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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import all radars
Step2: Import a specific radar
Step3: Import a specific radar site
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Python Code:
# Import radar module
%pylab inline
from davitpy.pydarn.radar import *
radars = network()
print radars
# How to get the total number of radars
print len(radars)
print radars.nradar
# How to get a specific radar from the mountain of recorded radars
print radars.getRadarByCode("bks")
# is ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The module provide for each of the file resources a class, with for each of the classes the methods download_file and list_files defined
Step2: ... | <ASSISTANT_TASK:>
Python Code:
import datamover as dm
print(dm.LocalConnector.list_files, dm.LocalConnector.download_file)
print(dm.S3Connector.list_files, dm.S3Connector.download_file)
print(dm.FTPConnector.list_files, dm.FTPConnector.list_files)
print(dm.GithubConnector.list_files, dm.GithubConnector.download_file)
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Place text search
Step2: Place details - get more information once we have a place
Step3: Now let's look up a series of places! We'll store ou... | <ASSISTANT_TASK:>
Python Code:
import requests
search_endpoint = 'https://maps.googleapis.com/maps/api/place/textsearch/json'
search_params = {
'query': 'Länggass Stübli',
'key': 'AIzaSyCNx-klDCfhopV6W_QPFZ0iwv5sp1J0XwQ',
'language': 'en'
}
r = requests.get( search_endpoint, params=search_params)
r.jso... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First investigate sensitivity of the LogisiticModels to different seeds
Step2: Essentially no difference when setting the seed for different ru... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from vessel_scoring import data, utils
from vessel_scoring.models import train_model_on_data
from vessel_scoring.evaluate_model import evaluate_model, compare_models
from IPython.core.display import display, HTML, Markdown
import numpy as np
import sys
from sklearn impo... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Then we need some imports
Step3: We define a simple function that returns our training dataset as a Ray Dataset
Step5: Now we define a simple ... | <ASSISTANT_TASK:>
Python Code:
!pip install -qU "ray[tune]" sklearn xgboost_ray comet_ml
import ray
from ray.air import RunConfig
from ray.air.result import Result
from ray.train.xgboost import XGBoostTrainer
from ray.tune.integration.comet import CometLoggerCallback
from sklearn.datasets import load_breast_cancer
de... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's untar the file unless it has already been untarred.
Step2: The UW3-500 dataset is a collection of text line images and corresponding grou... | <ASSISTANT_TASK:>
Python Code:
!test -f uw3-500.tgz || wget -nd http://www.tmbdev.net/ocrdata/uw3-500.tgz
!test -d book || tar -zxvf uw3-500.tgz
!ls book/0005/010001.*
!dewarp=center report_every=500 save_name=test save_every=10000 ntrain=11000 ../clstmctc uw3-500.h5
!ls book/*/*.bin.png | sort -r > uw3.files
!sed 1... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript. You also have the optio... | <ASSISTANT_TASK:>
Python Code:
from data_generator import vis_train_features
# extract label and audio features for a single training example
vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()
from IPython.display import Markdown, display
from data_generator impor... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Rectangle and Triangle Pulses Defined
Step2: Consider an interactive version of the above
Step3: More Signal Plotting
Step5: Custom Piecewise... | <ASSISTANT_TASK:>
Python Code:
t = arange(-4,4,.01)
x = cos(2*pi*t)
plot(t,x)
grid()
t = arange(-5,5,.01)
x_rect = ss.rect(t-3,2)
x_tri = ss.tri(t+2,1.5)
subplot(211)
plot(t,x_rect)
grid()
ylabel(r'$\Pi((t-3)/2)$');
subplot(212)
plot(t,x_tri)
grid()
xlabel(r'Time (s)')
ylabel(r'$\Lambda((t+2)/1.5)$');
tight_layout()
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We Can Proud of Ourselves 💃
Step2: Are we done ?! 🍰
Step3: When a file does not exists or when the file is not a valid json we raise FileNo... | <ASSISTANT_TASK:>
Python Code:
! pip install typeguard rollbar returns tenacity > /dev/null 2>&1
import contextlib
import json
import logging
import pathlib
import os
from typing import Union
import requests
from typeguard import typechecked
# Naive code snippets
def get_relevant_restaurants(user):
base_url = "http... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Network
Step2: Graphviz
Step3: ParameterGraph
Step4: Parameter
Step5: DomainGraph
Step6: MorseDecomposition
Step7: MorseGraph
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Python Code:
import DSGRN
network = DSGRN.Network("network.txt")
print(network)
print(network.graphviz())
import graphviz
graph = graphviz.Source(network.graphviz())
graph
parametergraph = DSGRN.ParameterGraph(network)
print("There are " + str(parametergraph.size()) + " nodes in the parameter graph... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Doubly Linked List
Step2: Stack
Step3: ```
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Python Code:
# Node of a Singly Linked List
class Node:
# constructor
def __init__(self, data=None, next=None):
self.data = data
self.next = next
# method of getter, setter for data
def setData(self, data):
self.data = data
def getData(self):
re... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Mutiple Plots on One Graph
Step2: Save it to a File
Step3: Adjust the Axes
Step4: Add a Grid
Step5: Change Line Types and Colors
