
Step1: This collection of rates has the main CNO rates plus a breakout rate into the hot CNO cycle
Step2: To evaluate the rates,... | <ASSISTANT_TASK:>
Python Code:
import pynucastro as pyrl
Explanation: hot-CNO and breakout
End of explanation
files = ["c12-pg-n13-ls09",
"c13-pg-n14-nacr",
"n13--c13-wc12",
"n13-pg-o14-lg06",
"n14-pg-o15-im05",
"n15-pa-c12-nacr",
"o14--n14-wc12",
"o15--n1... |
713 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Calculating Annotation Coverage
This section shows how to calculate annotation coverage as described here
Step1: 2. Read associations
2a. You can read... | <ASSISTANT_TASK:>
Python Code:
# Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz
from goatools.base import download_ncbi_associations
gene2go = download_ncbi_associations()
Explanation: Calculating Annotation Coverage
This section shows how to calculate annotation coverage as described here:
Annotation coverage... |
714 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
kochbar (7M) Results Analysis - MCMC - 100 iter-5Fold
Step1: User Rating Average and Standard Deviation Histograms
Step2: Item Rating Average and Sta... | <ASSISTANT_TASK:>
Python Code:
Analyse('kochbar\\Output', ['kochbar_ds.','kochbar_ds2.','kochbar_ds3.','kochbar_ds4.','kochbar_ds5.','kochbar_ds6.','kochbar_ds7.'], 100)
print 'kochbar_ds - Rating + Users + Item\nkochbar_ds2 - Rating + Users + Item + AvgRatUser\nkochbar_ds3 - Rating + Users + Item + AvgRatUser + StdDev... |
715 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Statistics
Step1: Provide one or two visualizations that show the distribution of the sample data. Write one or two sentences noting what you observe ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (16.0, 8.0)
df = pandas.read_csv('./stroopdata.csv')
df.describe()
Explanation: Statistics: The Science of Decisions Project Instructions
Background Information
In a Stroop task, participants... |
716 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Interpolation Exercise 1
Step1: 2D trajectory interpolation
The file trajectory.npz contains 3 Numpy arrays that describe a 2d trajectory of a particl... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
Explanation: Interpolation Exercise 1
End of explanation
# YOUR CODE HERE
with np.load('trajectory.npz') as data:
x = data['x']
t=data['t']
y=data... |
717 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Global Ocean Waves Analysis
As a part of the continuous Marine Data support, this time Planet OS Team releases a Meteo France Global Ocean Waves Analys... | <ASSISTANT_TASK:>
Python Code:
import os
from dh_py_access import package_api
import dh_py_access.lib.datahub as datahub
import xarray as xr
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import imageio
import shutil
import datetime
import matplotlib as mpl
mpl.rcParams['fon... |
718 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Enron POI Classifier
general imports and mods
Step1: sklearn imports
Step2: load data
Step3: original classifier
Step4: data-record snapshot
Step5:... | <ASSISTANT_TASK:>
Python Code:
import sys
import os
from time import time
import re
import pickle
sys.path.append("ud120-projects/tools/")
sys.path.append("ud120-projects/final_project/")
#sys.path.append("ud120-projects/maildir/")
import numpy as np
import pandas as pd
#from matplotlib import pyplot as plt
#import sea... |
719 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Classification and Regression
There are two major types of supervised machine learning problems, called classification and regression.
In classificatio... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Classification and Regression
There are two major types of supervised machine learning problems, called classification and regression.
In classification, the goal is to predict a class l... |
720 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Handwritten Digit Recognition
This tutorial guides you through a classic computer vision application
Step1: We plot the first 10 images and print thei... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import urllib
import gzip
import struct
def download_data(url, force_download=True):
fname = url.split("/")[-1]
if force_download or not os.path.exists(fname):
urllib.urlretrieve(url, fname)
return fname
def read_data(label_url, image_url)... |
721 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
AdaptiveMD
Example 1 - Setup
0. Imports
Step1: We want to stop RP from reporting all sorts of stuff for this example so we set a specific environment ... | <ASSISTANT_TASK:>
Python Code:
import sys, os
Explanation: AdaptiveMD
Example 1 - Setup
0. Imports
End of explanation
# verbose = os.environ.get('RADICAL_PILOT_VERBOSE', 'REPORT')
os.environ['RADICAL_PILOT_VERBOSE'] = 'ERROR'
Explanation: We want to stop RP from reporting all sorts of stuff for this example so we set a... |
722 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2019 The TensorFlow Authors.
