repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/solutions/2_first_model.ipynb | apache-2.0 | PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]
BUCKET = PROJECT
REGION = "us-central1"
%env PROJECT=$PROJECT
%env BUCKET=$BUCKET
%env REGION=$REGION
"""
Explanation: First BigQuery ML models for Taxifare Prediction
Learning Objectives
* Choose the correct BigQuery ML model type and specify options
... |
mne-tools/mne-tools.github.io | stable/_downloads/9552276573be20bde95d1b4bc52b4768/20_event_arrays.ipynb | bsd-3-clause | import os
import numpy as np
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, verbose=False)
raw.crop(tmax=60).load_data()... |
google-research/computation-thru-dynamics | notebooks/LFADS Tutorial.ipynb | apache-2.0 | # Numpy, JAX, Matplotlib and h5py should all be correctly installed and on the python path.
from __future__ import print_function, division, absolute_import
import datetime
import h5py
import jax.numpy as np
from jax import random
from jax.experimental import optimizers
from jax.config import config
#config.update("ja... |
r-karasik/lanl-auth-cybersecurity | machine learning.ipynb | mit | df=pd.read_csv('md/msample1.csv', header=None)
len(df)
df[8].value_counts()
"""
Explanation: Load file sampled from data in auth.txt.gz so that number of fails is similar to the number of successes.
End of explanation
"""
Y=(df[8]=='Success')
"""
Explanation: Creating clsssification label
End of explanation
"""
... |
KaiSzuttor/espresso | doc/tutorials/11-ferrofluid/11-ferrofluid_part3.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
import numpy as np
"""
Explanation: Ferrofluid - Part III
Table of Contents
Susceptibility with fluctuation formulas
Derivation of the fluctuation formula
Simulation
Magnetization curve of a 3D... |
lguduy/Data-Structure-and-Algorithms-in-Python | Note/第二章. 抽象数据类型和 Python 类.ipynb | mit | class Student(object):
skills = []
def __init__(self, name):
self.name = name
stu = Student('ly')
print Student.skills # 访问类数据属性
Student.skills.append('Python')
print Student.skills
print stu.skills # 通过实例也能访问类数据属性
print dir(Student)
Student.age = 25 # 通过类名动态添加类数据属性
print di... |
mne-tools/mne-tools.github.io | 0.17/_downloads/1048fcdfaf4847afa747b1cc9df74e2d/plot_movement_compensation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement')
head_pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_qu... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/jax/solutions/jax_fundamentals.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
import numpy as np
from matplotlib import pyplot as plt
# Check connected accelerators. Depending on what runtime you're connected to,
# this will show a single CPU/GPU, or 8 TPU cores (jf_2x2 aka JellyDonut).
# You can start a TPU runtime via : "Connect to a runtime" -> "Start" ->
... |
tpin3694/tpin3694.github.io | python/testable_documentation.ipynb | mit | import doctest
"""
Explanation: Title: Testable Documentation
Slug: testable_documentation
Summary: Testable Documentation in Python.
Date: 2016-01-23 12:00
Category: Python
Tags: Testing
Authors: Chris Albon
Interesting in learning more? Here are some good books on unit testing in Python: Python Testing: Beginner'... |
probml/pyprobml | notebooks/book1/04/laplace_approx_beta_binom_jax.ipynb | mit | try:
from probml_utils import latexify, savefig
except:
%pip install git+https://github.com/probml/probml-utils.git
from probml_utils import latexify, savefig
import jax
import jax.numpy as jnp
from jax import lax
try:
from tensorflow_probability.substrates import jax as tfp
except ModuleNotFoundError... |
WoodResourcesGroup/EPIC_AllPowerLabs | IOUdata/ReadFromDB-Copy1.ipynb | mit | import pandas as pd
from sqlalchemy import create_engine
"""
Explanation: This notebook is intended to show how to use pandas, and sql alchemy to upload data into DB2-switch.
Install using pip or any other package manager pandas, sqlalchemy and pg8000. The later one is the driver to connect to the db.
