markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Add a column indicating week/weekend | # %load snippets/01-pandas_introduction103.py | _____no_output_____ | MIT | pandas/01-pandas_introduction.ipynb | jai-singhal/data_science |
Now we can groupby the hour of the day and the weekend (or use `pivot_table`): | # %load snippets/01-pandas_introduction104.py
# %load snippets/01-pandas_introduction105.py
# %load snippets/01-pandas_introduction106.py
# %load snippets/01-pandas_introduction107.py | _____no_output_____ | MIT | pandas/01-pandas_introduction.ipynb | jai-singhal/data_science |
EXERCISE: What are the number of exceedances of hourly values above the European limit 200 µg/m3 ?Count the number of exceedances of hourly values above the European limit 200 µg/m3 for each year and station after 2005. Make a barplot of the counts. Add an horizontal line indicating the maximum number of exceedances (w... | # re-reading the data to have a clean version
no2 = pd.read_csv('data/20000101_20161231-NO2.csv', sep=';', skiprows=[1], na_values=['n/d'], index_col=0, parse_dates=True)
# %load snippets/01-pandas_introduction109.py
# %load snippets/01-pandas_introduction110.py
# %load snippets/01-pandas_introduction111.py | _____no_output_____ | MIT | pandas/01-pandas_introduction.ipynb | jai-singhal/data_science |
Testa se um modulo foi importado | 'my_keras_utilities' in sys.modules
| _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
try: train_network(model_week05, model_name, train_generator, validation_generator, **fit_params);except AttributeError: print('nope') | import keras.backend as K
K.set_image_data_format('channels_first')
K.set_floatx('float32')
print('Backend: {}'.format(K.backend()))
print('Data format: {}'.format(K.image_data_format()))
!nvidia-smib
!ls ../Task\ 5 | ls: cannot access '../Task 5': No such file or directory
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Função auxiliar | class MyCb(TrainingPlotter):
def on_epoch_end(self, epoch, logs={}):
super().on_epoch_end(epoch, logs)
def train_network(model, model_name, train_generator, validation_generator,
train_steps=10, valid_steps=10, opt='rmsprop', nepochs=50,
patience=50, reset=False,... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Subindo o dataset | #auternar o comentário, se estiver no client ou no remote
data = np.load('/etc/jupyterhub/ia368z_2s2017/datasets/cifar10-redux.npz')
#data = np.load('../Task 5/cifar10-redux.npz')
X_train = data['X_train']
y_train = data['y_train']
X_test = data['X_test']
y_test = data['y_test']
X_train.dtype, y_train.dtype, X_test.dty... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Separando o conjunto de treinamento em validação e treinamento, numa proporção 80/20 % | p=np.random.permutation(len(X_train))
percent_factor=0.85
new_train_x = X_train[p]
new_train_y = y_train[p]
new_X_train = new_train_x[0:(np.floor(len(new_train_x)*percent_factor))]
new_y_train = new_train_y[0:(np.floor(len(new_train_y)*percent_factor))]
new_X_val = new_train_x[(np.ceil(len(new_train_x)*percent_factor... | Número de diferentes classes 3
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Normalizando os dados | a=0
print(np.mean(X_train)) | 113.781868652
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Guaranteeing that it only runs onceif (a==0): X_test = X_test.astype('float32') new_X_train = new_X_train.astype('float32') new_X_val = new_X_val.astype('float32') new_X_val /= 255. new_X_train /= 255. X_test /= 255. a=1print(np.mean(new_X_train))print(np.mean(new_X_val))print(np.mean(X_tes... | from keras.utils import np_utils