Step1: 사전 제작 Estimator
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
S... | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... |
723 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<small> <i> This notebook was prepared by Marco Guajardo. For license visit github </i> </small>
.
Challenge Notebook
Problem
Step1: Unit Test
<b> The... | <ASSISTANT_TASK:>
Python Code:
def reverse_words (S):
#TODO: implement me
pass
Explanation: <small> <i> This notebook was prepared by Marco Guajardo. For license visit github </i> </small>
.
Challenge Notebook
Problem: Given a string of words, return a string with the words in reverse
Constraits
Test Cases
Alg... |
724 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Análisis de los datos obtenidos
Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción. Los datos analizados son del fil... | <ASSISTANT_TASK:>
Python Code:
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos... |
725 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
The Inference Button
Step1: Generating data
Create some toy data to play around with and scatter-plot it.
Essentially we are creating a regression li... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pymc3 import *
import numpy as np
import matplotlib.pyplot as plt
Explanation: The Inference Button: Bayesian GLMs made easy with PyMC3
Author: Thomas Wiecki
This tutorial appeared as a post in a small series on Bayesian GLMs on my blog:
The Inference Button: Baye... |
726 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
The goal here is to make a model where you put something into working memory, then leave it there for a while, and then clear the working memory. When... | <ASSISTANT_TASK:>
Python Code:
dimensions = 10
input_scale = 1
n_neurons_per_dim = 50
intercept_low = -0.5
intercept_high = 1.0
tau_input = 0.01
tau_recurrent = 0.1
tau_reset = 0.2
max_rate_high = 200
max_rate_low = 150
sensory_delay = 0.05
reset_scale = 0.3
model = nengo.Network()
with model:
vocab = spa.Vocabular... |
727 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Matplotlib Exercise 2
Imports
Step1: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Matplotlib Exercise 2
Imports
End of explanation
!head -n 30 open_exoplanet_catalogue.txt
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar pla... |
728 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-6
Step1: First, initialize the values needed in this program.
Step2: ... | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-6:
Creates and plot of the torque-speed curve of an induction motor with a double-cage rotor design as depicted in Figure 6-29.
Note: You should first click on "Cell → Run All... |
729 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from utils import *
import random
from random import shuffle
Explanation: Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. L... |
730 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
WAve Models (WAM) Usage Example
WAM wave models are most widely used wave models in the world.
This notebook illustrates ways of using WAM models data... | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import urllib.request
import numpy as np
import simplejson as json
import pandas as pd
from netCDF4 import Dataset, date2num, num2date
import ipywidgets as widgets
from IPython.display import display, clear_output
import dateutil.parser
import matplotlib.pyplot as plt... |
731 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Creating an advanced interactive map with Bokeh
This page demonstrates, how it is possible to visualize any kind of geometries (normal geometries + Mul... | <ASSISTANT_TASK:>
Python Code:
from bokeh.palettes import YlOrRd as palette #Spectral6 as palette
from bokeh.plotting import figure, save
from bokeh.models import ColumnDataSource, HoverTool, LogColorMapper
from bokeh.palettes import RdYlGn10 as palette
import geopandas as gpd
import pysal as ps
import numpy as np
# ... |
732 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Byte 4
Step1: Custom functions and global variables
Step2: Dataset
Step3: Next, read the two documents describing the dataset (data/ACS2015_PUMS_REA... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import os
from IPython.display import Image
from IPython.display import display
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing imp... |
733 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Gaussian Process Regression in Pytorch
Thomas Viehmann, tv@lernapparat.d
... | <ASSISTANT_TASK:>
Python Code:
from matplotlib import pyplot
%matplotlib inline
import IPython
import torch
from torch import nn
from torch.autograd import Variable
import numpy
Explanation: Gaussian Process Regression in Pytorch
Thomas Viehmann, tv@lernappara... |
734 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Training Ensemble on MNIST Dataset
On the function points branch of nengo
On the vision branch of nengo_extras
Step1: Represent each number using a on... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import nengo
import numpy as np
import scipy.ndimage
import matplotlib.animation as animation
from matplotlib import pylab
from PIL import Image
import nengo.spa as spa
import cPickle
import random
from nengo_extras.data import load_mnist... |
735 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: No... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
numbers = {0: "", 1:"one", 2:"two", 3:"three", 4:"four", 5:"five", 6:"six", 7:"seven", 8:"eight", 9:"nine", 10:"ten", 11:"eleven",
12:"twelve", 13:"thirteen", 14:"fourteen", 15:"fifteen", 16:"sixteen", 17:"seventeen", 18:"eighteen", 19:"nineteen",
20... |
736 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction
This notebook demonstrates how to perform phase and electrochemical assessments starting from a VASP calculation using Python Materials Ge... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pymatgen.core import Composition, Element
from pymatgen.ext.matproj import MPRester
from pymatgen.io.vasp import Vasprun
from pymatgen.phasediagram.maker import PhaseDiagram, CompoundPhaseDiagram
from pymatgen.phasediagram.analyzer import PDAnalyzer
from pymatgen.p... |
737 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Today
Step1: Q. Is the following call sequence acceptable?