Step6: Lab... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-3, 3, 0.01)
plt.plot(x, norm.pdf(x))
plt.show()
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Vamos a hacer una prueba en primer lugar
Step2: En la gráfica anterior podemos ver no solo la posición en el primer cuadro, si no tambien la ve... | <ASSISTANT_TASK:>
Python Code:
from generacion_trayectorias import grafica_trayectoria
%matplotlib inline
ts, qs, q̇s, q̈s = grafica_trayectoria(0, 2, 0, 1, 1000)
from numpy import pi
τ = 2*pi
ts, q1, q̇1, q̈1 = grafica_trayectoria(0, 2, 0, τ/4, 100)
ts, q2, q̇2, q̈2 = grafica_trayectoria(2, 4, τ/4, -τ/6, 100)
ts, q... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Get the MNIST data
Step2: Create the Network
Step3: Set up the Loss Function
Step4: Set up the Training Function
Step5: Set up the Initializ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
import gzip
import pickle
# Seed for reproducibility
np.random.seed(42)
# Download the MNIST digits dataset (only if not present locally)
import os
import urllib.request
mnist_data = './data/MN... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1 - Problem Statement
Step2: The characters are a-z (26 characters) plus the "\n" (or newline character), which in this assignment plays a role... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from utils import *
import random
data = open('dinos.txt', 'r').read()
data= data.lower()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('There are %d total characters and %d unique characters in your data.' % (data_size, vocab_size))
char... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading an mp3 file
Step2: Trim it and make it a 2d.
Step3: Let's make it a batch of 4 items
Step4: A Keras model
Step5: The model has no tr... | <ASSISTANT_TASK:>
Python Code:
import librosa
import kapre
import tensorflow as tf
from tensorflow.keras.models import Sequential
import numpy as np
from datetime import datetime
now = datetime.now()
print('%s/%s/%s' % (now.year, now.month, now.day))
print('Tensorflow: {}'.format(tf.__version__))
print('Librosa: {}'.fo... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First, let's a create a phase diagram to show the logistic map's fixed-point attractor at 0.655 when the growth rate parameter is set to 2.9
Ste... | <ASSISTANT_TASK:>
Python Code:
import IPython.display as IPdisplay
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pynamical
from pynamical import simulate, save_fig, phase_diagram, phase_diagram_3d
%matplotlib inline
title_font = pynamical.get_title_font()
label_font = pynamical.get_label... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We will use this helper funciton to write lists containing article ids, categories, and authors for each article in our database to local file.
... | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow as tf
import numpy as np
from google.cloud import bigquery
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-centr... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Build a base image to work with fairing
Step2: Start an AI Platform job
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Python Code:
BUCKET = "gs://" # your bucket here
assert re.search(r'gs://.+', BUCKET), 'A GCS bucket is required to store your results.'
!cat Dockerfile
!docker build . -t {base_image}
!docker push {base_image}
additional_files = '' # If your code requires additional files, you can specify them her... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we'll load the text file and convert it into integers for our network to use.
Step3: Now I need to split up the data into batches, and in... | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Univariate normal
Step3: Grid algorithm
Step4: Update
Step5: Posterior distribution of sigma
Step6: Posterior distribution of mu
Step7: Pos... | <ASSISTANT_TASK:>
Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py and create directories
import os
if not os.path.exists('utils.py'):
!wget https:/... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First reload the data we generated in notmist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Step3:... | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Build a Shyft model
Step2: Create a collection of simulation cells
Step4: So the first step is to extract these from the netcdf file, and g... | <ASSISTANT_TASK:>
Python Code:
# Pure python modules and jupyter notebook functionality
# first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%matplotlib inline
import os
import datetime as dt
import numpy as n... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Sockets are the endpoints of a bidirectional communications channel.
Step2: To check if the socket is actually created one can check using the... | <ASSISTANT_TASK:>
Python Code:
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# This creates a socket
# AF_INET => family ipv4.
# SOCK_STREAM => TCP protocol.
import socket
def client_handler(client_sock):
# Do things here
# .send() Takes byte type object
# b' it indicates that the ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now let's re-create Figure 2
Step2: Now let's see what the performance is as we vary different parameters. To do this, I'm using pytry, a simp... | <ASSISTANT_TASK:>
Python Code:
# the facilitation spikes
def stim_1_func(t):
index = int(t/0.001)
if index in [100, 1100, 2100]:
return 1000
else:
return 0
# the trigger spikes
def stim_2_func(t):
index = int(t/0.001)
if index in [90, 1500, 2150]:
return 1000
else:
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Use star value of different reviews to filter comments.
