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9,800 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to generate histograms using YugabyteDB (PostgreSQL-compatible)
This provides and example of how to generate frequency histograms using YugabyteDB.
Disambiguation
Step2: Define the quer... | Python Code:
# connect to PostgreSQL using psycopg2
# !pip install psycopg2-binary
import psycopg2
# Connect to an existing database and create the test table
with psycopg2.connect("dbname=yugabyte user=yugabyte host=localhost port=5433") as yb_conn:
cur = yb_conn.cursor()
# use this drop statement if you... |
9,801 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 3
Step1: We can now import the deepchem package to play with.
Step2: MoleculeNet Overview
In the last two tutorials we loaded the Delaney dataset of molecular solubilities. Let's... | Python Code:
!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
Explanation: Tutorial 3: An Introduction To MoleculeNet
One of the most powerful f... |
9,802 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-5 (d)
Creates a plot of the torque-speed curve of the induction motor as depicted in Figure 6-23.
Note
Step1: ... | Python Code:
%pylab notebook
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 6 (Code examples)
Example 6-5 (d)
Creates a plot of the torque-speed curve of the induction motor as depicted in Figure 6-23.
Note: You should first click on "Cell → Run All" in order that the plots get generated.
Import ... |
9,803 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step2: Assignment 3b
Step3: Encoding issues with txt files
For Windows users, the file “AnnaKarenina.txt” gets the encoding cp1252.
In order to open the file, you have to a... | Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d .... |
9,804 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Columns to remove
Step1: Run decision tree
Step2: Check variance inflation factors
If the VIF is equal to 1 there is no multicollinearity among factors, but if the VIF is greater than 1, t... | Python Code:
plt.scatter(train['avg_blocktime_6'], train['avg_blocktime_60'])
Explanation: Columns to remove:
Possible leakage: avg_price_6, avg_price_60, avg_gasUsed_b_6, avg_gasUsed_t_6, avg_gasUsed_b_60, avg_gasUsed_t_60, avg_price_60
Poor performance: gasLimit_t, gasUsed_t, newContract, avg_uncle_count_6, avg_txcn... |
9,805 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load from here
Step1: https
Step2: Manual overwrites | Python Code:
dc=pd.read_csv('dcg.csv')
Explanation: Load from here
End of explanation
pop2=pd.read_csv('worldcities.csv')
pop2
def ccc(c,country):
if c=='Moscow region (oblast)': return 'Moscow'
if c=='Rostov-on-Don': return 'Rostov-na-Donu'
if c=='Gdansk, Gdynia and Sopot': return 'Gdansk+Gdynia'
if c=... |
9,806 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Polynomial Regression and Overfitting
Step1: In this notebook we want to discuss both <em style="color
Step2: Let us plot the data. We will use colors to distinguish between x1 and x2. T... | Python Code:
import numpy as np
import sklearn.linear_model as lm
Explanation: Polynomial Regression and Overfitting
End of explanation
np.random.seed(42)
N = 20 # number of data points
X1 = np.array([k for k in range(N)])
X2 = np.array([k + 0.2 * (np.random.rand() - 0.5) for k in ra... |
9,807 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Brian part 1
Step1: In Python, the notation ''' is used to begin and end a multi-line string. So the equations are just a string with one line per equation. The equations ar... | Python Code:
tau =
eqs = '''
'''
Explanation: Introduction to Brian part 1: Neurons
Adapted form brian2 tutorial
All Brian scripts start with the following. If you're trying this notebook out in IPython, you should start by running this cell.
Later we'll do some plotting in the notebook, so we activate inline plotting... |
9,808 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example notebook
Step1: Create a structure analysis object
If you include a 'mapfile' then we will use locus information to subsample just a single SNP from each locus so that the resulting... | Python Code:
# conda install ipyrad -c ipyrad
# conda install structure clumpp -c ipyrad
# conda install toytree -c eaton-lab
import ipyrad.analysis as ipa
import toyplot
Explanation: Example notebook: Structure with pop assignments
This notebook shows how to use the ipyrad.analysis toolkit to generate structure input ... |
9,809 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Logistic Regression with PyMC3
This is a reproduction with a few slight alterations of Bayesian Log Reg by J. Benjamin Cook
Author
Step1: The Adult Data Set is commonly used to ben... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn
import warnings
warnings.filterwarnings('ignore')
from collections import OrderedDict
from time import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from... |
9,810 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transport through a barrier
All systems so far had a flat potential. In this notebook, we will change this.
Basic setup (like in the previous notebook)
Step1: "MOSFET" toy model
Let's cons... | Python Code:
import numpy as np
import kwant
%run matplotlib_setup.ipy
from matplotlib import pyplot
lat = kwant.lattice.square()
Explanation: Transport through a barrier
All systems so far had a flat potential. In this notebook, we will change this.
Basic setup (like in the previous notebook):
End of explanation
def ... |
9,811 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementat... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of t... |
9,812 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiments analysis challenge on movie review
Designed by
Step1: Trainning and testing the model with cross validation.
Step2: The next cell may take some time.
Step3: Trainning the mode... | Python Code:
from __future__ import division, print_function
import pandas as pd
import numpy as np
data_dir = 'data/'
# Load Original Data / contains data + labels 10 k
train = pd.read_csv("../data/train.data")#.drop('id',axis =1 )
# Your validation data / we provide also a validation dataset, contains only data : 5k... |
9,813 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is largely based on material of the Python Scientific Lecture Notes (https
Step1: Note
Step2: Parameters
Mandatory parameters (positional arguments)
Step3: Optional paramete... | Python Code:
def the_answer_to_the_universe():
print(42)
the_answer_to_the_universe()
Explanation: This notebook is largely based on material of the Python Scientific Lecture Notes (https://scipy-lectures.github.io/), adapted with some exercises.