## Transforma o vetor de labels para o formato de one-hot encoding.
n_classes = 3
y_train_oh = np_utils.to_categorical(new_y_train-3, n_classes)
y_val_oh = np_utils.to_categorical(new_y_val-3, n_classes)
y_test_oh = np_utils.to_categorical(y_test-3, n_classes)
print(y_train_oh.shape)
p... | (1700, 3)
(300, 3)
(500, 3)
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Fazendo o data augmentation | print(X_train.shape)
print(X_test.shape)
print('new x train shape', new_X_train.shape)
print('y train oh shape', y_train_oh.shape)
print('new x val shape', new_X_val.shape)
print('y val oh shape', y_val_oh.shape)
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
nb_train_samples = new_... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Conjunto de treinaemntosamples_train = train_datagen.flow(new_X_train)n_samples_train = nb_train_samples/batch_sizeConjunto de testesamples_test = train_datagen.flow(X_test)n_samples_test = nb_test_samples/batch_sizeConjunto de validacaosamples_val = train_datagen.flow(new_X_val)n_samples_val = nb_val_samples/batch_siz... | n_classes = len(np.unique(y_test))
print(n_classes) | 3
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Treinamento Transfer_Learning Subindo a VGG-16 | print(y_train_oh.shape)
from keras.applications.vgg16 import VGG16
modelvgg = VGG16(include_top=False, weights='imagenet',classes=y_train_oh.shape[1])
train_feature = modelvgg.predict_generator(generator=train_generator, steps=int(np.round(train_generator.n / batch_size)))
print(train_feature.shape, train_feature.dtyp... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
topmodel.summary() modelvgg.summary() | train_feature.shape[1:]
train_feat = train_feature.reshape(1700,512)
print(train_feat.shape)
modelvgg.output
def model_build():
img_rows, img_cols = 32, 32 # Dimensões das imagens
#imagens com 3 canais e 32x32
input_shape = (3, img_rows, img_cols)
# Definindo a rede
model = Sequential()
#primei... | cifar_redux_augmented_vgg.history utils week05
cifar_redux_augmented_vgg.model week02 week06
models week03
my_cifar_dataplus_model_weights.h5 week04
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
w1, b1, w2, b2 = load_model('../my_cifar_dataplus_model_weights.h5').get_weights() w1, b1, w2, b2 = load_model('../my_cifar_dataplus_model_weights').get_weights()m m mmm | model2.load_weights('../my_cifar_dataplus_model_weights.h5')
| _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Aqui os features deixam de ser imagens model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(n_classes)) model.add(Activation('softmax')) topmodel = Sequential() topmodel.add(layer=keras.layers.Flatten(input_shape=feat_train.shape[1:])) t... | modelvgg.layers.pop(18)
modelvgg.layers.pop(17)
modelvgg.layers.pop(16)
modelvgg.layers.pop(15)
#modelvgg.summary()
train_features = modelvgg.predict(new_X_train)
print('train_features shape and type',train_features.shape,train_features.dtype)
validation_features = modelvgg.predict(new_X_val)
print('validation_features... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Treinando class MyCb(TrainingPlotter): | class MyCb(TrainingPlotter):
def on_epoch_end(self, epoch, logs={}):
super().on_epoch_end(epoch, logs)
def train_network(model, model_name, Xtra, ytra, Xval, yval,
opt='rmsprop', batch_size=100, nepochs=50, patience=50, reset=False, ploss=1.0):
do_plot = (ploss > 0.0)
... | _____no_output_____ | MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Construção da rede neural | print(train_features.shape)
print(new_X_train.shape)
img_height, img_width = new_X_train.shape[2],new_X_train.shape[3]
print(img_height,img_width)
!ls ..
from keras.models import Model
from keras.models import load_model
model_name = '../cifar10_vgg_finetune' # modelo da rede atual
top_model_name = '../cifar_redux_a... | (1700, 3, 32, 32) (1700, 3) (300, 3, 32, 32) (300, 3)
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
(1600, 32, 32, 3) (1600, 3) (400, 32, 32, 3) (400, 3) | h = model.fit(new_X_train.reshape(1700,3,32,32), y_train_oh,
validation_data=(new_X_val.reshape(300,3,32,32), y_val_oh),
batch_size=batch_size,
epochs=100,
)
h = model.fit(X_train[train_i], y_train_oh[train_i],
validation_data=(X_train[val_i], y_trai... | [INFO] compiling model...
[INFO] training for 100 epochs ...
| MIT | CIFAR_10/Other_numpy/my_cifar_transferlearning-Copy1.ipynb | kaelgabriel/Neural_Networks_Pytorch |
Data Distribution vs. Sampling Distribution: What You Need to Know This notebook is accompanying the article [Data Distribution vs. Sampling Distribution: What You Need to Know](https://www.ealizadeh.com/blog/statistics-data-vs-sampling-distribution/).Subscribe to **[my mailing list](https://www.ealizadeh.com/subscrib... | from IPython.display import Image
Image("https://www.ealizadeh.com/wp-content/uploads/2021/01/data_dist_sampling_dist_featured_image.png", width=1200) | _____no_output_____ | MIT | notebooks/data_vs_sampling_distributions.ipynb | e-alizadeh/medium |
--- It is important to distinguish between the data distribution (aka population distribution) and the sampling distribution. The distinction is critical when working with the central limit theorem or other concepts like the standard deviation and standard error.In this post we will go over the above concepts and as we... | # Plotting packages and initial setup
import seaborn as sns
sns.set_theme(palette="pastel")
sns.set_style("white")
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams["figure.dpi"] = 150
savefig_options = dict(format="png", dpi=150, bbox_inches="tight")
from scipy.stats import skewnorm
from sklearn.p... | _____no_output_____ | MIT | notebooks/data_vs_sampling_distributions.ipynb | e-alizadeh/medium |
Plotting the data distribution | fig, ax = plt.subplots(figsize=(10, 6))
ax.set_title("Data Distribution", fontsize=24, fontweight="bold")