Step2: <b>Cleaning and Preprocessing</b>
Step3: <b>Preparing Document-Term Matrix</b>
... | <ASSISTANT_TASK:>
Python Code:
good_app = app.loc[app['weighted_rating'] >=4.0]
bad_app = app.loc[app['weighted_rating'] <=2.5]
good_app = good_app.reset_index(drop=True)
bad_app = bad_app.reset_index(drop=True)
category = app['category']
cate_list = []
for i in category.unique():
cate = i.lower()
cate_list.app... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Map Basics
Step2: The map above (run the code cell if you don't see it) shows a disconnected network of 10 intersections. The two intersections... | <ASSISTANT_TASK:>
Python Code:
# Run this cell first!
from helpers import Map, load_map, show_map
from student_code import shortest_path
%load_ext autoreload
%autoreload 2
map_10 = load_map('map-10.pickle')
show_map(map_10)
map_10.intersections
# this shows that intersection 0 connects to intersections 7, 6, and 5
m... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Comparing the Errors
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Python Code:
num_states = 8
gamma = 0.9
true_values = gamma**np.arange(num_states)[::-1]
d_pi = np.ones(num_states)/num_states
D_pi = np.diag(d_pi)
print("True values:")
print(true_values)
print("On-policy distribution:")
print(d_pi)
def compute_value_dct(theta_lst, features):
return [{s: np.dot(t... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Read in the hanford.csv file
Step2: <img src="images/hanford_variables.png">
Step3: 4. Calculate the coefficient of correlation (r) and gen... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.formula.api as smf
df=pd.read_csv('/home/sean/git/algorithms/class6/data/hanford.csv')
df
df.describe()
lm = smf.ols(formula="Mortality~Exposure",data=df).fit()
lm.params
intercept, slope = lm.pa... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Assessing the veracity of semantic markup for dataset pages
Step2: Import Modules
Step3: Upload Dataset
Step4: Load dataset in pandas.DataFra... | <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... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lets start by generating some behavioral data from the social influence task. Here green advice/choice is encoded as 0 and the blue advice/choic... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
sns.set(style = 'white', color_codes = True)
%matplotlib inline
import sys
import os
import os
cwd = os.getcwd()
sys.path.append(cwd[:-len('befit/examples/social_influence')])... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Preprocessing
Step2: Visualization of ELC usage data
Step3: Calendar heat map of sign-ins
Step4: Sign-ins by course
Step5: Sign-ins by hour ... | <ASSISTANT_TASK:>
Python Code:
#@title
#%%capture
import numpy as np #Linear algebra
import pandas as pd #Time series, datetime object manipulation
import matplotlib.pyplot as plt #plotting
#import seaborn as sb
#plt.style.use('fivethirtyeight') #Plot style preferred by author.
import calendar
from tabulate import tab... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Creating counters
Step2: Reading characters from input, and counting them. To add an item to the counter, simply increment its value.
Step3: G... | <ASSISTANT_TASK:>
Python Code:
with open('../inputs/day06.txt', 'r') as f:
data = [line.strip() for line in f.readlines()]
from collections import Counter
counters = [Counter() for i in range(0, len(data[0]))]
for line in data:
for index, char in enumerate(line):
counters[index][char] += 1
answer = '... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Generate Data
Step2: Anomaly Models
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Python Code:
import os
import numpy as np
from pathlib import Path
cwd = os.getcwd()
os.chdir(Path(cwd).parents[1])
from lsanomaly import LSAnomaly
import lsanomaly.notebooks.static_mix as demo
n_samples = 20
offset = 2.5
X, xx, yy = demo.data_prep(n_samples=n_samples, offset=offset)
sigma_candidate... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here we can see one of the images.
Step2: Train a network
Step3: Saving and loading networks
Step4: The simplest thing to do is simply save t... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import helper
import fc_model
# Define a transform to no... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Bayesian Network in a Jupyter Notebook (BJN)
Step11: Random structure and parameter generators
Step13: Graph Utilities and Visualizations
Step... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy as sc
from scipy.special import gammaln
from scipy.special import digamma
%matplotlib inline
from itertools import combinations
import pygraphviz as pgv
from IPython.display import Image
from IPython.display import display
def normalize(A, axis=None):
N... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Install the Google cloud-storage library as well.
Step2: Restart the Kernel
Step3: Before you begin
Step4: Region
Step5: Timestamp
Step6: A... | <ASSISTANT_TASK:>
Python Code:
! pip3 install google-cloud-automl
! pip3 install google-cloud-storage
import os
if not os.getenv("AUTORUN"):
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
PROJECT_ID = "[your-project-id]" ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 单词嵌入向量
Step2: 使用嵌入向量层
Step3: 创建嵌入向量层时，嵌入向量的权重会随机初始化（就像其他任何层一样）。在训练过程中，通过反向传播来逐渐调整这些权重。训练后，学习到的单词嵌入向量将粗略地编码单词之间的相似性（因为它们是针对训练模型的特定问题而学习的）。
Step... | <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... |
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Description:
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Python Code::
import pandas as pd
pd.get_dummies(df1.town)
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Description:
Step5: More than one State object
Step7: And here's run_simulation, which is a solution to the exercise at the end of the previous notebook.