Step2: No! The following are all OK!
Step3: Keyword arguments
Step4: The first two argum... | <ASSISTANT_TASK:>
Python Code:
from math import exp
# Could avoid this by using our constants.py module!
h = 6.626e-34 # MKS
k = 1.38e-23
c = 3.00e8
def intensity(wave, temp, mydefault=0):
wavelength = wave / 1e10
B = 2 * h * c**2 / (wavelength**5 * (exp(h * c / (wavelength * k * temp)) - 1))
return B
Expl... |
738 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Styling
New in version 0.17.1
<span style="color
Step1: Here's a boring example of rendering a DataFrame, without any (visible) styles
Step2: Note
St... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot
# We have this here to trigger matplotlib's font cache stuff.
# This cell is hidden from the output
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), ... |
739 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
← Back to Index
Spectral Features
For classification, we're going to be using new features in our arsenal
Step1: librosa.feature.spectral_bandwid... | <ASSISTANT_TASK:>
Python Code:
x, fs = librosa.load('simple_loop.wav')
IPython.display.Audio(x, rate=fs)
spectral_centroids = librosa.feature.spectral_centroid(x, sr=fs)
plt.plot(spectral_centroids[0])
Explanation: ← Back to Index
Spectral Features
For classification, we're going to be using new features in our ar... |
740 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Python API to EasyForm
Step1: You can access the values from the form by treating it as an array indexed on the field names
Step2: The array works bo... | <ASSISTANT_TASK:>
Python Code:
from beakerx import *
f = EasyForm("Form and Run")
f.addTextField("first")
f['first'] = "First"
f.addTextField("last")
f['last'] = "Last"
f.addButton("Go!", tag="run")
f
Explanation: Python API to EasyForm
End of explanation
"Good morning " + f["first"] + " " + f["last"]
f['last'][::-1] +... |
741 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Algorithms Exercise 1
Imports
Step3: Word counting
Write a function tokenize that takes a string of English text returns a list of words. It should al... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
Explanation: Algorithms Exercise 1
Imports
End of explanation
s = "this is a test\n here it is"
print(s.splitlines())
s.split(" ")
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t\n'... |
742 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
How HDBSCAN Works
HDBSCAN is a clustering algorithm developed by Campello, Moulavi, and Sander. It extends DBSCAN by converting it into a hierarchical ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets as data
%matplotlib inline
sns.set_context('poster')
sns.set_style('white')
sns.set_color_codes()
plot_kwds = {'alpha' : 0.5, 's' : 80, 'linewidths':0}
Explanation: How HDBSCAN Works
HDBSCAN i... |
743 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Predicting Student Admissions with Neural Networks
In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of ... | <ASSISTANT_TASK:>
Python Code:
# Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
data[:10]
Explanation: Predicting Student Admissions with Neural Networks
In this notebook... |
744 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<small><i>This notebook was prepared by Thunder Shiviah. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem
Step1: Unit Test... | <ASSISTANT_TASK:>
Python Code:
def list_primes(n):
# TODO: Implement me
pass
Explanation: <small><i>This notebook was prepared by Thunder Shiviah. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement list_primes(n), which returns a list of primes up to n (inclusive).