End of explanati... |
csc-training/python-introduction | notebooks/exercises/8 - Object oriented programming.ipynb | mit | class Car:
def __init__(self, make, model, year, mpg=25, tank_capacity=30.0, miles=0):
self.make = make
self.model = model
self.year = year
self.mpg = mpg
self.gallons_in_tank = tank_capacity # cars start with a full tank
self.tank_capacity = tank_capacity
... |
PMEAL/OpenPNM-Examples | Simulations/relative_diffusivity.ipynb | mit | import openpnm
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Relative Diffusivity
Generating the Network, adding Geometry and creating Phases
This example shows you how to calculate a transport property relative to the saturation of the domain by a particular phase. In this case the property is t... |
AllenDowney/ModSimPy | soln/chap07soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
from pandas import read_html
"""
Expl... |
mne-tools/mne-tools.github.io | 0.23/_downloads/b7659d33d6ffe8531d004e9d6051f16f/forward_sensitivity_maps.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.source_space import compute_distance_to_sensors
from mne.source_estimate import SourceEstimate
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter S1.A - Review & Class work/Interview Questions - Core Python.ipynb | gpl-3.0 | lst = [1, 2, 3, 44, 4, 2, 44, 55, 2, 34]
print(list(set(lst)))
"""
Explanation: Interview Questions - Core Python
I have only recently started collecting the Interview Questions for Core Python. I will keep on adding more interview questions.
I am also planning to pen a seperate ebook with details questions and solut... |
namco1992/algorithms_in_python | algorithms/tree.ipynb | mit | class BinaryTree():
def __init__(self, root_obj):
self.key = root_obj
self.left_child = None
self.right_child = None
def insert_left(self, new_node):
# if the tree do not have a left child
# then create a node: one tree without children
if self.left_child is ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/walkthrough/labs/tfx_walkthrough.ipynb | apache-2.0 | import os
import tempfile
import time
from pprint import pprint
import absl
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
import tfx
from tensorflow_metadata.proto.v0 import (
anomalies_pb2,
schema_pb2,
statisti... |
Kismuz/btgym | examples/model_based_stat_arb/analytic_data_model_an_introduction.ipynb | lgpl-3.0 | # Import and visualize data:
filename1 = './data/ETH_USD_hour_data.csv'
filename2 = './data/BTC_USD_hour_data.csv'
asset1 = pd.read_csv(filename1)
asset2 = pd.read_csv(filename2)
full_slice = slice(None, None)
fig = plt.figure(num=0, figsize=(16, 8))
plt.title('Entire Dataset')
ax1 = fig.add_subplot(111)
ax1.plot... |
parrt/msan501 | notes/linked-list.ipynb | mit | class Node:
def __str__(self):
return "(%s,%s)" % (self.value, str(self.next))
def __repr__(self):
return str(self)
def __init__(self, value, next=None):
self.value = value
self.next = next
"""
Explanation: Linked lists
We've studied arrays/lists that are built into Python b... |
gregcaporaso/short-read-tax-assignment | ipynb/simulated-community/taxonomy-assignment.ipynb | bsd-3-clause | from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.simulated_communities import copy_expected_composition
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_... |
google-research/policy-learning-landscape | notebooks/ExampleLandscapes.ipynb | apache-2.0 | # 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, software
# distributed under the L... |
aliakbars/uai-ai | scripts/tugas1b.ipynb | mit | from __future__ import print_function, division # Gunakan print(...) dan bukan print ...
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import random
import requests
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.metrics import accuracy_... |
google/xarray-beam | docs/read-write.ipynb | apache-2.0 | # hidden imports & helper functions
import textwrap
import apache_beam as beam
import xarray_beam as xbeam
import xarray
def summarize_dataset(dataset):
return f'<xarray.Dataset data_vars={list(dataset.data_vars)} dims={dict(dataset.sizes)}>'
def print_summary(key, chunk):
print(f'{key}\n with {summarize_da... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/rome/update_from_v335_to_v337.ipynb | gpl-3.0 | import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '335'
NEW_VERSION = '337'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(... |
thewtex/SimpleITK-Notebooks | 33_Segmentation_Thresholding_Edge_Detection.ipynb | apache-2.0 | import SimpleITK as sitk
from downloaddata import fetch_data as fdata
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from scipy import linalg
from ipywidgets import interact, fixed
"""
Explanation: <h1 align="center">Segmentation: Thresholding and Edge Detection</h1>
In this notebook our go... |
ageron/tensorflow | tensorflow/lite/tutorials/post_training_quant.ipynb | apache-2.0 | ! pip uninstall -y tensorflow
! pip install -U tf-nightly
import tensorflow as tf
tf.enable_eager_execution()
! git clone --depth 1 https://github.com/tensorflow/models
import sys
import os
if sys.version_info.major >= 3:
import pathlib
else:
import pathlib2 as pathlib
# Add `models` to the python path.
mo... |
kit-cel/wt | wt/vorlesung/ch1_3/dice_even_odd.ipynb | gpl-2.0 | # importing
import numpy as np
"""
Explanation: Content and Objective
Confirm results derived in the lecture when analyzing probability of sum of two dice being greater than 9, conditioned on the result of first dice being even and odd
Dice are sampled and occurences of according events are being counted
Import
End ... |
bjodah/aqchem | examples/ammonical_cupric_solution.ipynb | bsd-2-clause | from collections import defaultdict
from chempy import atomic_number
from chempy.chemistry import Species, Equilibrium
from chempy.equilibria import EqSystem, NumSysLin, NumSysLog, NumSysSquare
from IPython.display import Latex, display
import matplotlib.pyplot as plt
%matplotlib inline
def show(s): # convenience func... |
banneker-aztlan/python-week-2 | Part 2/galaxy_spec.ipynb | mit | # only necessary if you're running Python 2.7 or lower
from __future__ import print_function
from __builtin__ import range
import numpy as np
# import plotting utility and define our naming alias
from matplotlib import pyplot as plt
# plot figures within the notebook rather than externally
%matplotlib inline
"""
Ex... |
mne-tools/mne-tools.github.io | 0.24/_downloads/6965b7b1a563cc32b2b5388d95203d43/60_cluster_rmANOVA_spatiotemporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Denis Engemannn <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
from numpy.random import randn
import matplotlib.pyplot as plt
import mne
from mne.stats ... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/CommandPattern.ipynb | mit | class backSys():
def cook(self,dish):
pass
class mainFoodSys(backSys):
def cook(self,dish):
print ("MAINFOOD:Cook %s"%dish)
class coolDishSys(backSys):
def cook(self,dish):
print ("COOLDISH:Cook %s"%dish)
class hotDishSys(backSys):
def cook(self,dish):
print ("HOTDISH:Coo... |
amirziai/learning | machine-learning/Receiver Operating Characteristics (ROC).ipynb | mit | %matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
# some classification metrics
# more here:
# http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics
from sklearn.metrics import (auc, roc_curve, roc_auc_score,
acc... |
Arcana/emoticharms.trade | viability.ipynb | gpl-2.0 | def get_spending_of_attendee():
if random.random() < 0.03: # Let's say 3% doesn't even care about the secret shop
return 0
return int((random.paretovariate(2) - 0.5) * 100)
print([get_spending_of_attendee() for _ in range(100)])
"""
Explanation: First I'm going to define a function which gets us an ... |
obscode/bootcamp | MoreNotebooks/ModelFitting/ErrorsInXandY.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
N = 50
sig_x = 0.5
sig_y = 0.5
a_true = 5.0
b_true = 2.0
x_true = np.random.uniform(0,10,size=N)
y_true = a_true + x_true*b_true
x_obs = x_true + np.random.normal(0, sig_x, size=N)
y_obs = y_true + np.random.normal(0, sig_y, size=N)
fig,ax = plt.subplots(1)
ax.error... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_model_performance/labs/b_feature_engineering_wd.ipynb | apache-2.0 | import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: More Feature Engineering - Wide and Deep models
Learning Objectives
* Build a Wide and Deep model using the appropriate Tensorflow feature columns
Introduction
In this notebook we'll use what we learned about feature col... |
metpy/MetPy | v0.11/_downloads/d4dcac00a3f9fe87c2dfb49b8fcc70fb/sigma_to_pressure_interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date
from metpy.cbook import get_test_data
from metpy.interpolate import log_interpolate_1d
from metpy.plots import add_metpy_logo, add_timestamp
from metpy.units import units
"""
Explanation... |
HumanCompatibleAI/imitation | examples/6_train_mce.ipynb | mit | from imitation.algorithms.mce_irl import (
MCEIRL,
mce_occupancy_measures,
mce_partition_fh,
TabularPolicy,
)
import gym
import imitation.envs.examples.model_envs
from imitation.algorithms import base
from imitation.data import rollout
from imitation.envs import resettable_env
from stable_baselines3.co... |
yuanotes/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = '/input/data/small_vocab_en'
target_path = '/input/data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_cenonce_session_3B.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.ml - Arbres de décision / Random Forest
Classification, régression, visualisation avec des méthodes ensemblistes (arbres, forêts, ...).