Step8: N... | <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 library
from modsim import *
# set the ra... |
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Description:
Step1: In this part of the lecture we explain Stochastic Gradient Descent (SGD) which is an optimization method commonly used in neural networks. We wi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from fastai.learner import *
# Here we generate some fake data
def lin(a,b,x): return a*x+b
def gen_fake_data(n, a, b):
x = s = np.random.uniform(0,1,n)
y = lin(a,b,x) + 0.1 * np.random.normal(0,3,n)
return x, y
x, y = gen_fake_data(50, 3., 8.)
plt.scatter... |
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Description:
Step1: Theory and Algorithm
Step2: The results for a single realization of the simulation are plotted below. As expected, the estimated bias value ten... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
fsize = 15
time_of_sim = 30 # s
freq_acc = 50 # frequency of accelerometer measurements
freq_meas = 2
dt = 1./freq_acc
dt_meas = 1./freq_meas
time_steps_acc = freq_acc*time_of_sim+1
time_steps_meas = freq_meas*time_of... |
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Description:
Step1: Load and process review dataset
Step2: Just like we did previously, we will work with a hand-curated list of important words extracted from the... | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
import json
with open('important_words.json', 'r') as f:
important_words = json.load(f)
important_words = [str(s) for s in important_words]
# Remote punctuation
def remove_punctuati... |
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Description:
Step1: Update the following notebook settings
Step2: Ensure the repo is up to date
Step3: Cherry picks for minor release
Step4: Run tests localy
Ste... | <ASSISTANT_TASK:>
Python Code:
%cd ..
NEW_VERSION = '3.0.0'
LAST_VERSION = '2.5.1'
DEVELOP_VERSION = '2.6.0-develop'
NEXT_FUTURE_VERSION = '3.0.0'
MAJOR_RELEASE = True
STABLE_BRANCH = '2.5-stable'
GIT_REMOTE_UPSTREAM = 'origin'
WORK_BRANCH = 'master' if MAJOR_RELEASE else STABLE_BRANCH
CHERRY_PICKS = []
! git checkou... |
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Description:
Step1: Listwise ranking
Step2: We can then import all the necessary packages
Step3: We will continue to use the MovieLens 100K dataset. As before, we... | <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... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: To initialize the extractor, you'll only need the starting and ending epoch of the time period you wish to visualize and the number of sample po... | <ASSISTANT_TASK:>
Python Code:
from poliastro.czml.extract_czml import CZMLExtractor
from poliastro.examples import molniya
start_epoch = molniya.epoch
end_epoch = molniya.epoch + molniya.period
N = 80
extractor = CZMLExtractor(
start_epoch,
end_epoch,
N
)
extractor.add_orbit(molniya,
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Description:
Step1: For this section we will use the Boston Housing Data.
Step2: <img src='https
Step3: Model (Introducing Tensorboard)
Step4: Learning in a TF S... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_boston
boston_dataset = load_boston()
print(boston_dataset.DESCR)
import pandas as pd
from sklearn.datasets import load_boston
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
boston_dataset = load_b... |
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Description:
Step1: Load config from default location.
Step2: Create API endpoint instance as well as API resource instances (body and specification).
Step3: Fill... | <ASSISTANT_TASK:>
Python Code:
from kubernetes import client, config
config.load_kube_config()
api_instance = client.AppsV1Api()
dep = client.V1Deployment()
spec = client.V1DeploymentSpec()
name = "my-busybox"
dep.metadata = client.V1ObjectMeta(name=name)
spec.template = client.V1PodTemplateSpec()
spec.template.meta... |
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Description:
Step1: Examples
Step2: Example 1
Step3: Exemplo 3
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Python Code:
import numpy as np
def phasecorr(f,h):
F = np.fft.fftn(f)
H = np.fft.fftn(h)
T = F * np.conjugate(H)
R = T/np.abs(T)
g = np.fft.ifftn(R)
return g.real
testing = (__name__ == "__main__")
if testing:
import numpy as np
import sys,os
ia898path = os.path.a... |
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Description:
Step1: Read data created in the previous chapter.