Constrain... |
745 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
练习 1：写程序，可由键盘读入用户姓名例如Mr. right，让用户输入出生的月份与日期，判断用户星座，假设用户是金牛座，则输出，Mr. right，你是非常有性格的金牛座！。
Step1: 练习 2：写程序，可由键盘读入两个整数m与n(n不等于0)，询问用户意图，如果要求和则计算从m到n的和输出，... | <ASSISTANT_TASK:>
Python Code:
name=input('请输入你的姓名，回车结束：')
print(name,'你好！')
month=int(input('请输入你的出生月份，回车结束：'))
date=int(input('请输入你的出生日期，回车结束：'))
print('你的生日是：',month,'月',date,'日')
if month == 3:
if date >= 21:
print(name,'，你是白羊座。')
else:
print(name,'，你是双鱼座。')
if month == 4:
if date >= 20:... |
746 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Machine Learning Engineer Nanodegree
Capstone Project
Step1: Load the Dataset
Keras provides api to download and load the mnist dataset in a single li... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from numpy import random
from keras.datasets import mnist # helps in loading the MNIST dataset
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D... |
747 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The diff... | <ASSISTANT_TASK:>
Python Code:
# Create a sample of Gaussian draws
np.random.seed(0)
x_data = np.random.randn(1000)
Explanation: Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The difference with Hist is that the binning is ... |
748 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Getting started in scikit-learn with the famous iris dataset
From the video series
Step1: Machine learning on the iris dataset
Framed as a supervised ... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import IFrame
IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)
Explanation: Getting started in scikit-learn with the famous iris dataset
From the video series: Introduction to machine learning with scikit-learn
A... |
749 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Two coins refresher
Given a world that consist of bunch random events (2 coins for simplicity) we are interested in quantifying the probability of diff... | <ASSISTANT_TASK:>
Python Code:
#Your code here
Explanation: Two coins refresher
Given a world that consist of bunch random events (2 coins for simplicity) we are interested in quantifying the probability of different combinations of the world state.
The state space
The combination of all possible outcomes is called the... |
750 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Comparing the output of pip in the magical venv that can run the alexnet_based and a fresh install that can't. Have saved both of these to different te... | <ASSISTANT_TASK:>
Python Code:
cd ..
!cat magical.freeze
!cat fresh.freeze
Explanation: Comparing the output of pip in the magical venv that can run the alexnet_based and a fresh install that can't. Have saved both of these to different text files:
End of explanation
magical = []
with open("magical.freeze") as f:
f... |
751 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Process an observation
Setup the processing
Step1: Fetch all the image documents from the metadata store. We then filter based off image status and me... | <ASSISTANT_TASK:>
Python Code:
# Default input parameters (replaced in next cell)
sequence_id = '' # e.g. PAN012_358d0f_20191005T112325
# Unused option for now. See below.
# vmag_min = 6
# vmag_max = 14
position_column_x = 'catalog_wcs_x'
position_column_y = 'catalog_wcs_y'
input_bucket = 'panoptes-images-processed'
#... |
752 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Layer Gradient Checks
Here, we use numerical gradient checking to verify the backpropagation correctness of all layers in the Layers folder. We should... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
import LearnyMcLearnface as lml
Explanation: Layer Gradient Checks
Here, we use numerical gradient checking to verify the backpropagation correctness of all layers in the Layers folder. We should expect to see very small nonzero value... |
753 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
English Wikipedia page views, 2008 - 2017
For this assignment, your job is to analyze traffic on English Wikipedia over time, and then document your pr... | <ASSISTANT_TASK:>
Python Code:
import requests
import pandas
endpoint = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers={'User-Agent' : 'https://github.com/r1rajiv92', 'From' : 'rajiv92@uw.edu'}
yearMonthCombinations = { '2015' : [ 7, 8, 9, ... |
754 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2017 Google LLC.
Step1: # Pandas 간단 소개
학습 목표
Step2: Pandas의 기본 데이터 구조는 두 가지 클래스로 구현됩니다.
DataFrame은 행 및 이름 지정된 열이 포함된 관계형 데이터 테이블이라고 생각할 수 있... | <ASSISTANT_TASK:>
Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... |
755 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Module 5- Other Forms of Visualization
author
Step1: The following module creates a pi chart that illustrates the breakdown of which isotope is measur... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import csv
import io
import urllib.request
import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np
url = 'https://radwatch.berkeley.edu/sites/default/files/pictures/rooftop_tmp/weather.csv'
response = urllib.request.urlopen(url)
r... |
756 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Plotting HYCOM Global Ocean Forecast Data
Note
Step2: Let's choose a location near Oahu, Hawaii...