End of explanation
"""
import os
if not os.p... |
mne-tools/mne-tools.github.io | 0.23/_downloads/a3fade035778bc07f682b0807e91849e/decoding_csp_eeg.ipynb | bsd-3-clause | # Authors: Martin Billinger <martin.billinger@tugraz.at>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSplit, cross_val_score
from mn... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/04-Visualization-Matplotlib-Pandas/04a-Matplotlib/Matplotlib Exercises A - Solved! .ipynb | apache-2.0 | import numpy as np
x = np.arange(0,100)
y = x * 2
z = x ** 2
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>
Matplotlib Exercises
Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These ar... |
eds-uga/csci1360e-su17 | assignments/A3/A3_Q3.ipynb | mit | v1 = safe_access({"one": [1, 2, 3], "two": [4, 5, 6], "three": "something"}, "three")
assert v1 == "something"
v2 = safe_access({"one": [1, 2, 3], "two": [4, 5, 6], "three": "something"}, "two", [10, 11, 12])
assert set(v2) == set((4, 5, 6))
default_val = 3
try:
value = safe_access({"one": 1, "two": 2}, "three", ... |
ernestyalumni/cuBlackDream | examples/LinReg.ipynb | mit | import timeit
start_time = timeit.default_timer()
result1500 = gradDesc(Xex1data1,yex1data1, Theta,b,0.01,1500)
elapsedtime = timeit.default_timer() - start_time
print(elapsedtime ) # in seconds
a1,a1b = feedfwd(Xex1data1, Theta,b)
res,J = costJ(Xex1data1,Theta,b,yex1data1)
d_Theta,d_b,Theta1p1, btp1 = grad_desc_1(X... |
metpy/MetPy | v0.12/_downloads/0c4dbfdebeb6fcd2f5364a69f0c6d4a8/Skew-T_Layout.ipynb | bsd-3-clause | import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
"""
Explanation: Skew-T with Complex Layout
Combine a Skew-T and a hodogra... |
gaufung/PythonStandardLibrary | FileSystem/Codecs.ipynb | mit | import binascii
def to_hex(t, nbytes):
"""Format text t as a sequence of nbyte long values
separated by spaces.
"""
chars_per_item = nbytes * 2
hex_version = binascii.hexlify(t)
return b' '.join(
hex_version[start:start + chars_per_item]
for start in range(0, len(hex_version), ... |
tjwei/HackNTU_Data_2017 | Week03/01-Read Tar and CSV.ipynb | mit | import tarfile
# 檔案名稱格式
filename_format="M06A_{year:04d}{month:02d}{day:02d}.tar.gz".format
xz_filename_format="xz/M06A_{year:04d}{month:02d}{day:02d}.tar.xz".format
csv_format = "M06A/{year:04d}{month:02d}{day:02d}/{hour:02d}/TDCS_M06A_{year:04d}{month:02d}{day:02d}_{hour:02d}0000.csv".format
# 打開剛才下載的檔案試試
data_conf... |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/lidar/create_hillshade_from_terrain_raster_py.ipynb | agpl-3.0 | from osgeo import gdal
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: Create a Hillshade from a Terrain Raster in Python
In this tutorial, we will learn how to create a hillshade from a terrain raster in Python.