Reusing code
<div class="alert alert-danger">
<b>Rule of thumb</b>: <... |
9,814 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sockets can be configured to act as a server and listen for incoming messages, or connect to other applications as a client. After both ends of a TCP/IP socket are connected, communication i... | Python Code:
# %load socket_echo_server.py
import socket
import sys
# Create a TCP/IP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Bind the socket to the port
server_address = ('localhost', 10000)
print('starting up on {} port {}'.format(*server_address))
sock.bind(server_address)
# Listen for inco... |
9,815 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Compiler for a Fragment of C
This file shows how a simple compiler for a fragment of the programming language C can be implemented using Ply.
Specification of the Scanner
Step1: Th... | Python Code:
import ply.lex as lex
tokens = [ 'NUMBER', 'ID', 'EQ', 'NE', 'LE', 'GE', 'AND', 'OR',
'INT', 'IF', 'ELSE', 'WHILE', 'RETURN'
]
Explanation: A Simple Compiler for a Fragment of C
This file shows how a simple compiler for a fragment of the programming language C can be implemented using P... |
9,816 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quiz 2
Intelligent Systems 2016-1
After solving all the questions in the exam save your notebook with the name username.ipynb and submit it to
Step3: 1. (1.7)
Implement a MDP that solves th... | Python Code:
from mdp import *
from rl import *
Explanation: Quiz 2
Intelligent Systems 2016-1
After solving all the questions in the exam save your notebook with the name username.ipynb and submit it to: https://www.dropbox.com/request/0Eh9d2PvQMdAyJviK4Nl
End of explanation
class LinearMDP(GridMDP):
A two-dimensi... |
9,817 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 3
Imports
Step1: Using interact for animation with data
A soliton is a constant velocity wave that maintains its shape as it propagates. They arise from non-linear wave eq... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from math import sqrt
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 3
Imports
End of explanation
def soliton(x, t, c, a):
z = (0.5) * c * (((1) /... |
9,818 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook is a revised version of notebook from Sara Robinson and Ivan Chueng
E2E ML on GCP
Step1: Restart the kernel
After you install the additional packages, you need to restart the noteb... | Python Code:
import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
U... |
9,819 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Resit Assignment part A
Deadline
Step1: Please make sure you can load the English spaCy model
Step2: Exercise 1
Step3: Please test your function using the following function call
Step4: ... | Python Code:
import spacy
Explanation: Resit Assignment part A
Deadline: Tuesday, November 30, 2021 before 17:00
Please name your files:
ASSIGNMENT-RESIT-A.ipynb
utils.py (from part B)
raw_text_to_coll.py (from part B)
Please name your zip file as follows: RESIT-ASSIGNMENT.zip and upload it via Canvas (Resit Assignme... |
9,820 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Making predictions
Load Model
This notebook loads a model previously trained in 2_keras.ipynb or 3_eager.ipynb from earlier in the TensorFlow Basics workshop.
Note
Step2: Live Predictions
... | Python Code:
# In Jupyter, you would need to install TF 2 via !pip.
%tensorflow_version 2.x
## Load models from Drive (Colab only).
models_path = '/content/gdrive/My Drive/amld_data/models'
data_path = '/content/gdrive/My Drive/amld_data/zoo_img'
## Or load models from local machine.
# models_path = './amld_models'
# d... |
9,821 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What's going on with ROCAUC for Binary Classification?
We've identified a bug in ROCAUC
Step1: Binary Classification with 1D Coefficients or Feature Importances
When the function has 1D coe... | Python Code:
%matplotlib inline
import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
from yellowbrick.classifier import ROCAUC
from sklearn.model_selection import train_test_split
occupancy = pd.read_csv('data/occupancy/occupancy.cs... |
9,822 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Large-scale multi-label text classification
Author
Step1: Perform exploratory data analysis
In this section, we first load the dataset into a pandas dataframe and then perform
some basic ex... | Python Code:
from tensorflow.keras import layers
from tensorflow import keras
import tensorflow as tf
from sklearn.model_selection import train_test_split
from ast import literal_eval
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
Explanation: Large-scale multi-label text classification
Author: ... |
9,823 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Objective
Predict the survival of Titanic passengers using a K-Means algorithm.
Data Analysis
Data Import
Step1: Selection of Features
Step2: Cleaning Data
Step6: Experiment Heueristics (... | Python Code:
import pandas
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.cluster import KMeans
from pprint import pprint
TITANIC_TRAIN = 'train.csv'
TITANIC_TEST = 'test.csv'
# t_df refers to titanic_dataframe
t_df = pandas.read_csv(TITANIC_TRAIN, header=0)
Explanation: Objective... |
9,824 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The D... | Python Code:
%matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called... |
9,825 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Einops tutorial, part 1
Step1: Load a batch of images to play with
Step2: Composition of axes
transposition is very common and useful, but let's move to other capabilities provided by eino... | Python Code:
# Examples are given for numpy. This code also setups ipython/jupyter
# so that numpy arrays in the output are displayed as images
import numpy
from utils import display_np_arrays_as_images
display_np_arrays_as_images()
Explanation: Einops tutorial, part 1: basics
<!-- <img src='http://arogozhnikov.github.... |
9,826 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ZIKA CLASSIFICATION MODEL
IMPORTS
Step1: LOAD DATA
Step2: ALGORITHMS
Step3: TRAIN MODELS
Step4: OPTIMIZE N PRINCIPAL COMPONENTS
Step5: SAVE MODELS
Step6: RUN MODEL | Python Code:
# Algorithms
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
# Metrics
from sklearn.metrics import confusion_matrix, roc_curve, auc, accuracy_score
from sklearn.metrics import cla... |
9,827 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power spectral density (PSD) in a label
Returns an STC file containing the PSD (in dB) of each of the sources
within a label.