sns.histplot(skewed_data_scaled, bins=30, stat="density", kde=True, legend=False, ax=ax)
# fig.savefig("original_skewed_data_distribution.png", **savefig_options) | _____no_output_____ | MIT | notebooks/data_vs_sampling_distributions.ipynb | e-alizadeh/medium |
Sampling DistributionIn the sampling distribution, you draw samples from the dataset and compute a statistic like the mean. It's very important to differentiate between the data distribution and the sampling distribution as most confusion comes from the operation done on either the original dataset or its (re)samples.... | import numpy as np
import random
sample_size = 50
sample_means = []
random.seed(1) # Setting the seed for reproducibility of the result
for _ in range(2000):
sample = random.sample(skewed_data_scaled.tolist(), sample_size)
sample_means.append(np.mean(sample))
print(
f"Mean: {np.mean(... | _____no_output_____ | MIT | notebooks/data_vs_sampling_distributions.ipynb | e-alizadeh/medium |
Above sampling distribution is basically the histogram of the mean of each drawn sample (in above, we draw samples of 50 elements over 2000 iterations). The mean of the above sampling distribution is around 0.23, as can be noted from computing the mean of all samples means.⚠️ *Do not confuse the sampling distribution w... | from mlxtend.evaluate import bootstrap
avg, std_err, ci_bounds = bootstrap(
skewed_data_scaled,
num_rounds=1000,
func=np.mean, # A function to compute a sample statistic can be passed here
ci=0.95,
seed=123 # Setting the seed for reproducibility of the result
)
print(
f"Mean: {avg.round(5)... | Mean: 0.23293
Standard Error: +/- 0.00144
CI95: [0.23023, 0.23601]
| MIT | notebooks/data_vs_sampling_distributions.ipynb | e-alizadeh/medium |
Test "best of two" classifier This notebook test a classifier that operates in two layers:- First we use a SVM classifier to label utterances with high degree of certainty.- Afterwards we use heuristics to complete the labeling | import os
import sys
import pandas as pd
import numpy as np
import random
import pickle
import matplotlib.pyplot as plt
root_path = os.path.dirname(os.path.abspath(os.getcwd()))
sys.path.append(root_path)
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
f... | Accuracy of SVM classifier on training set: 0.30
| MIT | notebooks/3-. Check combination 2 layers classifier with test set [utterance level][180t].ipynb | cinai/classification_ibl |
Test 2 | test_i = '[test2]'
file_name = test_i+'IBL_topic_distribution_by_utterance_before_after_{}_{}.xlsx'.format(WITH_STEMMING,NUM_TOPICS)
df_data = pd.read_excel(os.path.join(tables_path,'test','before_after',file_name))
the_keys = list(set(df_data['phase']))
total_samples = 0
class_samples = {}
for key in the_keys:
n =... | Accuracy 0.466
| MIT | notebooks/3-. Check combination 2 layers classifier with test set [utterance level][180t].ipynb | cinai/classification_ibl |
Classifying Digits with K-Nearest-Neighbors (KNN) This is a very simple implementation of classifying images using the k-nearest-neighbors algorithm. The accuracy is pretty good for how simple the algorithm is. The parameters can be tinkered with but at the time of writing I am using k = 5, training data size = 10000,... | k = 5
batch_size_train = 10000
batch_size_test = 1000
train_mnist = torchvision.datasets.MNIST('C:/projects/summer2020/vision/digits/', train=True, download=True,
transform=torchvision.transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_mnist, batch_size=batch_size_train, shu... | _____no_output_____ | MIT | digits_knn.ipynb | sasiegel/mnist-classification |
The k-nearest-neighbors algorithm is not very efficient and my implementation is even less efficient. I was aiming for simplicity over efficiency. We loop through each test image and find the distance to every training image. Distance is measured as Euclidean (p=2). We take the k nearest images and record the ground tr... | n_test = test_imgs.shape[0]
n_train = train_imgs.shape[0]
pred_test_targets = torch.zeros_like(test_targets)
for i in range(n_test):
test_img = test_imgs[i]
distances = [torch.dist(test_img, train_imgs[j], p=2) for j in range(n_train)]
nearest_indices = np.array(distances).argsort()[:5]
pred_test_target... | Prediction accuracy: 0.959
| MIT | digits_knn.ipynb | sasiegel/mnist-classification |
Distributed data parallel BERT training with TensorFlow2 and SMDataParallelHSMDataParallel is a new capability in Amazon SageMaker to train deep learning models faster and cheaper. SMDataParallel is a distributed data parallel training framework for TensorFlow, PyTorch, and MXNet.This notebook example shows how to use... | %%time
! python3 -m pip install --upgrade sagemaker
import sagemaker
from sagemaker import get_execution_role
from sagemaker.estimator import Estimator
import boto3
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
role = get_execution_role() # provide a pre-existing role ARN as an a... | SageMaker Execution Role:arn:aws:iam::835319576252:role/service-role/AmazonSageMaker-ExecutionRole-20191006T135881
AWS account:835319576252
AWS region:us-east-1
| Apache-2.0 | 08_optimize/distributed-training/tensorflow/data_parallel/bert/tensorflow2_smdataparallel_bert_demo.ipynb | MarcusFra/workshop |
Prepare SageMaker Training Images1. SageMaker by default use the latest [Amazon Deep Learning Container Images (DLC)](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) TensorFlow training image. In this step, we use it as a base image and install additional dependencies required for trai... | image = "tf2-smdataparallel-bert-sagemaker" # Example: tf2-smdataparallel-bert-sagemaker
tag = "latest" # Example: latest
!pygmentize ./Dockerfile
!pygmentize ./build_and_push.sh
%%time
! chmod +x build_and_push.sh; bash build_and_push.sh {region} {image} {tag} | WARNING! Using --password via the CLI is insecure. Use --password-stdin.
WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
Login Succeeded
Sendi... | Apache-2.0 | 08_optimize/distributed-training/tensorflow/data_parallel/bert/tensorflow2_smdataparallel_bert_demo.ipynb | MarcusFra/workshop |
Preparing FSx Input for SageMaker1. Download and prepare your training dataset on S3.2. Follow the steps listed here to create a FSx linked with your S3 bucket with training data - https://docs.aws.amazon.com/fsx/latest/LustreGuide/create-fs-linked-data-repo.html. Make sure to add an endpoint to your VPC allowing S3 a... | # Clone herring forks repository for reference implementation BERT with TensorFlow2-SMDataParallel
!rm -rf deep-learning-models
!git clone --recursive https://github.com/HerringForks/deep-learning-models.git
import boto3
import sagemaker
sm = boto3.client('sagemaker')
notebook_instance_name = sm.list_notebook_instances... | _____no_output_____ | Apache-2.0 | 08_optimize/distributed-training/tensorflow/data_parallel/bert/tensorflow2_smdataparallel_bert_demo.ipynb | MarcusFra/workshop |
Configure FSx Input for the SageMaker Training Job | from sagemaker.inputs import FileSystemInput
#YOUR_MOUNT_PATH_FOR_TRAINING_DATA # NOTE: '/fsx/' will be the root mount path. Example: '/fsx/albert''''
file_system_directory_path='/fsx/'
file_system_access_mode='rw'
file_system_type='FSxLustre'
train_fs = FileSystemInput(file_system_id=file_system_id,
... | _____no_output_____ | Apache-2.0 | 08_optimize/distributed-training/tensorflow/data_parallel/bert/tensorflow2_smdataparallel_bert_demo.ipynb | MarcusFra/workshop |
Continuous Control---In this notebook I will implement the distributed disttributional deep determenstic policy gradients (D4PG) algorithm. different algorithm can also be used to solve this problem such as : ''' 1 - Deep determenstic policy gradients (DDPG) 2 - Proximal policy optimization (PPO) 3 - Async... | import random
import time
import torch
import torch.nn as nn
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from unityagents import UnityEnvironment
from d4pg_agent import Agent
# select thi... | INFO:unityagents:
'Academy' started successfully!
Unity Academy name: Academy
Number of Brains: 1
Number of External Brains : 1
Lesson number : 0
Reset Parameters :
goal_speed -> 1.0
goal_size -> 5.0
Unity brain name: ReacherBrain
Number of Visual Observations (per agent): 0
... | MIT | Project-2_Continuous-Control/Continuous-Control/Continuous_Control.ipynb | Mohammedabdalqader/DRL |
Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python. | # get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name] | _____no_output_____ | MIT | Project-2_Continuous-Control/Continuous-Control/Continuous_Control.ipynb | Mohammedabdalqader/DRL |
2. Examine the State and Action SpacesRun the code cell below to print some information about the environment. | # reset the environment
env_info = env.reset(train_mode=True)[brain_name]
# number of agents
num_agents = len(env_info.agents)
print('Number of agents:', num_agents)
# size of each action
action_size = brain.vector_action_space_size
print('Size of each action:', action_size)
# examine the state space
states = env_i... | Number of agents: 20
Size of each action: 4
There are 20 agents. Each observes a state with length: 33
The state for the first agent looks like: [ 0.00000000e+00 -4.00000000e+00 0.00000000e+00 1.00000000e+00
-0.00000000e+00 -0.00000000e+00 -4.37113883e-08 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e... | MIT | Project-2_Continuous-Control/Continuous-Control/Continuous_Control.ipynb | Mohammedabdalqader/DRL |
3. Take Random Actions in the EnvironmentIn the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.Note that **in this coding environment, you will not be able to watch the agents while they are training**, and you should set `train_mode=True` to res... | for i in range(10):
env_info = env.reset(train_mode=True)[brain_name] # reset the environment
states = env_info.vector_observations # get the current state (for each agent)
scores = np.zeros(num_agents) # initialize the score (for each agent)
while True... | _____no_output_____ | MIT | Project-2_Continuous-Control/Continuous-Control/Continuous_Control.ipynb | Mohammedabdalqader/DRL |
4. ImplementationNow i will implement the D4PG Algorithm | agent = Agent(state_size=state_size, action_size=action_size,num_agents=num_agents, seed=7)
# Training the agent over a number of episodes until we reach the desired average reward, which is > 30
def d4pg(n_episodes=2000):
scores = []
scores_deque = deque(maxlen=100)
rolling_average_score = []
... | _____no_output_____ | MIT | Project-2_Continuous-Control/Continuous-Control/Continuous_Control.ipynb | Mohammedabdalqader/DRL |
NewEgg.Com WebScraping Program For Laptops - Beta v1.0 - April 2020--- | # Import dependencies.