Step2: <h2> Train and eval input functions to read from Pandas Dataframe </h2>
Step3: Our input fun... | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
# In CSV, label is the first col... |
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Description:
Step1: 1. Importing groundwater time series
Step2: 2. Creating a Pastas TimeSeries object
Step3: 3. Configuring a TimeSeries object
Step4: Predefine... | <ASSISTANT_TASK:>
Python Code:
# Import some packages
import pastas as ps
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
gwdata = pd.read_csv('../data/head_nb1.csv', parse_dates=['date'],
index_col='date', squeeze=True)
gwdata.plot(figsize=(15,4))
oseries = ps.TimeSeries(g... |
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Description:
Step1: 2. Read in the hanford.csv file
Step2: <img src="images/hanford_variables.png">
Step3: 4. Calculate the coefficient of correlation (r) and gen... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
df = pd.read_csv('hanford.csv')
df.describe()
df.corr()
lm = smf.ols... |
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Description:
Step1: Exam Instructions
Step2: MRjob class for calculating pairwise similarity using K-L Divergence as the similarity measure
Step4: Questions
Step5... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from __future__ import division
%reload_ext autoreload
%autoreload 2
%%writefile kltext.txt
1.Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms (data in various forms, dat... |
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Description:
Step1: 2D trajectory interpolation
Step2: Use these arrays to create interpolated functions $x(t)$ and $y(t)$. Then use those functions to create the ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
# YOUR CODE HERE
data = np.load("trajectory.npz")
t = data["t"]
x = data["x"]
y = data["y"]
assert isinstance(x, np.ndarray) and len(x)==40
assert isinstance... |
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Description:
Step1: The source space
Step2: Fixed dipole orientations
Step3: Restricting the dipole orientations in this manner leads to the following
Step4: The... | <ASSISTANT_TASK:>
Python Code:
import mne
import numpy as np
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
data_path = sample.data_path()
evokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')
left_auditory = evokeds[0].apply_baseline()
fwd = mne.... |
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Description:
Step1: Here we use
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Python Code:
# Author: Jaakko Leppakangas <jaeilepp@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mayavi import mlab
import mne
from mne.datasets.sample import data_path
print(__doc__)
data_path = data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw =... |
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Description:
Step1: Source of Data
Step2: Illustrate Markdown Parsing Using mdparse
Step5: Download And Pre-Process Data
Step6: Note
Step7: Cached pre-processed... | <ASSISTANT_TASK:>
Python Code:
from mdparse.parser import transform_pre_rules, compose
import pandas as pd
from tqdm import tqdm_notebook
from fastai.text.transform import defaults
df = pd.read_csv(f'https://storage.googleapis.com/issue_label_bot/language_model_data/000000000000.csv.gz').sample(5)
df.head(1)
pd.set_o... |
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Description:
Step1: Check that the bornagain python module is correctly installed
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Python Code:
print('hello, world!')
from __future__ import print_function #needed for python2/python3 compatibility
try:
import bornagain as ba
print("successfully loaded bornagain module")
major, minor = ba.major_version_number, ba.minor_version_number
print("BornAgain version number... |
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Description:
Step1: Now we need a function to actually time the reference implementation. We can do external timing use the time module, and the Java program also r... | <ASSISTANT_TASK:>
Python Code:
import sklearn.datasets
import numpy as np
import pandas as pd
import subprocess
import time
def get_reference_timings(data, filename='tmp_data.csv',
jarfile='/Users/leland/Source/HDBSCAN_Star/HDBSCAN_Star.jar',
min_points=5, min_clust... |
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Description:
Step1: We've seen in a previous tutorial <tut-raw-class> how to plot data
Step2: It may not be obvious when viewing this tutorial online, but by... | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=60).load_data()
ra... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Try TSNE and time it
Step2: Try PCA instead
Step3: Append all view_items for PCA processing
Step4: Append all buy_items for PCA processing
St... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import os
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
os.chdir('/Users/Walkon302/Desktop/deep-learning-models-master/view2buy')
# Read the preprocessed file, containing the user profile and item features from view2buy fold... |
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Description:
Step1: Tree ensembles (RandomForestClassifier)
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Python Code:
# import
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.ensemble impo... |
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Description:
Step1: The first thing we do is load in the BLASTP output we generated, so that we can plot some of the key features. We do that using the ex02.read_da... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
# Import helper module
from helpers import rbbh
# Load one-way BLAST results into a data frame called data_fwd
data_fwd = rbbh.read_data("data/pseudomonas_blastp/B728a_vs_NCIMB_11764.tab")
# Show first few lines of the loaded data
data_fwd.head()
# Show descriptive statist... |
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Description:
Step1: Env setup
Step2: Object detection imports
Step3: Model preparation
Step4: Download Model
Step5: Load a (frozen) Tensorflow model into memory... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This is needed since the notebook is sto... |
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Description:
Step1: Connect to Graphistry + Test
Step2: Connect to TigerGraph and Test
Step3: Query Tigergraph
Step4: Visualize result of TigerGraph query
Step5:... | <ASSISTANT_TASK:>
Python Code:
TIGER_CONFIG = {
'fqdn': 'http://MY_TIGER_SERVER:9000'
}
#!pip install graphistry
import pandas as pd
import requests
### COMMON ISSUES: wrong server, wrong key, wrong protocol, network notebook->graphistry firewall permissions
import graphistry
# To specify Graphistry account & serv... |
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Description:
Step1: Create some plot data
Step2: Define range of data to make sparklines
Step3: Output to new DataFrame of Sparklines
Step4: Insert Sparklines in... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
import sparklines
density_func = 78
mean, var, skew, kurt = stats.chi.stats(density_func, moments='mvsk')
x_chi = np.linspace(stats.chi.ppf(0.01, density_func),
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We want to stop RP from reporting all sorts of stuff for this example so we set a specific environment variable to tell RP to do so. If you want... | <ASSISTANT_TASK:>
Python Code:
import sys, os, time
# verbose = os.environ.get('RADICAL_PILOT_VERBOSE', 'REPORT')
os.environ['RADICAL_PILOT_VERBOSE'] = 'ERROR'
from adaptivemd import Project
from adaptivemd import OpenMMEngine
from adaptivemd import PyEMMAAnalysis
from adaptivemd import File, Directory, WorkerSchedul... |
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Description:
Step1: Computing the eigenvalues and the eigenvectors
Step2: The @ operator stands, in this context, for matrix multiplication.