Step1: Set parameters
Step2: View PSD of source... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
Explanation: Compute source power spect... |
9,828 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Minimal Waveguide Example
In this example we show how to calculate the stationary paraxial field for a slab and cylindircal waveguide using PyPropagate.
Step1: Setting up the propagators
We... | Python Code:
from pypropagate import *
%matplotlib inline
Explanation: Minimal Waveguide Example
In this example we show how to calculate the stationary paraxial field for a slab and cylindircal waveguide using PyPropagate.
End of explanation
settings = presets.settings.create_paraxial_wave_equation_settings()
settings... |
9,829 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class 9
Step1: The Solow model with exogenous population growth
Now, let's suppose that production is a function of the supply of labor $L_t$
Step2: An alternative approach
Suppose that we... | Python Code:
# Initialize parameters for the simulation (A, s, T, delta, alpha, K0)
K0 = 20
T= 100
A= 10
alpha = 0.35
delta = 0.1
s = 0.15
# Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0
capital = np.zeros(T+1)
capital[0] = K0
# Compute all capital values by iterating over t... |
9,830 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Language Basics, IPython, and Jupyter Notebooks
Step1: The Python Interpreter
```python
$ python
Python 3.6.0 | packaged by conda-forge | (default, Jan 13 2017, 23
Step2: from numpy... | Python Code:
import numpy as np
np.random.seed(12345)
np.set_printoptions(precision=4, suppress=True)
Explanation: Python Language Basics, IPython, and Jupyter Notebooks
End of explanation
import numpy as np
data = {i : np.random.randn() for i in range(7)}
data
Explanation: The Python Interpreter
```python
$ python
Pyt... |
9,831 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boolean Generator
This notebook will show how to use the boolean generator to generate a boolean combinational function. The function that is implemented is a 2-input XOR.
Step 1
Step1: Ste... | Python Code:
from pynq.overlays.logictools import LogicToolsOverlay
logictools_olay = LogicToolsOverlay('logictools.bit')
Explanation: Boolean Generator
This notebook will show how to use the boolean generator to generate a boolean combinational function. The function that is implemented is a 2-input XOR.
Step 1: Downl... |
9,832 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pcygni-Profile Calculator Tool Tutorial
This brief tutorial should give you a basic overview of the main features and capabilities of the Python P-Cygni Line profile calculator, which is bas... | Python Code:
import matplotlib.pyplot as plt
Explanation: Pcygni-Profile Calculator Tool Tutorial
This brief tutorial should give you a basic overview of the main features and capabilities of the Python P-Cygni Line profile calculator, which is based on the Elementary Supernova Model (ES) of Jefferey and Branch 1990.
I... |
9,833 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Solutions to
Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 4)
Determining Important Nodes (cont'd)
Betweenness Centrality
If you interpret the Facebook graph as we... | Python Code:
from networkit import *
%matplotlib inline
cd ~/Documents/workspace/NetworKit
G = readGraph("input/PGPgiantcompo.graph", Format.METIS)
# Code for 7-1)
# exact computation
bc = centrality.Betweenness(G, True)
%time bc.run()
bc.ranking()[:15]
# Code for 7-2)
# approximate computation
bca = centrality.ApproxB... |
9,834 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Semi-supervision and domain adaptation with AdaMatch
Author
Step1: Before we proceed, let's review a few preliminary concepts underlying this example.
Preliminaries
In semi-supervised learn... | Python Code:
!pip install -q tf-models-official
Explanation: Semi-supervision and domain adaptation with AdaMatch
Author: Sayak Paul<br>
Date created: 2021/06/19<br>
Last modified: 2021/06/19<br>
Description: Unifying semi-supervised learning and unsupervised domain adaptation with AdaMatch.
Introduction
In this exampl... |
9,835 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SQL Bootcamp
Sarah Beckett-Hile | NYU Stern School of Business | March 2015
Today's plan
SQL, the tool of business
Relational Databases
Why can't I do this in Excel?
Setting up this course... | Python Code:
# check to see if support code is there
import os
print('List of files in working directory:')
[print(file) for file in os.listdir()]
file = 'SQL_support_code.py'
if not os.path.isfile(file):
raise Exception('***** Program halted, file missing *****')
Explanation: SQL Bootcamp
Sarah Beckett-Hile | NYU ... |
9,836 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time-frequency beamforming using DICS
Compute DICS source power [1]_ in a grid of time-frequency windows.
References
.. [1] Dalal et al. Five-dimensional neuroimaging
Step1: Read raw data
S... | Python Code:
# Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import csd_fourier
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path ... |
9,837 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Steady-state space-charge-limited current with traps
This example shows how to simulate effects of a single trap level on current-voltage characteristics of a single carrier device.