Step3: Important! You'll need to replace ap... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import dateutil.parser
import datetime
from urllib.request import urlopen, Request
import simplejson as json
def extract_reference_time(API_data_loc):
Find reference time that corresponds to most complete forecast. ... |
757 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
An Attractive Representation of Count Data
I like to play with ways to visualise data, in particular with how to represent data in the most meaningful,... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
before = np.loadtxt('Data/questionnaire_before.txt',delimiter=',')
after = np.loadtxt('Data/questionnaire_after.txt',delimiter=',')
form = np.loadtxt('Data/questionnaire_form.txt',delimiter=',')
questions = np.array(ran... |
758 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<h1><span style="color
Step1: Simulate example data
Step2: Input data file
Step3: Population assignments
Step4: Filter missing data and convert to ... | <ASSISTANT_TASK:>
Python Code:
# conda install ipyrad ipcoal -c conda-forge -c bioconda
import ipyrad.analysis as ipa
import toytree
import ipcoal
print('ipyrad', ipa.__version__)
print('toytree', toytree.__version__)
print('ipcoal', ipcoal.__version__)
Explanation: <h1><span style="color:gray">ipyrad-analysis toolkit:... |
759 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Consuming API
Instead of using web scraping, using an API, application programming interface, is the preferred method.
Usually, you need to register in... | <ASSISTANT_TASK:>
Python Code:
!pip install omdb
Explanation: Consuming API
Instead of using web scraping, using an API, application programming interface, is the preferred method.
Usually, you need to register in order to use an API. But we will use a freely available API called Open Movie Database API.
1. Install Pyt... |
760 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
Step1: 2. Get Cloud P... | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" ... |
761 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
WEB_API Scrapper
Step1: I would like to get the Best Seller list for the Month of October 2015. First I signed up to the New York Times API, and after... | <ASSISTANT_TASK:>
Python Code:
import urllib2
import json
import pandas as pd
Explanation: WEB_API Scrapper
End of explanation
url = urllib2.urlopen('http://api.nytimes.com/svc/books/v3/lists/2015-10-01/hardcover-fiction.json?callback=books&sort-by=rank&sort-order=DESC&api-key=efb1f6ff386ce33c0b913d44bce40fd8%3A10%3A73... |
762 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Network Analysis--Using Null Models
Adapted from Professor Clauset's lectures and homeworks for Network Analysis and Modeling // Course page
Step1: Gr... | <ASSISTANT_TASK:>
Python Code:
#relatively fast networks package (pip install python-igraph) that I used for these homeworks
import igraph
# slow-and-steady networks package. fewer bugs, easier drawing
import networkx as nx
# plots!
import matplotlib.pyplot as plt
from matplotlib import style
%matplotlib inline
# othe... |
763 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
False positive and false negatives
This notebook explores the two sources of systematic error that we identify and trim in our datasets.
Step1: False ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
Explanation: False positive and false negatives
This notebook explores the two sources of systematic error that we identify and trim in our datasets.
End of explanation
stats2 = pd.read_csv("los... |
764 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Matplot lib - All your plotting functions under one roof (almost!)
Matplotlib is a simple (most of the time) plotting library.
So let's have a try!
St... | <ASSISTANT_TASK:>
Python Code:
# This will plot a simple scatter graph of points.
# The points will have all different sizes just for visual appearance, as well as varied colours
%matplotlib inline
# Import the required libraries
import numpy as np
import matplotlib.pyplot as plt
# Lets say we want to plot 50 points
N... |
765 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Silicon Forest Math Series<br/>Oregon Curriculum Network
Introduction to Public Key Cryptography
Here in the Silicon Forest, we do not expect ev... | <ASSISTANT_TASK:>
Python Code:
import pprint
def primes():
generate successive prime numbers (trial by division)
candidate = 1
_primes_so_far = [2] # first prime, only even prime
yield _primes_so_far[0] # share it!
while True:
candidate += 2 # check odds only from now on
for ... |
766 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Frequentism and Bayesianism II
Step1: In other words, we'd give Bob the following odds of winning
Step2: So we've estimated using frequentist ideas t... | <ASSISTANT_TASK:>
Python Code:
p_hat = 5. / 8.
freq_prob = (1 - p_hat) ** 3
print("Naïve Frequentist Probability of Bob Winning: {0:.2f}".format(freq_prob))
Explanation: Frequentism and Bayesianism II: When Results Differ
Mario Juric & Jake VanderPlas, University of Washington
e-mail: mjuric... |
767 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: Where Am I?
Startup.ML Conference - San Francisco - Jan 20, 2017
Who Am I?