Step3: Modal Response... | <ASSISTANT_TASK:>
Python Code:
M = np.array(((2.0, 0.0), ( 0.0, 1.0)))
K = np.array(((3.0,-2.0), (-2.0, 2.0)))
p = np.array(( 0.0, 1.0))
w = 2.0
evals, Psi = eigh(K, M)
Mstar = Psi.T@M@Psi
Kstar = Psi.T@K@Psi
pstar = Psi.T@p
print(evals,end='\n\n')
print(Psi,end='\n\n')
print(Mstar,end='\n\n')
print(Kstar,end='\n\n')... |
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Description:
Step1: <div id='intro' />
Step2: en donde cada punto interior (azul), representa un punto donde queremos conocer el valor de la función $u(x,y)$. Cons... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from mpl_toolkits.mplot3d import axes3d
from matplotlib import pyplot as plt
from ipywidgets import interact
from ipywidgets import IntSlider
import sympy as sym
import matplotlib as mpl
mpl.rcParams['font.size'] = 14
mpl.rcParams['axes.labelsize'] = 20
mpl.rcParams['xt... |
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Description:
Step1: A Motivating Example Using sklearn
Step2: Remember that the form of data we will use always is
Step3: Training and Test Datasets
Step4: Tunin... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.se... |
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Description:
Step1: Setup Game
Step2: Catcher Model
Step3: Test the agent
Step5: Show Playing
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Python Code:
import os, sys
sys.path.append(os.path.join('..'))
import keras.backend as K
K.set_image_dim_ordering('th') # needs to be set since it defaults to tensorflow now
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core i... |
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Description:
Step1: Random sampling
Step2: Sobol
Step3: Classic Latin hypercube sampling
Step4: Centered Latin hypercube sampling
Step5: Maximin optimized hyper... | <ASSISTANT_TASK:>
Python Code:
print(__doc__)
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
from skopt.space import Space
from skopt.sampler import Sobol
from skopt.sampler import Lhs
from skopt.sampler import Halton
from skopt.sampler import Hammersly
from skopt.sampler import Grid
from scipy.... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: SeqRecord objects contain metadata
Step2: The attributes can be modified as needed.
Step3: Unstructured annotations are organised into a dicti... | <ASSISTANT_TASK:>
Python Code:
import Bio.SeqRecord as BSR
import Bio.Seq as BS
import Bio.Alphabet as BA
# sequence
seq = BS.Seq('MDGEDVQALVIDNGSGMCKA', BA.generic_protein)
# sequence record
record = BSR.SeqRecord(seq)
print(record)
# get sequence from record
print(record.seq)
# add identifier
record.id = "AC500001"... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Thinking about how machine learning is normally performed, the idea of a train/test split makes sense. Real world systems train on the data they... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
y
import numpy as np
rng = np.random.RandomState(0)
permutation = rng.permutation(len(X))
X, y = X[permutation],... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Base manifold (three dimensional)
Step2: Two dimensioanal submanifold - Unit sphere
Step3: Christoffel symbols of the first kind
Step4: One d... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import sys
from galgebra.printer import Format, xpdf
Format()
from sympy import symbols, sin, pi, latex, Array, permutedims
from galgebra.ga import Ga
from IPython.display import Math
from sympy import cos, sin, symbols
g3coords = (x,y,z) = symbols(... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data import
Step2: Fluctuation assay
Step3: Figure 5 - Loss of heterozygosity
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Python Code:
# Load external dependencies
from setup import *
# Load internal dependencies
import config,plot,utils
%load_ext autoreload
%autoreload 2
%matplotlib inline
# Load data
loh_length_df = pd.read_csv(dir_data+'seq/loh/homozygosity_length.csv')
loh_length_df = loh_length_df.set_index("50kb_b... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 0
Step2: Part 1
Step3: Try to use the following example of the scikit-learn help, to plot the classification regions for different pairs ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
# Initialize the random generator seed to compare results
np.random.seed(0)
iris = datasets.load_iris()
X = iris.data ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load a raw miniSASP file
Step2: Get sun_intensities
Step3: Plot as a function of Altitude
| <ASSISTANT_TASK:>
Python Code:
from atmPy.instruments.miniSASP import miniSASP
from atmPy.tools import plt_tools
from atmPy.instruments.piccolo import piccolo
%matplotlib inline
plt_tools.setRcParams(plt)
ms_raw = miniSASP.read_csv('./data/miniSASP_raw.txt')
ms_raw.data.PhotoAsh.plot()
sun_intensities = ms_raw.find_p... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We first define the Tensorflow graph, and create some data.