Step1: ... | Python Code:
%matplotlib inline
import matplotlib.pylab as plt
import oedes
import numpy as np
oedes.init_notebook() # for displaying progress bars
Explanation: Steady-state space-charge-limited current with traps
This example shows how to simulate effects of a single trap level on current-voltage characteristics of a... |
9,838 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 STYLE="background
Step1: <h4 style="border-bottom
Step2: <h4 style="border-bottom
Step3: <h2 STYLE="background
Step4: 上図から分かるように、奇数と偶数が等確率で出るルーレットを１０回プレイして、奇数と偶数が同じ回数だけ出る確率（５回ずつ出る確率）... | Python Code:
# 乱数を扱うためのライブラリをインポートする。
import random
sample_size = 10 # 乱数発生回数
# 一様乱数を dist に格納する （distribution : 分布）
dist = [random.random() for i in range(sample_size)]
# dist の中身を確認する。
dist
# 図やグラフを図示するためのライブラリをインポートする。
import matplotlib.pyplot as plt
%matplotlib inline
# ヒストグラムを描く。
plt.hist(dist)
plt.grid()
plt.show... |
9,839 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Manuscript7 - Compute percent of significant information transfers FROM source regions
Analysis for Fig. 7
Master code for Ito et al., 2017¶
Takuya Ito (takuya.ito@rutgers.edu)
Step1: 0.0 B... | Python Code:
import sys
sys.path.append('utils/')
import numpy as np
import loadGlasser as lg
import scipy.stats as stats
import matplotlib.pyplot as plt
import statsmodels.sandbox.stats.multicomp as mc
import sys
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import nibabel as nib
import os
impor... |
9,840 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression
In this example we will see how to classify images as horses or people using logistic regression. The tutorial builds upon the concepts introduced in the Objax basics tut... | Python Code:
%pip --quiet install objax
import matplotlib.pyplot as plt
import os
import numpy as np
import tensorflow_datasets as tfds
import objax
from objax.util import EasyDict
Explanation: Logistic Regression
In this example we will see how to classify images as horses or people using logistic regression. The tuto... |
9,841 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling and Simulation in Python
Chapter 1
Copyright 2020 Allen Downey
License
Step1: The first time you run this on a new installation of Python, it might produce a warning message in pin... | Python Code:
try:
import pint
except ImportError:
!pip install pint
import pint
try:
from modsim import *
except ImportError:
!pip install modsimpy
from modsim import *
Explanation: Modeling and Simulation in Python
Chapter 1
Copyright 2020 Allen Downey
License: Creative Commons Attribution 4.0 ... |
9,842 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read and dump calin and ACTL header and event
calin/examples/iact_data/read and dump calin and ACTL raw header and event from zfits file.ipynb - Stephen Fegan - 2017-03-09
Copyright 2017, St... | Python Code:
%pylab inline
import calin.iact_data.raw_actl_event_data_source
import calin.iact_data.telescope_data_source
import json
import struct
import base64
Explanation: Read and dump calin and ACTL header and event
calin/examples/iact_data/read and dump calin and ACTL raw header and event from zfits file.ipynb - ... |
9,843 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nearest neighbor classification
Arguably the most simplest classification method.
We are given example input vectors $x_i$ and corresponding class labels $c_i$ for $i=1,\dots, N$.
The colle... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
df = pd.read_csv(u'data/iris.txt',sep=' ')
df
X = np.hstack([
np.matrix(df.sl).T,
np.matrix(df.sw).T,
np.matrix(df.pl).T,
np.matrix(df.pw).T])
print X[:5] # sample view
c = np.matrix(df.c).T
print c[:5]... |
9,844 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Writing Cython
In this notebook, we'll take a look at how to implement a simple function using Cython. The operation we'll implement is the first-order diff, which takes in an array of lengt... | Python Code:
import numpy as np
x = np.random.randn(10000)
Explanation: Writing Cython
In this notebook, we'll take a look at how to implement a simple function using Cython. The operation we'll implement is the first-order diff, which takes in an array of length $n$:
$$\mathbf{x} = \begin{bmatrix} x_1 \ x_2 \ \vdots \... |
9,845 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Item-Based Collaborative Filtering
As before, we'll start by importing the MovieLens 100K data set into a pandas DataFrame
Step1: Now we'll pivot this table to construct a nice matrix of us... | Python Code:
import pandas as pd
r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3), encoding="ISO-8859-1")
m_cols = ['movie_id', 'title']
movies = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.item... |
9,846 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Evoked data structure
Step1: Creating Evoked objects from Epochs
Step2: You may have noticed that MNE informed us that "baseline correction" has been
applied. This happened automatical... | Python Code:
import os
import mne
Explanation: The Evoked data structure: evoked/averaged data
This tutorial covers the basics of creating and working with :term:evoked
data. It introduces the :class:~mne.Evoked data structure in detail,
including how to load, query, subselect, export, and plot data from an
:class:~mne... |
9,847 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Least squares problems
We sometimes wish to solve problems of the form
$$
\boldsymbol{A} \boldsymbol{x} = \boldsymbol{b}
$$
where $\boldsymbol{A}$ is a $m \times n$ matrix. If $m > n$, in ge... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# Use seaborn to style the plots and use accessible colors
import seaborn as sns
sns.set()
sns.set_palette("colorblind")
import numpy as np
N = 100
x = np.linspace(-1, 1, N)
def runge(x):
return 1 /(25 * (x**2) + 1)
plt.xlabel('$x$')
plt.ylabel('$y$')
... |
9,848 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theano example
Step1: The model
Logistic regression is a probabilistic, linear classifier. It is parametrized
by a weight matrix $W$ and a bias vector $b$. Classification is
done by project... | Python Code:
import os
import requests
import gzip
import six
from six.moves import cPickle
if not os.path.exists('mnist.pkl.gz'):
r = requests.get('http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz')
with open('mnist.pkl.gz', 'wb') as data_file:
data_file.write(r.content)
with gzip.open('m... |
9,849 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center>
<h1>Introduction to Jet Images and Computer Vision</h1>
<h3>Michela Paganini - Yale University</h3>
<h4>High Energy Phenomenology, Experiment and Cosmology Seminar Series</h4>
<img ... | Python Code:
import os
from keras.utils.data_utils import get_file
# Info for downloading the dataset from Zenodo
MD5_HASH = 'f9b11c46b6a0ff928bec2eccf865ecf0'
DATAFILE = 'jet-images_Mass60-100_pT250-300_R1.25_Pix25.hdf5'
URL_TEMPLATE = 'https://zenodo.org/record/{record}/files/{filename}'
print('[INFO] MD5 verificatio... |
9,850 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a LAS file from scratch
Step1: Step 1
Create some fake data, and make some of the values at the bottom NULL (numpy.nan). Note that of course every curve in a LAS file is recorded a... | Python Code:
import lasio
import datetime
import numpy
import os
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Building a LAS file from scratch
End of explanation
depths = numpy.arange(10, 50, 0.5)
fake_curve = numpy.random.random(len(depths))
fake_curve[-10:] = numpy.nan # Add some null values at t... |
9,851 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Development notebook
Step1: Photo-count detection
Theory
Stochastic master equation in Milburn's formulation
$\displaystyle d\rho(t) = dN(t) \mathcal{G}[a] \rho(t) - dt \gamma \mathcal{H}[\... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
Explanation: Development notebook: Tests for QuTiP's stochastic master equation solver
Copyright (C) 2011 and later, Paul D. Nation & Robert J. Johansson
In this notebook we test the qutip stochastic master equation s... |
9,852 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the Licens... | Python Code:
from collections import Counter
import math
def char2vec(word):