Chris Fregly
Research Scientist @ PipelineIO
Video Series Author "Hig... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
import time
# make things wide
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
from IPython.display import clear... |
768 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Regression
This section follows the section 7.10 of Modern Statistical Methods in Astronomy by Feigelson and Babu
ordinary least squares
The statsmodel... | <ASSISTANT_TASK:>
Python Code:
import statsmodels
statsmodels.__version__
import pandas as pd
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.sandbox.regression.predstd import wls_prediction_std
import matplotlib.pyplot as plt
Explanation: Regression
This section f... |
769 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Formulas & Automata generators
The spot.gen package contains the functions used to generate the patterns produced by genltl and genaut.
Step1: LTL pat... | <ASSISTANT_TASK:>
Python Code:
import spot
import spot.gen as sg
spot.setup()
from IPython.display import display
Explanation: Formulas & Automata generators
The spot.gen package contains the functions used to generate the patterns produced by genltl and genaut.
End of explanation
sg.ltl_pattern(sg.LTL_AND_GF, 3)
sg.lt... |
770 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Synthetic Seismogram Widget
Using the Notebook
This is the <a href="https
Step1: 1 Normal Incidence Seismogram
Backgrounds
Step2: 1.2 Depth to Time c... | <ASSISTANT_TASK:>
Python Code:
# Import the necessary packages
%matplotlib inline
from SimPEG.utils import download
from geoscilabs.seismic.syntheticSeismogram import InteractLogs, InteractDtoT, InteractWconvR, InteractSeismogram
from geoscilabs.seismic.NMOwidget import ViewWiggle, InteractClean, InteractNosiy, NMOs... |
771 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Plot Gaze Timeline with Kinematic Events (shooting study)
Input
Step1: Coding errors
shotFired not always accurate. So create new var
Step2: Find gaz... | <ASSISTANT_TASK:>
Python Code:
## set to full width if not using theme
#from IPython.core.display import display, HTML
#display(HTML("<style>.container { width:100% !important; }</style>"))
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import string
import matplotlib.patches as mpatches
import ... |
772 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Using and Creating Labels
Labels are a way to 'bookmark' certain pores for easier lookup later, such as specifying boundary conditions. When networks ... | <ASSISTANT_TASK:>
Python Code:
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
import numpy as np
Explanation: Using and Creating Labels
Labels are a way to 'bookmark' certain pores for easier lookup later, such as specifying boundary conditions. When networks are generated they include a set of re... |
773 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Functions
Functions are blocks of code identified by a name, which can receive ""predetermined"" parameters or not ;).
In Python, functions
Step... | <ASSISTANT_TASK:>
Python Code:
def caps(val):
caps returns double the value of the provided value
return val*2
a = caps("TEST ")
print(a)
print(caps.__doc__)
Explanation: Functions
Functions are blocks of code identified by a name, which can receive ""predetermined"" parameters or not ;).
In Python, f... |
774 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: OLS Analysis Using Full PSU dataset
Step3: Partitioning a dataset in training and test sets
Step4: Determine Feature Importances
Test Predicti... | <ASSISTANT_TASK:>
Python Code:
#Import required packages
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
def format_date(df_date):
Splits Meeting Times and Dates into datetime objects where applicable using regex.
df_date['Days'] = df_date['Meeting_Times'].str.ex... |
775 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
OrderExtend example with boat image
This note shows how the OrderExtend algorithm performs the matrix completion task on a partially observed image dat... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from model import OrderExtend
img = ndimage.imread('images/boat.jpeg', flatten=True)
img /= np.max(img) #normalize image [0,1]
plt.imshow(img, cmap = cm.Greys_r)
Expl... |
776 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
textblob
Step1: Vamos a crear nuestro primer ejemplo de textblob a través del objeto TextBlob. Piensa en estos textblobs como una especie de cadenas d... | <ASSISTANT_TASK:>
Python Code:
from textblob import TextBlob
Explanation: textblob: otro módulo para tareas de PLN (NLTK + pattern)
textblob es una librería de procesamiento del texto para Python que permite realizar tareas de Procesamiento del Lenguaje Natural como análisis morfológico, extracción de entidades, anális... |
777 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from mpl_toolkits.mplot3d import Axes3D
from numpy.linalg import eigvals
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a C... |
778 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Intro to MongoDB
Makes sense for data science applications
widely used in big data
document database or associative array
python dictionary
Resources
M... | <ASSISTANT_TASK:>
Python Code:
import pprint
def get_client():
from pymongo import MongoClient
return MongoClient('mongodb://localhost:27017/')
def get_db():
# 'examples' here is the database name. It will be created if it does not exist.
db = get_client().examples
return db
def add_city(db... |
779 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction to Python part VI (And a discussion of random vectors)
Activity 1
Step1: We can access elements of a list using indices – numbered positi... | <ASSISTANT_TASK:>
Python Code:
odds = [1, 3, 5, 7]
print('odds are:', odds)
Explanation: Introduction to Python part VI (And a discussion of random vectors)
Activity 1: Discussion of multiple random variables
How is the notion of the expected value extended into multiple variables? What does this represent?