import os
import re
import time
import glob
import random
import datetime
import requests
import pandas as pd
from re import search
from splinter import Browser
from playsound import playsound
from bs4 import BeautifulSoup as soup | _____no_output_____ | MIT | archive/newer_notebooks_wip_drafts/drafts/new_egg_webscraper_app_NOTFINAL_review_too_much.ipynb | jhustles/new_egg_webscraper |
Functions & Classes Setup--- | # Build a function to return date throughout the program.
def return_dt():
global current_date
current_date = str(datetime.datetime.now()).replace(':','.').replace(' ','_')[:-7]
return current_date
"""
NewEgg WebScraper function that scrapes data, saves it into a csv file, and creates Laptop... | _____no_output_____ | MIT | archive/newer_notebooks_wip_drafts/drafts/new_egg_webscraper_app_NOTFINAL_review_too_much.ipynb | jhustles/new_egg_webscraper |
Main Program Logic--- | """ Welcome to the program message!
"""
print("=== NewEgg.Com Laptop WebScraper Beta v1.0 ===")
print("=="*30)
print('Scope: This project is a beta and is only built to scrape the laptop section of NewEgg.com due to limited time. \n')
print("Instructions: \n")
return_dt()
print(f'Current Date And Time: {current_da... | _____no_output_____ | MIT | archive/newer_notebooks_wip_drafts/drafts/new_egg_webscraper_app_NOTFINAL_review_too_much.ipynb | jhustles/new_egg_webscraper |
Saliency visualization | from chainer_chemistry.saliency.calculator.gradient_calculator import GradientCalculator
from chainer_chemistry.saliency.calculator.integrated_gradients_calculator import IntegratedGradientsCalculator
from chainer_chemistry.link_hooks.variable_monitor_link_hook import VariableMonitorLinkHook
# 1. instantiation
gradie... | _____no_output_____ | MIT | examples/table/visualize-saliency-table.ipynb | corochann/chainer-saliency |
visualize saliency of all data --> this can be considered as "feature importance" | saliency_mean = np.mean(saliency_vanilla, axis=0)
visualizer.visualize(saliency_mean, feature_names=iris.feature_names, num_visualize=-1,
scaler=normalize_scaler)
visualizer.visualize(saliency_mean, feature_names=iris.feature_names, num_visualize=-1,
scaler=normalize_scaler, save... | _____no_output_____ | MIT | examples/table/visualize-saliency-table.ipynb | corochann/chainer-saliency |
sklearn random forest feature importanceRef: - https://qiita.com/TomokIshii/items/290adc16e2ca5032ca07 - https://stackoverflow.com/questions/44101458/random-forest-feature-importance-chart-using-python | import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(
X,... | _____no_output_____ | MIT | examples/table/visualize-saliency-table.ipynb | corochann/chainer-saliency |
Twin-Delayed DDPGComplete credit goes to this [awesome Deep Reinforcement Learning 2.0 Course on Udemy](https://www.udemy.com/course/deep-reinforcement-learning/) for the code. Installing the packages | !pip install pybullet | Requirement already satisfied: pybullet in /usr/local/lib/python3.6/dist-packages (2.7.1)
| Apache-2.0 | P2S10.ipynb | aks1981/ML |
Importing the libraries | import os
import time
import random
import numpy as np
import matplotlib.pyplot as plt
import pybullet_envs
import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
from gym import wrappers
from torch.autograd import Variable
from collections import deque | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Step 1: We initialize the Experience Replay memory | class ReplayBuffer(object):
def __init__(self, max_size=1e6):
self.storage = []
self.max_size = max_size
self.ptr = 0
def add(self, transition):
if len(self.storage) == self.max_size:
self.storage[int(self.ptr)] = transition
self.ptr = (self.ptr + 1) % self.max_size
else:
sel... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Step 2: We build one neural network for the Actor model and one neural network for the Actor target | class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.layer_1 = nn.Linear(state_dim, 400)
self.layer_2 = nn.Linear(400, 300)
self.layer_3 = nn.Linear(300, action_dim)
self.max_action = max_action
def forward(self, x):
x = F.relu... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Step 3: We build two neural networks for the two Critic models and two neural networks for the two Critic targets | class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
# Defining the first Critic neural network
self.layer_1 = nn.Linear(state_dim + action_dim, 400)
self.layer_2 = nn.Linear(400, 300)
self.layer_3 = nn.Linear(300, 1)
# Defining the second Criti... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Steps 4 to 15: Training Process | # Selecting the device (CPU or GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Building the whole Training Process into a class
class TD3(object):
def __init__(self, state_dim, action_dim, max_action):
self.actor = Actor(state_dim, action_dim, max_action).to(device)
self.acto... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We make a function that evaluates the policy by calculating its average reward over 10 episodes | def evaluate_policy(policy, eval_episodes=10):
avg_reward = 0.