# Counts each of the the characters in word.
# We use a dictionary instead of a sparse matrix to describe the characters,
# however the concept is identical.
return Counter(word)
def support(v):
# The support of a vector over a basis... |
9,853 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recurrent Neural Networks for Vietnamese Name Entity Recognition
Step1: This the second part of the Recurrent Neural Network Tutorial. The first part is here.
In this part we will implement... | Python Code:
import csv
import itertools
import operator
import numpy as np
import nltk
import sys
from datetime import datetime
from utils import *
import matplotlib.pyplot as plt
%matplotlib inline
# Download NLTK model data (you need to do this once)
nltk.download("book")
Explanation: Recurrent Neural Networks for V... |
9,854 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading the output from sinsin2dtex.cu
We go from a flattened std
Step1: Quick aside on Wireframe plots in matplotlib
cf. mplot3d tutorial, matplotlib
Step2: EY | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import csv
ld = [ 1., 1.]
WIDTH = 640
HEIGHT = 640
print WIDTH*HEIGHT
hd = [ld[0]/(float(WIDTH)), ld[1]/(float(HEIGHT)) ]
with open('sinsin2dtex_result.csv','r') as csvfile_result:
plot_results = csv.reader(csvfile_result, delimiter=',')
result_li... |
9,855 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Maze Solver
In this notebook, we write a maze solver by solving the Poisson equation with two Dirichlet boundary conditions imposed on the two faces that correspond to the start and end of t... | Python Code:
# Install the required pmeal packages in the current Jupyter kernel
import sys
try:
import openpnm as op
except:
!{sys.executable} -m pip install openpnm
import openpnm as op
try:
import porespy as ps
except:
!{sys.executable} -m pip install porespy
import porespy as ps
import reque... |
9,856 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Признаки по одному
1.1. Количественные
Гистограмма и боксплот
Step1: 1.2. Категориальные
countplot
Step2: 2. Взаимодействия признаков
2.1. Количественный с количественным
pairplot, scat... | Python Code:
df['Total day minutes'].hist();
sns.boxplot(df['Total day minutes']);
df.hist();
Explanation: 1. Признаки по одному
1.1. Количественные
Гистограмма и боксплот
End of explanation
df['State'].value_counts().head()
df['Churn'].value_counts()
sns.countplot(df['Churn']);
sns.countplot(df['State']);
sns.countplo... |
9,857 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
https
Step1: Now read the train and test questions into list of questions.
Step2: Using keras tokenizer to tokenize the text and then do padding the sentences to 30 words
Step3: Now let u... | Python Code:
import os
import csv
import codecs
import numpy as np
import pandas as pd
np.random.seed(1337)
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
from keras.layers import Dense, Input, Flatten, merge, LSTM, L... |
9,858 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
This tutorial will show you how to use the Table-Cleaner validation framework.
First, let's import the necessary modules. My personal style is to abbreviate
the scientific python li... | Python Code:
import numpy as np
import pandas as pd
from IPython import display
import table_cleaner as tc
Explanation: Tutorial
This tutorial will show you how to use the Table-Cleaner validation framework.
First, let's import the necessary modules. My personal style is to abbreviate
the scientific python libraries wi... |
9,859 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pandas-validator example
This is example of pandas-validator in English.
Step1: Series Validator
Step2: DataFrame Validator
DataFrameValidator class can validate panda's dataframe object.
... | Python Code:
# Please install this package using following command.
# $ pip install pandas-validator
import pandas_validator as pv
import pandas as pd
import numpy as np
Explanation: pandas-validator example
This is example of pandas-validator in English.
End of explanation
# Create validator's instance
validator = pv.... |
9,860 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow Test
https
Step1: The Computational Graph
You might think of TensorFlow Core programs as consisting of two discrete sections
Step2: Notice that printing the nodes does not outpu... | Python Code:
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
Explanation: TensorFlow Test
https://www.tensorflow.org/get_started/
End of explanation
node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)... |
9,861 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parsing and cleaning tweets
This notebook is a slight modification of @wwymak's word2vec notebook, with different tokenization, and a way to iterate over tweets linked to their named user
W... | Python Code:
import gensim
import os
import numpy as np
import itertools
import json
import re
import pymoji
import importlib
from nltk.tokenize import TweetTokenizer
from gensim import corpora
import string
from nltk.corpus import stopwords
from six import iteritems
import csv
tokenizer = TweetTokenizer()
def keep_ret... |
9,862 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keras versus Poisonous Mushrooms
This example demonstrates building a simple dense neural network using Keras. The example uses Agaricus Lepiota training data to detect poisonous mushrooms.... | Python Code:
from pandas import read_csv
srooms_df = read_csv('../data/agaricus-lepiota.data.csv')
srooms_df.head()
Explanation: Keras versus Poisonous Mushrooms
This example demonstrates building a simple dense neural network using Keras. The example uses Agaricus Lepiota training data to detect poisonous mushrooms.