First, let's imp... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/pandas/03.06-Concat-And-Append.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The text is released under th... |
woters/ds101 | 4_Titanic.ipynb | mit | # pandas
import pandas as pd
from pandas import DataFrame
import re
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from skl... |
zlpure/CS231n | assignment2/BatchNormalization.ipynb | mit | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
altimesh/hybridizer-basic-samples | Jupyter/Labs/07_ConjugateGradient/HYB_CUDA_Csharp_ConjugateGradient.ipynb | mit | !hybridizer-cuda ./01-Naive/01-naive.cs ./Common_Files/SparseMatrixNaive.cs -o ./01-Naive/naive.exe -run
"""
Explanation: <div align="center"><h1>Resident Array on GPU</h1></div>
Prerequisites
To get the most out of this lab, you should already be able to:
- Write, compile, and run C# programs that both call CPU fun... |
willingc/jupyter-data-seeker | Sphinx GitHub.ipynb | gpl-2.0 | import getpass
from github3 import login
"""
Explanation: Sphinx dev tracker
This notebook uses the github3py project maintained by Ian Cordasco.
This notebook is a starter notebook for finding information about repositories that are managed by the Jupyter team. Repos are from the Jupyter and IPython GitHub organizati... |
kunaltyagi/SDES | notes/python/p_norvig/word/xkcd1313-part2.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from __future__ import division, print_function
from collections import Counter, defaultdict
import re
import itertools
import random
Set = frozenset # Data will be frozensets, so they can't be mutated.
def words(text):
"A... |
fujii-team/GPinv | notebooks/Spectroscopic_Abel_inversion.ipynb | apache-2.0 | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import sys
# In ../testing/ dir, we prepared a small script for generating the above matrix A
sys.path.append('../testing/')
import make_LosMatrix
# Import GPinv
import GPinv
"""
Explanation: An example of the Nonlinear infe... |
dcavar/python-tutorial-for-ipython | notebooks/Neural Network Example with Keras.ipynb | apache-2.0 | from keras.models import Sequential
from keras.layers import Dense
"""
Explanation: Neural Network Example with Keras
(C) 2018-2019 by Damir Cavar
Version: 1.1, January 2019
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to the L665 course on Machin... |
sujitpal/polydlot | src/tensorflow/05a-experiment-from-layers.ipynb | apache-2.0 | from __future__ import division, print_function
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import tensorflow as tf
DATA_DIR = "../../data"
TRAIN_FILE = os.path.join(DATA_DIR, "mnist_train.csv")
TEST_FI... |
INM-6/Python-Module-of-the-Week | session10_PyTorch/introduction_to_pytorch_mnist.ipynb | mit | transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batc... |
jrbourbeau/cr-composition | notebooks/legacy/lightheavy/fraction-distribution.ipynb | mit | %load_ext watermark
%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend
"""
Explanation: <a id='top'> </a>
Author: James Bourbeau
End of explanation
"""
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
%matplotlib inline
from __future__ import division, prin... |
parambharat/ML-Programs | P0:_Titanic_Survival/Titanic_Survival_Exploration.ipynb | mit | import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Titanic data... |
bMzi/ML_in_Finance | 0103_Plotting.ipynb | mit | # Standard imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
"""
Explanation: Plotting with Matplotlib
Introduction
Most certainly you are familiar with the frase "A pictures is worth a thousand words". Good graphics are tremendously helpful in visualizing... |
d-k-b/udacity-deep-learning | intro-to-tensorflow/intro_to_tensorflow_solution.ipynb | mit | # Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
"""
Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
:param image_data: The image data to be normalized
:return: Normalized image data
"""
a = 0.1
b = 0.9
grayscale... |
jacobdein/alpine-soundscapes | archive/Index exploration 2.ipynb | mit | import pandas
from Pymilio import database
import numpy as np
import matplotlib.pylab as plt
%matplotlib inline
"""
Explanation: Index exploration 2
This notebook explores the indicies computed from sound files in a <a href="https://github.com/ljvillanueva/pumilio">pumilio</a> database.