for _ in range(eval_episodes):
obs = env.reset()
done = False
while not done:
action = policy.select_action(np.array(obs))
obs, reward, done, _ = env.step(action)
avg_reward += reward
avg_reward /= eval_episodes
print (... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We set the parameters | env_name = "AntBulletEnv-v0" # Name of a environment (set it to any Continous environment you want)
seed = 0 # Random seed number
start_timesteps = 1e4 # Number of iterations/timesteps before which the model randomly chooses an action, and after which it starts to use the policy network
eval_freq = 5e3 # How often the ... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We create a file name for the two saved models: the Actor and Critic models | file_name = "%s_%s_%s" % ("TD3", env_name, str(seed))
print ("---------------------------------------")
print ("Settings: %s" % (file_name))
print ("---------------------------------------") | ---------------------------------------
Settings: TD3_AntBulletEnv-v0_0
---------------------------------------
| Apache-2.0 | P2S10.ipynb | aks1981/ML |
We create a folder inside which will be saved the trained models | if not os.path.exists("./results"):
os.makedirs("./results")
if save_models and not os.path.exists("./pytorch_models"):
os.makedirs("./pytorch_models") | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We create the PyBullet environment | env = gym.make(env_name) | /usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: [33mWARN: Box bound precision lowered by casting to float32[0m
warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
| Apache-2.0 | P2S10.ipynb | aks1981/ML |
We set seeds and we get the necessary information on the states and actions in the chosen environment | env.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0]) | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We create the policy network (the Actor model) | policy = TD3(state_dim, action_dim, max_action) | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
[link text](https://) We create the Experience Replay memory | replay_buffer = ReplayBuffer() | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We define a list where all the evaluation results over 10 episodes are stored | evaluations = [evaluate_policy(policy)] | ---------------------------------------
Average Reward over the Evaluation Step: 9.804960
---------------------------------------
| Apache-2.0 | P2S10.ipynb | aks1981/ML |
We create a new folder directory in which the final results (videos of the agent) will be populated | def mkdir(base, name):
path = os.path.join(base, name)
if not os.path.exists(path):
os.makedirs(path)
return path
work_dir = mkdir('exp', 'brs')
monitor_dir = mkdir(work_dir, 'monitor')
max_episode_steps = env._max_episode_steps
save_env_vid = False
if save_env_vid:
env = wrappers.Monitor(env, mon... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
We initialize the variables | total_timesteps = 0
timesteps_since_eval = 0
episode_num = 0
done = True
t0 = time.time() | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Training | max_timesteps = 500000
# We start the main loop over 500,000 timesteps
while total_timesteps < max_timesteps:
# If the episode is done
if done:
# If we are not at the very beginning, we start the training process of the model
if total_timesteps != 0:
print("Total Timesteps: {} Episode Num: {} Rewa... | Total Timesteps: 1000 Episode Num: 1 Reward: 512.6232988347093
Total Timesteps: 2000 Episode Num: 2 Reward: 493.70176430884203
Total Timesteps: 3000 Episode Num: 3 Reward: 492.40008391554187
Total Timesteps: 4000 Episode Num: 4 Reward: 476.3080060617612
Total Timesteps: 5000 Episode Num: 5 Reward: 494.0312655821267
---... | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Inference | class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.layer_1 = nn.Linear(state_dim, 400)
self.layer_2 = nn.Linear(400, 300)
self.layer_3 = nn.Linear(300, action_dim)
self.max_action = max_action
def forward(self, x):
x = F.relu... | _____no_output_____ | Apache-2.0 | P2S10.ipynb | aks1981/ML |
Web predictionsThe purpose of this notebook is to experiment with making predictions from "raw" accumulated user values, thatcould for instance be user input from a web form. | import findspark
findspark.init()
findspark.find()
import pyspark
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
conf = pyspark.SparkConf().setAppName('sparkify-capstone-web').setMaster('local')
sc = pyspark.SparkContext(conf=conf)
spark = SparkSession(sc)
from pyspark.ml.classificat... | _____no_output_____ | MIT | udacity/data-scientist-nanodegree/sparkify/.ipynb_checkpoints/web_pred-checkpoint.ipynb | thomasrobertz/mooc |
Lecture 9 - Motor Control Introduction to modeling and simulation of human movementhttps://github.com/BMClab/bmc/blob/master/courses/ModSim2018.md * In class: | import numpy as np
#import pandas as pd
#import pylab as pl
import matplotlib.pyplot as plt
import math