... |
9,863 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sommer 2017
HS 5
Step1: Die Bioscan GmbH plant ein System zur Zugangskontrolle. Dazu wurde bereits folgende Datenbank entwickelt und mit Testdaten gefüllt.
a) Liste aller Gebäude mit deren ... | Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/sommer_2017
Explanation: Sommer 2017
HS 5
End of explanation
%%sql
select G.*, R.*
from `gebaeude` G
left join Raum R on G.`GebID` = R.`GebID`
order by G.`Bezeichnung`, R.`Typ`
Explanation: Die Bioscan GmbH plant ein System zur Zugangskontrolle. Dazu wur... |
9,864 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: DDSP Synths and Effects
This notebook demonstrates the use of several of the Synths and Effects Processors in the DDSP library. While the core functions are also direc... | Python Code:
# Copyright 2021 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... |
9,865 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
03 - Sequence Model Approach
The more 'classical' approach to solving this problem
Train a model that can take any number of 'steps'
Makes a prediction on next step based on previous steps
L... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, LeakyReLU, Dropout, ReLU, GRU, TimeDistributed, Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tens... |
9,866 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
======================================================================
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)
================================================... | Python Code:
# Authors: Hari Bharadwaj <hari@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import numpy as np
from matplotlib import pyplot as plt
from mne import create_info, EpochsArray
from mne.baseline import ... |
9,867 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Задание 1
Вывести 10 самых больших по размеру треков жанра ROCK и формата MPEG
Step1: Задание 2
Вывести названия всех групп, их песен и названия их альбомов для всех треков жанра Рок, приоб... | Python Code:
%%sql
SELECT t
FROM tracks t
INNER JOIN genres g
ON t.genreid = g.genreid
INNER JOIN media_types m
ON m.mediatypeid = t.mediatypeid
ORDER BY t.bytes desc
limit 10
Explanation: Задание 1
Вывести 10 самых больших по размеру треков жанра ROCK и формата MPEG
End of explanation
%%sql
SELECT... |
9,868 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial for flexx.react - reactive programming
Also see http
Step1: First, we create two input signals
Step2: Input signals can be called with an argument to set their value
Step3: Now w... | Python Code:
from flexx import react
Explanation: Tutorial for flexx.react - reactive programming
Also see http://flexx.readthedocs.org/en/latest/react/
Where classic event-driven programming is about reacting to things that happen, RP is about staying up to date with changing signals. Signals are objects that have a v... |
9,869 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preamble
Step1: Notebook Environment
Step2: Example
Step3: Closed-form KL divergence between diagonal Gaussians
Step4: Monte Carlo estimation
The KL divergence is an expectation of log d... | Python Code:
%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
from keras import backend as K
from keras.layers import (Input, Activation, Dense, Lambda, Layer,
add, multiply)
from keras.models import... |
9,870 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Combining geosocial and financial data to understand retail performance
Geosocial data is location-based social media data that can be interpreted and analyzed as part of any location-orient... | Python Code:
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from cartoframes.auth import set_default_credentials
from cartoframes.data.observatory import *
from cartoframes.viz import *
from shapely import wkt
pd.set_option('display.max_columns', Non... |
9,871 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
png, jpeg와 SVG의 차이. 만약 scatter plot 같은 경우 scatter가 매우 많을 때에는 png보다 용량이 더 클 수 있다.
Step1: scale
Step2: cond number가 이상할 때(10000이 넘어갈 때). scale의 문제와 dependant 문제. 그래서 scale 과정을 거쳐서 1000 이하로 떨... | Python Code:
# sns.pairplot(df_all, diag_kind="kde", kind="reg")
# plt.show()
sns.jointplot("RM", "MEDV", data=df)
plt.show()
import statsmodels.api as sm
model = sm.OLS(df.ix[:, -1], df.ix[:, :-1])
result = model.fit()
print(result.summary())
Explanation: png, jpeg와 SVG의 차이. 만약 scatter plot 같은 경우 scatter가 매우 많을 때에는 pn... |
9,872 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook shows how to use an index file.<br/>
This example uses the index file from the Mediterranean Sea region (INSITU_MED_NRT_OBSERVATIONS_013_035) corresponding to the latest data.<... | Python Code:
indexfile = "datafiles/index_latest.txt"
Explanation: This notebook shows how to use an index file.<br/>
This example uses the index file from the Mediterranean Sea region (INSITU_MED_NRT_OBSERVATIONS_013_035) corresponding to the latest data.<br/>
If you download the same file, the results will be slightl... |
9,873 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
scipy.spatial
scipy.spatial can compute triangulations, Voronoi diagrams, and convex hulls of a set of points, by leveraging the Qhull library.
Moreover, it contains KDTree implementations f... | Python Code:
%matplotlib inline
import numpy as np
from scipy.spatial import Delaunay, ConvexHull, Voronoi
import matplotlib.pyplot as plt
points = np.random.rand(30, 2) # 30 random points in 2-D
tri = Delaunay(points)
hull = ConvexHull(points)
voronoi = Voronoi(points)
print "Neighbour triangles\n",tri.neighbors[0:5... |
9,874 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing PHOEBE 2 vs PHOEBE Legacy
NOTE
Step1: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Adding Datasets and Co... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Comparing PHOEBE 2 vs PHOEBE Legacy
NOTE: PHOEBE 1.0 legacy is an alternate backend and is not installed with PHOEBE 2.0. In order to run this backend, you'll need to have PHOEBE 1.0 installed and manually build the python bindings in the phoebe-py directory... |
9,875 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
New York University
Applied Data Science 2016 Final Project
Measuring household income under Redatam in CensusData
2. Merge Individual to Household Data
Project Description
Step1: DATA HAND... | Python Code:
# helper functions
import getEPH
import categorize
import createVariables
import schoolYears
import make_dummy
import functionsForModels
# libraries
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
from statsmo... |
9,876 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Gathering system data
Goals
Step4: If you want to stream command output, use subprocess.Popen and check carefully subprocess documentation!