Required packages
<a href="ht... |
pelodelfuego/word2vec-toolbox | notebook/dataExploration/dimensionDistribution.ipynb | gpl-3.0 | domainWordList = [open('../../data/domain/luu_animal.txt').read().splitlines(),
open('../../data/domain/luu_plant.txt').read().splitlines(),
open('../../data/domain/luu_vehicle.txt').read().splitlines()]
def buildCptDf(d, domain, polar=False):
cptList = cpe.buildConceptList(d, d... |
ctralie/TUMTopoTimeSeries2016 | SlidingWindow3-AudioApplications.ipynb | apache-2.0 | ##Do all of the imports and setup inline plotting
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from scipy.interpolate import InterpolatedUnivariateSpline
from ripser import ripser
from persim import plot_diagrams
import scipy.io.wavfile
from IPython.dis... |
jaety/ds-doodles | studies/01_WorldBank/World Bank Exploration.ipynb | mit | import wbdata
%matplotlib inline
wbdata.get_source() # List world bank data sources
# List all available indicators from that source. Very long list. Nicely scrolled in local notebook, but
# overwhelming on github cache
# wbdata.get_indicator(source=2)
# wbdata.get_data("EG.USE.PCAP.KG.OE") # Returns long list o... |
lucasb-eyer/BiternionNet | Inspection - Regression.ipynb | mit | def extract_array(mat, ref, dtype=np.float32):
N = len(ref)
arr = np.empty(N, dtype=dtype) # mat[ref[0,0]].dtype
for i in range(N):
arr[i] = mat[ref[i,0]][0,0]
return arr
def read_tosato(folder):
mat_full = h5py.File(pjoin(folder, 'or_label_full.mat'))
def loadall(traintest):
c... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emissions ... |
robertoalotufo/ia898 | src/circle.ipynb | mit | import numpy as np
def circle(s, r, c):
rows, cols = s[0], s[1]
rr0, cc0 = c[0], c[1]
rr, cc = np.meshgrid(range(rows), range(cols), indexing='ij')
g = (rr - rr0)**2 + (cc - cc0)**2 <= r**2
return g
"""
Explanation: Function circle
Synopse
This function creates a binary circle image.
g = c... |
GoogleCloudPlatform/analytics-componentized-patterns | retail/ltv/bqml/notebooks/bqml_automl_ltv_activate_lookalike.ipynb | apache-2.0 | # Install libraries.
# The magic cells insures that those libraries can be part of a custom container
# if moving the code somewhere else.
%pip install -q googleads
%pip install -q -U kfp matplotlib Faker --user
# Automatically restart kernel after installs
# import IPython
# app = IPython.Application.instance()
# ap... |
Mashimo/datascience | 01-Regression/Regularisation.ipynb | apache-2.0 | import pandas as pd
# load up the Credit dataset
#
data = pd.read_csv("../datasets/credit.csv", index_col=0)
data.shape
data.columns
data.head()
data.describe()
data.info()
"""
Explanation: Regularisation
The basic idea of regularisation is to penalise or shrink the large coefficients of a regression model.
Th... |
xiongzhenggang/xiongzhenggang.github.io | data-science/.ipynb_checkpoints/24-simple_liner-checkpoint.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
#使用seaborn-whitegrid风格
plt.style.use('seaborn-whitegrid')
import numpy as np
"""
Explanation: 简单线图
先设置ipython notebook 作图环境
End of explanation
"""
fig = plt.figure()
ax = plt.axes()
"""
Explanation: 对于所有Matplotlib图，我们首先创建一个图形和一个轴。以最简单的形式，可以如下创建图形和轴：
End of explanat... |
rpaseity/udlnd | dlnd-your-first-neural-network.ipynb | mit | %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 the code... |
ellisztamas/faps | docs/.ipynb_checkpoints/06 Simulating data-checkpoint.ipynb | mit | import numpy as np
import faps as fp
import matplotlib.pylab as plt
import pandas as pd
from time import time, localtime, asctime
np.random.seed(37)
allele_freqs = np.random.uniform(0.2, 0.5, 50)
adults = fp.make_parents(10, allele_freqs, family_name='adult')
"""
Explanation: Simulating data and power analysis
Tom ... |
danielfather7/teach_Python | Class and Inheritance/Python_Class and Inheritance.ipynb | gpl-3.0 | # Define a class named Pokemon
class Pokemon():
def __init__(self, name, attack, defence):
self.name = name
self.attack = attack
self.defence = defence
print('Hello world')
def poko_name(self):
return self.name
def poko_state(self):
return self.attac... |
geography-munich/sciprog | material/sub/koldunov/05 - Graphs and maps - Matplotlib and Basemap.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pylab as plt
import numpy as np
"""
Explanation: Graphs and maps (Matplotlib and Basemap)
Nikolay Koldunov
koldunovn@gmail.com
This is part of Python for Geosciences notes.