What is a ... |
780 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sentiment Classification & How To "Frame Problems" for a Neural Network
by Andrew Trask
Twitter
Step1: Note
Step2: Lesson
Step3: Project 1
Step4: W... | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... |
781 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Dependencies
Step1: Loading Data
First, we want to create our word vectors. For simplicity, we're going to be using a pretrained model.
As one of the... | <ASSISTANT_TASK:>
Python Code:
# Tensorflow
import tensorflow as tf
print('Tested with TensorFlow 1.2.0')
print('Your TensorFlow version:', tf.__version__)
# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff
# Rnn common functions
from tensorflow.contrib.le... |
782 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction
This notebook gives examples for processing spins monitor data.
Logging data is stored in monitors that are defined within the optimizatio... | <ASSISTANT_TASK:>
Python Code:
## Import libraries necessary for monitor data processing. ##
from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
from spins.invdes.problem_graph import log_tools
## Define filenames. ##
# `save_folder` is the full path to the directory cont... |
783 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
FD_1D_DX8_DT2 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author
Step1: Input Parameter
Step2: Preparation
Step3: Creat... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
Explanation: FD_1D_DX8_DT2 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the fir... |
784 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<a href="https
Step1: De esta manera, Python estandariza el aspecto del código desde la definición del lenguaje.
Nota
Step2: Los operadores para vari... | <ASSISTANT_TASK:>
Python Code:
if 2 + 3 == 5:
x = 5 + 3
mensaje = "Verdadero!"
else:
x = 5 - 3
mensaje = "Falso!"
print(x)
print(mensaje)
Explanation: <a href="https://www.python.org/"><img src="./Imagenes/python-logo.png" alt="Python Logo" style="width: 200px; display:inline;"/></a>
Python es un l... |
785 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Benchmark concepts
This notebook goes through all concepts and runs the query using EXPLAIN ANALYZE. This is useful for informing how long the queries ... | <ASSISTANT_TASK:>
Python Code:
import os
import re
import psycopg2
import getpass
from collections import OrderedDict
# database config
sqluser=getpass.getuser()
# keep sqlpass blank if using peer authentication
sqlpass=''
# database
sqldb='mimic'
sqlschema='public,mimiciii'
query_schema = 'set search_path to ' + sqlsc... |
786 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Contents
Introduction
Removing Features
Introduction
This IPython notebook illustrates how to remove features from feature table.
First, we need to imp... | <ASSISTANT_TASK:>
Python Code:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
Explanation: Contents
Introduction
Removing Features
Introduction
This IPython notebook illustrates how to remove features from feature table.
First, we need to import py_entitymatching package... |
787 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a se... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Explanation: Language Translation
In this proj... |
788 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
迷你项目：蒙特卡洛方法
在此 notebook 中，你将自己编写很多蒙特卡洛 (MC) 算法的实现。
虽然我们提供了一些起始代码，但是你可以删掉这些提示并从头编写代码。
第 0 部分：探索 BlackjackEnv
请使用以下代码单元格创建 Blackjack 环境的实例。
Step1: 每个状态都... | <ASSISTANT_TASK:>
Python Code:
import gym
env = gym.make('Blackjack-v0')
Explanation: 迷你项目：蒙特卡洛方法
在此 notebook 中，你将自己编写很多蒙特卡洛 (MC) 算法的实现。
虽然我们提供了一些起始代码，但是你可以删掉这些提示并从头编写代码。
第 0 部分：探索 BlackjackEnv
请使用以下代码单元格创建 Blackjack 环境的实例。
End of explanation
STICK = 0
HIT = 1
Explanation: 每个状态都是包含以下三个元素的 3 元组：
- 玩家的当前点数之和 $\in... |
789 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2019 The TensorFlow Authors.