Step2: Export TensorFlow SavedModel
Step3: Deploy Cluster Serving
Step4: We confi... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
tf.__version__
g = tf.Graph()
with g.as_default():
# Graph Inputs
features = tf.placeholder(dtype=tf.float32,
shape=[None, 2], name='features')
targets = tf.placeholder(dtype=tf.float32,
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Es ist also sehr einfach in Python diese Funktion zu programmieren.Das hat aber nichts mit Machine Learning zu tun, sondern das ist das klassisc... | <ASSISTANT_TASK:>
Python Code:
# Definition der Funktion umrechnung
def umrechnung(C):
F = #Ihr Code hier#
return #Ihr Code hier#
#
# Rufen Sie die definierte Funktion mit unterschiedlichen Werten (8,12.5,23,44.6)
# einmal auf. Sie sollten die Ergebnisse (46.4, 54.5, 73.4, 112.28) erhalten.
# Aufruf - Ersetzen ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Get the mutations that are segregating in each population
Step2: Look at the raw data in the first element of each list
Step3: Let's make that... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import fwdpy as fp
import pandas as pd
from background_selection_setup import *
mutations = [fp.view_mutations(i) for i in pops]
for i in mutations:
print(i[0])
mutations2 = [pd.DataFrame(i) for i in mutations]
for i in mutations2:
print(i.... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Keras provides a loading function similar to train_test_split from scikit-learn's
Step2: The neural nets in Keras act on the feature matrix sli... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
img_rows, img_cols = 28, 28
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0]... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: quantulum3
Step2: Finding quantity statements in large texts
Step3: Annotating a dataset
Step4: We could then do something to split multiple ... | <ASSISTANT_TASK:>
Python Code:
sentences = [
'4 years and 6 months’ imprisonment with a licence extension of 2 years and 6 months',
'No quantities here',
'I measured it as 2 meters and 30 centimeters.',
"four years and six months' imprisonment with a licence extension of 2 years and 6 months",
'it c... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a name="multipanel"></a>
Step2: So even with the sharing of axis information, there's still a lot of repeated code. This current version with ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, DayLocator
from siphon.simplewebservice.ndbc import NDBC
%matplotlib inline
# Read in some data
df = NDBC.realtime_observations('42039')
# Trim to the last 7 days
df = df[df['time'] > (pd.Times... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we read in a plasmid from Havens et al. 2012 and isolate the EYFP sequence.
Step2: Designing primers is straightforward - you just call d... | <ASSISTANT_TASK:>
Python Code:
import coral as cor
plasmid = cor.seqio.read_dna("../files_for_tutorial/maps/pGP4G-EYFP.ape")
eyfp_f = [f for f in plasmid.features if f.name == 'EYFP'][0]
eyfp = plasmid.extract(eyfp_f)
print len(eyfp)
eyfp
# Forward and reverse, one at a time using design_primer()
forward = cor.design... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1
Step2: Step 2
Step3: Step 3
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Python Code:
%reset
################################################################################
### Import packages
################################################################################
import os
import pandas as pd
from scipy.spatial import distance as dist
from scipy.cluster import h... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In this notebook we demo two equivalent ways of performing a two-sample Bayesian t-test to compare the mean value of two Gaussian populations us... | <ASSISTANT_TASK:>
Python Code:
import arviz as az
import bambi as bmb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
az.style.use("arviz-darkgrid")
np.random.seed(1234)
a = np.random.normal(6, 2.5, 160)
b = np.random.normal(8, 2, 120)
df = pd.DataFrame({"Group": ["a"] * 160 + ["b"] * 120, "Val"... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Adjacency Matrices
Step2: A little visualization, just to double check.