%matplotlib notebook | _____no_output_____ | MIT | courses/modsim2018/matheuspiquini/Lecture11.ipynb | MatheusKP/bmc |
Muscle properties | Lslack = .223
Umax = .04
Lce_o = .093 #optmal l
width = .63
Fmax = 3000
a = .25
b = .25*10 | _____no_output_____ | MIT | courses/modsim2018/matheuspiquini/Lecture11.ipynb | MatheusKP/bmc |
Initial conditions | LceNorm = .087/Lce_o
t0 = 0
tf = 2.99
h = 1e-3
u = 1
a = 0
t = np.arange(t0,tf,h)
F = np.empty(t.shape)
Fkpe = np.empty(t.shape)
FiberLength = np.empty(t.shape)
TendonLength = np.empty(t.shape)
U = np.arange(t0,1,h)
## Funcoes
def computeTendonForce(LseeNorm, Lce_o, Lslack):
'''
Compute Tendon Length
I... | _____no_output_____ | MIT | courses/modsim2018/matheuspiquini/Lecture11.ipynb | MatheusKP/bmc |
Simulation - Parallel | for i in range (len(t)):
#ramp
if t[i]<=1:
Lm = 0.31
elif t[i]>1 and t[i]<2:
Lm = .31 - .04*(t[i]-1)
#print(Lm)
#####################################################################
LseeNorm = (Lm/Lce_o) - LceNorm
FTendonNorm = computeTendonForce(LseeNorm, Lce_o... | _____no_output_____ | MIT | courses/modsim2018/matheuspiquini/Lecture11.ipynb | MatheusKP/bmc |
Plot | fig, ax = plt.subplots(1, 1, figsize=(6,6), sharex=True)
ax.plot(t,F,c='red')
plt.grid()
plt.xlabel('time (s)')
plt.ylabel('Force [N]')
#ax.legend()
fig, ax = plt.subplots(1, 1, figsize=(6,6), sharex=True)
ax.plot(t,FiberLength, label = 'fibra')
ax.plot(t, TendonLength, label = 'tendao')
ax.plot(t,FiberLength + Ten... | _____no_output_____ | MIT | courses/modsim2018/matheuspiquini/Lecture11.ipynb | MatheusKP/bmc |
Load a scope from a yaml file. | with open('gbnrtc_scope.yaml') as yf:
for _ in range(100):
print(yf.readline(), end="")
scope = emat.Scope('gbnrtc_scope.yaml')
scope | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Create a brand new set of `Boxes`. | boxes = emat.Boxes(scope=scope) | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Define a new top-level box (i.e. a box with no parents to inherit from). | box_1 = emat.Box(name='High Population Growth', scope=scope) | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Add a lower bound for population growth, to ensure the include values are all "high". | box_1.set_lower_bound('Land Use - CBD Focus', 1.0) | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Add some things as "relevant features" that we are interested in analyzing,even though we do not set bounds on them (yet). | box_1.relevant_features.add('Total LRT Boardings')
box_1.relevant_features.add('Downtown to Airport Travel Time')
box_1.relevant_features.add('Peak Transit Share')
box_1.relevant_features.add('AM Trip Time (minutes)')
box_1.relevant_features.add('AM Trip Length (miles)')
box_1.relevant_features.add('Region-wide VMT')
b... | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Define a new lower level box, which will inherit from the top level box we just created. | box_2 = emat.Box(name='Automated Vehicles', scope=scope, parent='High Population Growth') | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Set some thresholds on this lower level box. | box_2.set_lower_bound('Freeway Capacity', 1.25)
box_2.set_upper_bound('Auto IVTT Sensitivity', 0.9)
box_2
box_2.parent_box_name | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
So far, the individual boxes we created are just loose boxes. To connect them, we need to add them to the master `Boxes` object. | boxes.add(box_1)
boxes.add(box_2) | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
We can check on what named boxes are in a `Boxes` object with the `plain_names` method,which just gives the names, or the `fancy_names` method, which adds some iconsto help indicate the hierarchy. | boxes.plain_names()
boxes.fancy_names() | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Now that the boxes are linked together in a `Boxes` object, we can use the `get_chain` method to aggregatethe attributes of any box along with all parents in the chain. | boxes.get_chain('Automated Vehicles') | _____no_output_____ | BSD-3-Clause | docs/source/emat.examples/GBNRTC/gbnrtc_fresh_boxes.ipynb | tlumip/tmip-emat |
Time Series analysis of O'hare taxi rides data | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import TimeSeriesSplit, cross_validate, GridSearchCV
pd.set_option('display.max_rows', 6)
plt.style.use('ggplot')
... | _____no_output_____ | MIT | src/time_series_modelling.ipynb | jsleslie/Ohare_taxi_demand |
Tom's functions | # Custom functions
def lag_df(df, lag, cols):
return df.assign(**{f"{col}-{n}": df[col].shift(n) for n in range(1, lag + 1) for col in cols})
def ts_predict(input_data, model, n=20, responses=1):
predictions = []
n_features = input_data.size
for _ in range(n):
predictions = np.append(predicti... | _____no_output_____ | MIT | src/time_series_modelling.ipynb | jsleslie/Ohare_taxi_demand |
Lesson 04: Numpy - Used for working with tensors- Provides vectors, matrices, and tensors- Provides mathematical functions that operate on vectors, matrices, and tensors- Implemented in Fortran and C in the backend | import numpy as np | _____no_output_____ | MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Making Arrays | arr = np.array([1, 2, 3])