=============
Matplotlib is a python 2D plotting library which produces publication quality figures in a vari... |
barjacks/foundations-homework | 06/Dark Sky Forecast_Homework_6_Graded.ipynb | mit | import requests
response = requests.get("https://api.forecast.io/forecast/e554f37a8164ce189acd210d00a452e0/47.4079,9.4647")
weather_data = response.json()
weather_data.keys()
print(weather_data['timezone'])
"""
Explanation: You'll be using the Dark Sky Forecast API from Forecast.io, available at https://developer.fo... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_read_bem_surfaces.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif'
surfaces = mne.read_bem_surfaces(fname, patch_stats... |
Dima806/udacity-mlnd-capstone | capstone-step1-sensitivity-check-run3.ipynb | apache-2.0 | # Select test_size and random_state for splitting a subset
test_size=0.1
random_state=2
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
import gzip
import shutil
import seaborn as sns
from collections import Counter
from sklearn.mixture ... |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/06-Merging-Joining-and-Concatenating.ipynb | apache-2.0 | import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index = [0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
... |
tjwei/HackNTU_Data_2017 | Week05/From NumPy to Logistic Regression.ipynb | mit | from PIL import Image
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('bmh')
matplotlib.rcParams['figure.figsize']=(8,5)
"""
Explanation: 起手式，導入 numpy, matplotlib
End of explanation
"""
import gzip
import pickle
with gzip.open('../Week02/mnist.pkl.gz', 'rb... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_source_label_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power
print(__doc__)
"""
Explanation... |
googledatalab/notebooks | samples/Programming Language Correlation.ipynb | apache-2.0 | import google.datalab.bigquery as bq
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
"""
Explanation: Programming Language Correlation
This sample notebook demonstrates working with GitHub activity, which has been made possible via the publicly accessible GitHub Timeline BigQuery dataset via th... |
drublackberry/fantastic_demos | Probability/.ipynb_checkpoints/Calvin-checkpoint.ipynb | mit | MAX_TIME = 80. # max time waiting at traffic light
class TrafficLightPath:
'''Class that computes the probabilities of a traffic light path over itself and the
future (children) traffic lights.
'''
p = 0 # probability of this path
T = 0 # expected time of this path
Nw = 0 # r... |
ewulczyn/talk_page_abuse | src/analysis/Attackers, Victims, and Trolls.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib_venn import venn2
from load_utils import *
from analysis_utils import *
"""
Explanation: Loading Packages... |
DiracInstitute/kbmod | notebooks/precovery_demo.ipynb | bsd-2-clause | from precovery_utils import ssoisPrecovery
"""
Explanation: Gather precovery imaging
This notebook shows how to get precovery imaging for objects found with KBMOD. Once we have an object
identified we can record the observations we used in MPC format and use the following tools to search
other telescope data for possi... |
ThunderShiviah/code_guild | wk1/notebooks/wk1.3.ipynb | mit | a = {'one':1, 'two':2, 'three': 3}
b = dict(one=1, two=2, three= 3)
c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
a == b == c
"""
Explanation: wk1.3
warm - up
Create a dictionary called numbers with the keys 'one', 'two', 'three', and associated values 1, 2, 3 three different ways.