Step1: 乱数の生成
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: tf.... | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... |
790 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Tables to Networks, Networks to Tables
Networks can be represented in a tabular form in two ways
Step1: At this point, we have our stations and trips ... | <ASSISTANT_TASK:>
Python Code:
stations = pd.read_csv('datasets/divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online date'], index_col='id')
stations
trips = pd.read_csv('datasets/divvy_2013/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'], index_col=['trip_id'])
trips = trips.sort()
trips
Explanation:... |
791 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
When analyzing data, I usually use the following three modules. I use pandas for data management, filtering, grouping, and processing. I use numpy for ... | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
import toyplot
import toyplot.pdf
import toyplot.png
import toyplot.svg
print('Pandas version: ', pandas.__version__)
print('Numpy version: ', numpy.__version__)
print('Toyplot version: ', toyplot.__version__)
Explanation: When analyzing data, I usually use t... |
792 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
1 - Design parameters
<img src="https
Step1: System Height
From Geometry Expressions, we obtain
Step2: Lens Hole Radius
From Geometry Expressions, we... | <ASSISTANT_TASK:>
Python Code:
w, h, b, d, c1, c2, k1, k2, r_sys, r_ref = symbols("w, h, b, d, c_1, c_2, k_1, k_2, r_{sys}, r_{ref}", real=True)
# Constraints for hyperboloids:
k1_constraint = k1 > 2
k2_constraint = k2 > 2
c1_constraint = c1 > 0
c2_constraint = c2 > 0
xw, yw, zw = symbols("x_w, y_w, z_w", real=True)
# ... |
793 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Radial Wavefunctions and Quantum Defects
In this tutorial we show how to access quantum defects and wavefunctions, which are used for the computation o... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
Explanation: Radial Wavefunctions and Quantum Defects
In this tutorial we show how to access quantum defects and wavefunctions, which are used for the computation of matrix elements, using the Python API. Some aspects of this are discussed in Appendix A of the pairinte... |
794 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Naive Bayes Male or Female Multivariate
author
Step1: Since we are simply using two Multivariate Gaussian Distributions, our Naive Bayes model is very... | <ASSISTANT_TASK:>
Python Code:
from pomegranate import *
import numpy as np
Explanation: Naive Bayes Male or Female Multivariate
author: Nicholas Farn [<a href="sendto:nicholasfarn@gmail.com">nicholasfarn@gmail.com</a>]
This example shows how to create a Multivariate Guassian Naive Bayes Classifier using pomegranate. I... |
795 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Generate Region of Interests (ROI) labeled arrays for simple shapes
This example notebook explain the use of analysis module "skxray/core/roi" https
St... | <ASSISTANT_TASK:>
Python Code:
import skxray.core.roi as roi
import skxray.core.correlation as corr
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import LogNorm
import xray_vision.mpl_plotting as mpl_plot
Explanation: Generate Regi... |
796 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
<hr width=80%>
<center> Data Cleaning & Bigrams</center>
<hr width=80%>
Data Cleaning
Loading the data
Removing missing data
Adding 'yearmonth'
Stemmin... | <ASSISTANT_TASK:>
Python Code:
all_data_list = []
for year in range(1990,2017):
data = pd.read_csv('{}_Output.csv'.format(year), header=None)
all_data_list.append(data) # list of dataframes
data = pd.concat(all_data_list, axis=0)
data.columns = ['id','date','headline', 'lead']
data.head()
Explanation: <hr width... |
797 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
11 - Advanced Level Example - Modeling an Agriculture+PV (AGRIPV) Site
This journal shows how to model an AgriPV site, calculating the irradiance not o... | <ASSISTANT_TASK:>
Python Code:
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_11')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
print ("Your simulation will be stored in %s" % testfolder)
from bifacial_radiance import... |
798 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Modeling and Simulation in Python
Case study.
Copyright 2017 Allen Downey
License
Step1: Unrolling
Let's simulate a kitten unrolling toilet paper. As... | <ASSISTANT_TASK:>
Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
Explanatio... |
799 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
Given a pandas DataFrame, how does one convert several binary columns (where 1 denotes the value exists, 0 denotes it doesn't) into a single c... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'A': [1, 0, 0, 0, 1, 0],
'B': [0, 1, 0, 0, 0, 1],
'C': [0, 0, 1, 0, 0, 0],
'D': [0, 0, 0, 1, 0, 0]})
df["category"] = df.idxmax(axis=1)
<END_TASK> |
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