Step7: Parsing /proc
Linux /proc filesyst... | Python Code:
import psutil
import glob
import sys
import subprocess
#
# Our code is p3-ready
#
from __future__ import print_function, unicode_literals
def grep(needle, fpath):
A simple grep implementation
goal: open() is iterable and doesn't
need splitlines()
goal: comprehension can filte... |
9,877 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Morse Code Neural Net
I created a text file that has the entire alphabet of numerical morse code. Meaning, "." is represented by the number "0.5" and "-" is represented by "1.0". This neural... | Python Code:
import NeuralNetImport as NN
import numpy as np
import NNpix as npx
from IPython.display import Image
Explanation: Morse Code Neural Net
I created a text file that has the entire alphabet of numerical morse code. Meaning, "." is represented by the number "0.5" and "-" is represented by "1.0". This neural n... |
9,878 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Report counts of GO terms at various levels and depths
Reports the number of GO terms at each level and depth.
Level refers to the length of the shortest path from the top.
Depth refer... | Python Code:
# Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
Explanation: Report counts of GO terms at various levels and depths
Reports the number of GO terms at each level and depth.
Level refers to the length of the shortest p... |
9,879 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Big Data Doesn't Exist
The recent opinion piece Big Data Doesn't Exist on Tech Crunch by Slater Victoroff is an interesting discussion about the usefulness of data both big and small. Slate... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import ConfigParser
import json
import indicoio
import requests
import xmltodict
from BeautifulSoup import BeautifulSoup
import time
import pickle
propertiesFile = "indico.properties"
cp = ConfigParser.ConfigParser()
... |
9,880 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Filter
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right
Step2: Examples
In the follow... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this fi... |
9,881 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: 2d embeddings
Step2: 1d embeddings
Step3: Clustering in 2d
1d embedding vs size of airline
* find what is similar
* what is an outlier
Step4: Making results more st... | Python Code:
!pip install -q tf-nightly-gpu-2.0-preview
import tensorflow as tf
print(tf.__version__)
!curl -O https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat
# pd.read_csv?
import pandas as pd
df = pd.read_csv('routes.dat', quotechar="'", sep=',', encoding='utf-8', header=None, na_values=... |
9,882 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Descrição do Problema
A empresa Amazon deseja obter um sistema inteligente para processar os comentários de seus clientes sobre os seus produtos, podendo classificar tais comentários dent... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Explanation: 1. Descrição do Problema
A empresa Amazon deseja obter um sistema inteligente para processar os comentários de seus clientes sobre os seus produtos, podendo classificar tais comentários dentre as categorias: positivo ou neg... |
9,883 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Excercise - Functional Programming
Q
Step1: Ans | Python Code:
names = ["Aalok", "Chandu", "Roshan", "Prashant", "Saurabh"]
for i in range(len(names)):
names[i] = hash(names[i])
print(names)
Explanation: Excercise - Functional Programming
Q: Try rewriting the code below as a map. It takes a list of real names and replaces them with code names produced using a mor... |
9,884 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Onset Detection
Automatic detection of musical events in an audio signal is one of the most fundamental tasks in music information retrieval. Here, we will show how to d... | Python Code:
x, fs = librosa.load('simpleLoop.wav', sr=44100)
print x.shape
Explanation: ← Back to Index
Onset Detection
Automatic detection of musical events in an audio signal is one of the most fundamental tasks in music information retrieval. Here, we will show how to detect an onset, the start of a musical ev... |
9,885 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson 38
Step1: This is the primary function of the webbrowser module, but it can be used as part of a script to improve web scraping. selenium is a more full featured web browser module.
... | Python Code:
import webbrowser
webbrowser.open('https://automatetheboringstuff.com')
Explanation: Lesson 38:
The Webbrowser Module
The webbrowser module has tools to manage a webbrowser from Python.