End of explanation
"""
a['on... |
lindsayrgwatt/kickstarter | kickstarter_technology_projects_v2.ipynb | mit | import json
#data_path = '/Users/lindsayrgwatt/Dropbox/kickstarter_technology_032015.json'
data_path = 'C:\Users\lindwatt\Dropbox\kickstarter_technology_032015.json'
with open(data_path) as data_file:
data = json.load(data_file)
#print data.keys()
print "Our scraping yielded %i records" % data['count']
... |
particle-physics-playground/playground | activities/activity00_cms_muons.ipynb | mit | # Import standard libraries #
import numpy as np
import matplotlib.pylab as plt
%matplotlib notebook
# Import custom tools #
import h5hep
import pps_tools as pps
# Download the file #
file = 'dimuons_1000_collisions.hdf5'
pps.download_drive_file(file)
print("Reading in the data....")
# Read the data in as a list #... |
xMyrst/BigData | python/howto/005_Estructuras de control.ipynb | gpl-3.0 | x, y = 2, 0
if x > y:
print("x es mayor que y")
print("x sigue siendo mayor que y")
if 1 < 0:
print("1 es mayor que 0") # esto está dentro de bucle, pero no se escribe por que no cumple que 1 sea menor que 0
print("Esto se ejecuta siempre") # esto no está dentro del bloque y SE ejecuta siempre; ya que... |
molgor/spystats | notebooks/.ipynb_checkpoints/spatial_autocorrelation_from_fitted_model_POISSON-checkpoint.ipynb | bsd-2-clause | new_data.crs = {'init':'epsg:4326'}
"""
Explanation: Let´s reproject to Alberts or something with distance
End of explanation
"""
#new_data = new_data.to_crs("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs ")
"""
Explanation: Uncomment to reprojec... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/mpiesm-1-2-ham/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'mpiesm-1-2-ham', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: MPIESM-1-2-HAM
Topic: Atmosch... |
tabakg/potapov_interpolation | Dispersion_relation_chi_2_voxels_approach.ipynb | gpl-3.0 | import sympy as sp
import numpy as np
import scipy.constants
from sympy.utilities.autowrap import ufuncify
import time
import itertools
#from scipy import interpolate
import matplotlib.pyplot as plt
%matplotlib inline
from sympy import init_printing
init_printing()
import random
def plot_arr(arr):
fig = plt.fig... |
geoneill12/phys202-2015-work | assignments/assignment08/InterpolationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
"""
Explanation: Interpolation Exercise 2
End of explanation
"""
#ignore#
a = list(range(-5, 6))
b = list(range(-4, 5))
c = [5]
d = [-5]
e = [0]
g = [1]
x = np.h... |
syednasar/datascience | optimization_algos/Optimization.ipynb | mit | import time
import random
import math
people = [('Seymour','BOS'),
('Franny','DAL'),
('Zooey','CAK'),
('Walt','MIA'),
('Buddy','ORD'),
('Les','OMA')]
# LaGuardia airport in New York
destination='LGA'
"""Load this data into a dictionary with the origin and destina... |
tensorflow/docs-l10n | site/ja/agents/tutorials/6_reinforce_tutorial.ipynb | apache-2.0 | #@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 writing, software
# distributed under... |
sbrisard/janus | notebooks/using_scipy_iterative_solvers.ipynb | bsd-3-clause | import h5py as h5
import matplotlib.pyplot as plt
import numpy as np
import janus
import janus.material.elastic.linear.isotropic as material
import janus.operators as operators
import janus.fft.serial as fft
import janus.green as green
from scipy.sparse.linalg import cg, LinearOperator
%matplotlib inline
plt.rcPara... |
jacobdein/alpine-soundscapes | Compute distance to roads.ipynb | mit | points = 'sample_points_field'
roads = 'highway'
road_type_field = 'Type'
distance_table_filename = ""
"""
Explanation: Compute distance to roads
This notebook computes the distance to each of the nearest road types in a 'roads' vector map from a vector map of 'points' (sample locations).
This notebook uses GRASS G... |
dwiel/tensorflow_hmm | notebooks/gradient_descent_example.ipynb | apache-2.0 | observations = np.random.random((1, 90, 2)) * 4 - 2
plot(observations[0,:,:])
grid()
observations_variable = tf.Variable(observations)
posterior_graph, _, _ = hmm_tf.forward_backward(tf.sigmoid(observations_variable))
# build error function
sum_error_squared = tf.reduce_sum(tf.square(truth - posterior_graph))
# ca... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/sandbox-2/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.