print(arr, type(arr), arr.shape, arr.dtype, arr.ndim)
matrix = np.array(
[[1, 2, 3],
[4, 5, 6.2]]
)
print(matrix, type(matrix), matrix.shape, matrix.dtype, matrix.ndim)
a = np.zeros((10, 2))
print(a)
a = np.ones((4, 5))
print(a)
a = np.full((2, 3, 5), 6)
print(a)
a = np.eye(4)
print(a)... | [[0.94118745 0.22994581 0.75183424 0.3433619 0.53614551]
[0.4701853 0.68700713 0.3685086 0.19023418 0.17094098]
[0.96813951 0.00628098 0.02295652 0.9007116 0.03263926]
[0.56018717 0.13823581 0.71362452 0.57653406 0.9263221 ]
[0.22776242 0.92652569 0.04206205 0.13036483 0.10911229]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Indexing | arr = np.array([
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15]
])
print(arr) | [[ 1 2 3 4 5]
[ 6 7 8 9 10]
[11 12 13 14 15]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
The indexing format is: [rows , columns]You can then slice the individual dimension as follows: [start : end , start : end] | print(arr[1:, 2:4])
a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
print(a[ [0, 1, 2, 3], [1, 0, 2, 0] ])
print(a[0, 1], a[1, 0], a[2, 2], a[3, 0])
print(np.array([a[0, 1], a[1, 0], a[2, 2], a[3, 0]]))
b = np.array([1, 0, 2, 0])
print(a[np.arange(4), b])
a[np.arange(4), b] += 7
print(a)
a = np.array([[1,2,3... | [ 8 9 10 11 12]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Data Types | b = np.array([1, 2, 3], dtype=np.float64)
print(b.dtype) | float64
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
https://numpy.org/doc/stable/reference/arrays.dtypes.html Operations | x = np.array([
[1, 2],
[3, 4]
])
y = np.array([
[5, 6],
[7, 8]
])
print(x, x.shape)
print(y, y.shape)
print(x + y)
print(np.add(x, y))
print(x - y)
print(np.subtract(x, y))
print(x * y)
print(np.multiply(x, y))
print(x / y)
print(np.divide(x, y)) | [[0.2 0.33333333]
[0.42857143 0.5 ]]
[[0.2 0.33333333]
[0.42857143 0.5 ]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Matrix Multiplication | w = np.array([2, 4])
v = np.array([4, 6])
print(x)
print(y)
print(w)
print(v) | [[1 2]
[3 4]]
[[5 6]
[7 8]]
[2 4]
[4 6]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Vector-vector multiplication | print(v.dot(w))
print(np.dot(v, w)) | 32
32
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Matrix-vector multiplication | print(x.dot(w)) | [10 22]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Matrix multiplication | print(x.dot(y))
print(np.dot(x, y)) | [[19 22]
[43 50]]
[[19 22]
[43 50]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Transpose | print(x)
print(x.T) | [[1 2]
[3 4]]
[[1 3]
[2 4]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html Other Operations | print(x)
print(np.sum(x))
print(np.sum(x, axis=0))
print(np.sum(x, axis=1)) | [[1 2]
[3 4]]
10
[4 6]
[3 7]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
More array operations are listed here:http://docs.scipy.org/doc/numpy/reference/routines.math.html Broadcasting Broadcasting allows Numpy to work with arrays of different shapes. Operations which would have required loops can now be done without them hence speeding up your program. | x = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12],
[13, 14, 15],
[16, 17, 18],
])
print(x, x.shape)
y = np.array([1, 2, 3])
print(y, y.shape) | [1 2 3] (3,)
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Loop Approach | z = np.empty_like(x)
print(z, z.shape)
for i in range(x.shape[0]):
z[i, :] = x[i, :] + y
print(z) | [[ 2 4 6]
[ 5 7 9]
[ 8 10 12]
[11 13 15]
[14 16 18]
[17 19 21]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Tile Approach | yy = np.tile(y, (6, 1))
print(yy, yy.shape)
print(x + y) | [[ 2 4 6]
[ 5 7 9]
[ 8 10 12]
[11 13 15]
[14 16 18]
[17 19 21]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Broadcasting Approach | print(x, x.shape)
print(y, y.shape)
print(x + y) | [[ 2 4 6]
[ 5 7 9]
[ 8 10 12]
[11 13 15]
[14 16 18]
[17 19 21]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
- https://numpy.org/doc/stable/user/basics.broadcasting.html- http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc- http://docs.scipy.org/doc/numpy/reference/ufuncs.htmlavailable-ufuncs Reshape | x = np.array([
[1, 2, 3],
[4, 5, 6]
])
y = np.array([2, 2])
print(x, x.shape)
print(y, y.shape) | [[1 2 3]
[4 5 6]] (2, 3)
[2 2] (2,)
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Transpose Approach | xT = x.T
print(xT)
xTw = xT + y
print(xTw)
x = xTw.T
print(x) | [[3 4 5]
[6 7 8]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Transpose approach in one line | print( (x.T + y).T ) | [[3 4 5]
[6 7 8]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
Reshape Approach | print(y, y.shape, y.ndim)
y = np.reshape(y, (2, 1))
print(y, y.shape, y.ndim)
print(x + y) | [[3 4 5]
[6 7 8]]
| MIT | Labs/Lab 4/Lesson04-numpy.ipynb | cvlpieas/CIS428-ICV |
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