Step3: Steady-State Probability of Random Walker
Step4: The resulting... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import networkx as nx
import seaborn as sns
sns.set_style('ticks')
sns.set_context('poster')
A_directed = np.array( [[0, 1, 0, 0, 1],
[0, 0, 1, 0, 0],
[1, 0, 0, 1, 1],
[0, 1, 1, ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We'll start by generating some fake data (from a sinusoidal model) with error bars
Step2: Now, we'll choose a kernel (covariance) function to m... | <ASSISTANT_TASK:>
Python Code:
import george
george.__version__
import numpy as np
import matplotlib.pyplot as pl
np.random.seed(1234)
x = 10 * np.sort(np.random.rand(15))
yerr = 0.2 * np.ones_like(x)
y = np.sin(x) + yerr * np.random.randn(len(x))
pl.errorbar(x, y, yerr=yerr, fmt=".k", capsize=0)
pl.xlim(0, 10)
pl.yli... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Reading cleaned data
Step2: Preparing data
Step3: Regression approach
Step4: Classification approach
Step5: Binary solution
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Python Code:
import turicreate as tc
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
sf = tc.SFrame.read_csv('electrodes_clean.csv')
sf.explore() # in GUI
# optional save to SFrame
# sf = tc.SFrame('electrodes_clean.sframe')
sf_reg = sf.remove_column('TPLE category')
sf_class... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Compute the coronal averages for temperature and density over the whole parameter space
Step2: Define a function to do the spatial averaging.
S... | <ASSISTANT_TASK:>
Python Code:
import os
import pickle
import numpy as np
hfRes_format = '../results/static/HYDRAD_raw/%s/HYDRAD_%d/Results/profile%d.phy'
hydrad_labs = [20,40,200,500]
hydrad_res = {'electron':{},'ion':{},'single':{},
'loop_midpoint':4.5e+9, 'time':np.arange(0,5001)}
int_perc = 0.9
def ... |
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Description:
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Python Code:
import math
def Area_Parallelogram1(a , b , theta ) :
area =(abs(math . tan(math . radians(theta ) ) ) / 2 ) * abs(a ** 2 - b ** 2 )
return area
def Area_Parallelogram2(a , b , gamma ) :
area =(abs(math . sin(math . radians(gamma ) ) ) ) * abs(a * b )
return area
def A... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Transform data with PCA computed on the average ie evoked response
Step2: Transform data with ICA computed on the raw epochs (no averaging)
| <ASSISTANT_TASK:>
Python Code:
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.dec... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load train data
Step2: Define columns
Step3: Parse the weird date format of the date column
Step4: Drop duplicate rows
Step5: Drop duplicate... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%load_ext autoreload
%autoreload 2
from __future__ import division
from collections import defaultdict, namedtuple
import cPickle as pickle
from datetime import datetime, timedelta
import dateutil
from functools import partial
import inspect
import json
import os
import re
i... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <p style="font-family
Step2: Let's see what is in this image.
Step3: The shape of the ndarray show that it is a three layered matrix. The firs... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt
from skimage import data
photo_data = misc.imread('./wifire/sd-3layers.jpg')
type(photo_data)
plt.figure(figsize=(15,15))
plt.imshow(photo_data)
photo_data.shape
#print(photo_data)
photo_data.s... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 이 확률 분포는 다음 식에서 보다시피 주변 확률 분포의 곱으로 표현된다.
Step2: 여러 가지 Y값을 바꾸어도 조건부 확률은 변하지 않는 것을 확인할 수 있다.
Step3: 이번에는 다음과 같은 결합 확률 분포를 보자. 이 경우에는 독립 조건이 성립하지... | <ASSISTANT_TASK:>
Python Code:
np.set_printoptions(precision=4)
pmf1 = np.array([[0, 1, 2, 3, 2, 1],
[0, 2, 4, 6, 4, 2],
[0, 4, 8,12, 8, 4],
[0, 2, 4, 6, 4, 2],
[0, 1, 2, 3, 2, 1]])
pmf1 = pmf1/pmf1.sum()
pmf1
sns.heatmap(pmf1)
plt.xlabel("x")
plt.ylab... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a id="ref0"></a>
Step2: Create a tensor ranging from -10 to 10
Step3: When you use sequential, you can create a sigmoid object
Step4: Apply ... | <ASSISTANT_TASK:>
Python Code:
import torch.nn as nn
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
torch.manual_seed(2)
z=torch.arange(-10,10,0.1).view(-1, 1)
sig=nn.Sigmoid()
yhat=sig(z)
sig(torch.tensor(-1.0))
plt.plot(z.numpy(),yhat.numpy())
plt.xlabel('z')
plt.ylabel('yhat')
yh... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Prep 2
Step2: Prep 3
Step3: Prep 4
Step4: UMAP
Step5: Visualize 1
Step6: Fascinatingly, when Graphistry's force-directed graph layout algor... | <ASSISTANT_TASK:>
Python Code:
# Already installed in Graphistry & RAPIDS distros
# ! pip install --user umap-learn
# ! pip install --user graphistry
import graphistry, pandas as pd, umap
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Enumerate the parameter combinations
Step2: Specify the model changes
Step3: Specify the result 'y' that we want to retrieve
Step4: Trigger t... | <ASSISTANT_TASK:>
Python Code:
## Veneer started elsewhere (probably from a command line using veneer.manager.start)
ports = list(range(15004,15008))
ports
bv = BulkVeneer(ports)
v = bv.veneers[1]
network = v.network()
network.as_dataframe().plot()
network.outlet_nodes()
outlet_node = network.outlet_nodes()[0]['propert... |
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