webbrowser.open() opens a new browser window at a url:
End of explanation
import webbrowser, sys, pyperclip
sys.argv # Pa... |
9,886 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
03 PyTorch CPU to GPU copy
Step1: Alloocate a PyTorch Tensor on the GPU | Python Code:
% reset -f
from __future__ import print_function
from __future__ import division
import math
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import torch
import sys
print('__Python VERSION:', sys.version)
print('__pyTorch VERSION:', torch.__version__)
print('__CUDA VERSION')
from subp... |
9,887 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Input luminosity function
Step1: Run the simulation, save the spectra
Step2: Simulation outputs
Step3: the table of simulated quasars, including redshift, luminosity, synthetic flux/mags ... | Python Code:
M1450 = linspace(-30,-22,20)
zz = arange(0.7,3.5,0.5)
ple = bossqsos.BOSS_DR9_PLE()
lede = bossqsos.BOSS_DR9_LEDE()
for z in zz:
if z<2.2:
qlf = ple if z<2.2 else lede
plot(M1450,qlf(M1450,z),label='z=%.1f'%z)
legend(loc='lower left')
xlim(-21.8,-30.2)
xlabel("$M_{1450}$")
ylabel("log Phi")... |
9,888 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Prepare catalogs
Step1: Prioritize
```
x = base catalog on Dropbox
; CORRECT FOR EXTINCTION
r = x.r - x.extinction_r
i = x.i - x.extinction_i
g = x.g - x.extinction_g
; DE... | Python Code:
nmstoget = ('Dune', 'AnaK', 'Odyssey', 'Gilgamesh', 'OBrother', 'Narnia', 'Catch22')
hosts_to_target = [h for h in hsd.values() if h.name in nmstoget]
assert len(hosts_to_target)==len(nmstoget)
new_targets = [hosts.NSAHost(145729), hosts.NSAHost(21709)]
hosts_to_target.extend(new_targets)
# now set to the ... |
9,889 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
检索，查询数据
这一节学习如何检索pandas数据。
Step1: Python和Numpy的索引操作符[]和属性操作符‘.’能够快速检索pandas数据。
然而，这两种方式的效率在pandas中可能不是最优的，我们推荐使用专门优化过的pandas数据检索方法。而这些方法则是本节要介绍的。
多种索引方式
pandas支持三种不同的索引方式：
* .loc 是基本的基于labe... | Python Code:
import numpy as np
import pandas as pd
Explanation: 检索，查询数据
这一节学习如何检索pandas数据。
End of explanation
dates = pd.date_range('1/1/2000', periods=8)
dates
df = pd.DataFrame(np.random.randn(8,4), index=dates, columns=list('ABCD'))
df
panel = pd.Panel({'one':df, 'two':df-df.mean()})
panel
Explanation: Python和Nump... |
9,890 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Titanic
In this notebook, I explore the titanic data set provided by Kaggle, to try and predict survival rates.
To process the data, certain missing values were replaced with medians or ave... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import sklearn
from sklearn.ensemble import (RandomForestClassifier, RandomForestRegressor)
from sklearn.linear_model import LogisticRegression
from sklearn import svm
from sklearn.learning_curve import validation_curve
import sknn.mlp
import matplo... |
9,891 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimating the effect of a Member Rewards program
An example on how DoWhy can be used to estimate the effect of a subscription or a rewards program for customers.
Suppose that a website has... | Python Code:
# Creating some simulated data for our example
import pandas as pd
import numpy as np
num_users = 10000
num_months = 12
signup_months = np.random.choice(np.arange(1, num_months), num_users) * np.random.randint(0,2, size=num_users) # signup_months == 0 means customer did not sign up
df = pd.DataFrame({
... |
9,892 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vectorized Operations
not necessary to write loops for element-by-element operations
pandas' Series objects can be passed to MOST NumPy functions
documentation
Step1: add Series without loo... | Python Code:
import pandas as pd
import numpy as np
my_dictionary = {'a' : 45., 'b' : -19.5, 'c' : 4444}
my_series = pd.Series(my_dictionary)
my_series
Explanation: Vectorized Operations
not necessary to write loops for element-by-element operations
pandas' Series objects can be passed to MOST NumPy functions
documenta... |
9,893 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
automaton.has_bounded_lag
Check if the transducer has bounded lag, i.e. that the difference of length between the input and output words is bounded, for every word accepted.
It is a pre-cond... | Python Code:
import vcsn
ctx = vcsn.context("lat<lan_char(ab), lan_char(xy)>, b")
ctx
a = ctx.expression(r"'a,x''b,y'*'a,\e'").automaton()
a
Explanation: automaton.has_bounded_lag
Check if the transducer has bounded lag, i.e. that the difference of length between the input and output words is bounded, for every word ac... |
9,894 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GlobalAveragePooling2D
[pooling.GlobalAveragePooling2D.0] input 6x6x3, data_format='channels_last'
Step1: [pooling.GlobalAveragePooling2D.1] input 3x6x6, data_format='channels_first'
Step2:... | Python Code:
data_in_shape = (6, 6, 3)
L = GlobalAveragePooling2D(data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 * np.random.random(data_in_shape)... |
9,895 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Following on from Guide to the Sequential Model
10 May 2017 - WH Nixalo
Getting started with the Keras Sequential model
The Sequential model is a linear stack of layers.
You can create a Seq... | Python Code:
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential([Dense(32, input_shape=(784,)),
Activation('relu'),
Dense(10),
Activation('softmax'),])
Explanation: Following on from Guide to the Sequential Model
... |
9,896 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Get The Data
You can get the data on Kaggle's site.
Step2: Data Cleaning
Step3: Sex
Here we convert the gender labels (male, female) into a dummy variable (1, 0).
Step4: Emb... | Python Code:
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score
import csv as csv
Explanation: Title: Titanic Competition With Random Forest
Slug: titanic_competition_with_random_f... |
9,897 | Given the following text description, write Python code to implement the functionality described below step by step
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 is made available under the FSF-approve... | Python Code:
%matplotlib inline
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
import csv
Explanation: <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 is made available under the FSF-appr... |
9,898 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
逻辑斯特回归示例
逻辑斯特回归
正则化后的逻辑斯特回归
Step1: 逻辑斯特回归
Step2: 逻辑斯特回归假设
$$ h_{\theta}(x) = g(\theta^{T}x)$$
$$ g(z)=\frac{1}{1+e^{−z}} $$
Step3: 其实scipy包里有一个函数可以完成一样的功能
Step4: 求偏导(梯度)
$$ \frac{\delta ... | Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from sklearn.preprocessing import PolynomialFeatures
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set... |
9,899 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting started with the Keras Sequential model
The Sequential model is a linear stack of layers.
Step1: Same neural network architecture as before, but now in keras
Step2: Compilation Ste... | Python Code:
# simulate data
X, Y = backprop_make_classification()
plt.scatter(X[:, 0], X[:, 1], c=Y.argmax(1))
Explanation: Getting started with the Keras Sequential model
The Sequential model is a linear stack of layers.
End of explanation
model = Sequential()
model.add(Dense(3, input_dim=2)) # input layer is implici... |
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