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 new numerical featureCreate synthetic column, strongly correlated with target.Each value is calculated according to the formula: v = y * a + random(-b, b) So its scaled target value with some noise.Then a fraction of values is permuted, to reduce the correlation.In this case, a=10, b=5, fraction=0.05 | if 'new_feature' in df.columns:
df.pop('new_feature')
new_feature, corr = create_numerical_feature_classification(y, fraction=0.05, seed=SEED, verbose=True)
df = pd.concat([pd.Series(new_feature, name='new_feature'), df], axis=1)
plt.scatter(new_feature, y, alpha=0.3)
plt.xlabel('Feature value')
plt.ylabel('Targe... | Initial new feature - target point biserial correlation, without shuffling: 0.878, p: 0.0
New feature - target point biserial correlation, after shuffling: 0.849, p: 0.0
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
Random Forest feature importanceRandom Forest offers a simple way to measure feature importance. A certain feature is considered to be important if it reduced node impurity often, during fitting the trees.We can see that adding a feature strongly correlated with target improved the model's performance, compared to res... | X_train, X_test, y_train, y_test = train_test_split(
df, y, test_size=0.3, random_state=SEED)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
rf = RandomForestClassifier(random_state=SEED)
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
print(f'Random Fore... | Random Forest f1 score: 0.985
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
Permutation feature importanceThe impurity-based feature importance of Random Forests suffers from being computed on statistics derived from the training dataset: the importances can be high even for features that are not predictive of the target variable, as long as the model has the capacity to use them to overfit.F... | sf = SimilarityForestClassifier(n_estimators=100, random_state=SEED).fit(X_train, y_train)
perm_importance_results = get_permutation_importances(rf, sf,
X_train, y_train, X_test, y_test,
corr, df.columns.value... | 19it [31:09, 98.37s/it]
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
New categorical feature | if 'new_feature' in df.columns:
df.pop('new_feature')
new_feature, corr = create_categorical_feature_classification(y, fraction=0.05, seed=SEED, verbose=True)
df = pd.concat([pd.Series(new_feature, name='new_feature'), df], axis=1)
df_category = pd.concat([pd.Series(new_feature, name='new_feature'), pd.Series(y, ... | 19it [26:26, 83.49s/it]
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
Regression, numerical feature | X, y = load_svmlight_file('data/mpg')
X = X.toarray().astype(np.float32)
features = [f'f{i+1}' for i in range(X.shape[1])]
df = pd.DataFrame(X, columns=features)
df.head()
if 'new_feature' in df.columns:
df.pop('new_feature')
new_feature, corr = create_numerical_feature_regression(y, fraction=0.2, seed=SEED, verb... | 19it [42:41, 134.82s/it]
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
Regression, categorical feature | if 'new_feature' in df.columns:
df.pop('new_feature')
new_feature, corr = create_categorical_feature_regression(y, fraction=0.15, seed=SEED, verbose=True)
df = pd.concat([pd.Series(new_feature, name='new_feature'), df], axis=1)
plt.scatter(new_feature, y, alpha=0.3)
plt.xlabel('Feature value')
plt.ylabel('Target'... | 19it [46:58, 148.33s/it]
| BSD-3-Clause | examples/bias/Bias.ipynb | dabrze/similarity_forest |
Heap MapsA heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. | from beakerx import *
data = [[533.08714795974, 484.92105712087596, 451.63070008303896, 894.4451947886148, 335.44965728686225, 640.9424094527392, 776.2709495045433, 621.8819257981404, 793.2905673902735, 328.97078791524234, 139.26962328268513, 800.9314566259062, 629.0795214099808, 418.90954534196544, 513.8036215... | _____no_output_____ | Apache-2.0 | doc/python/Heatmap.ipynb | ssadedin/beakerx |
Copyright 2019 The TensorFlow Authors. | #@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... | _____no_output_____ | MIT | Course2Part4Lesson4.ipynb | MBadriNarayanan/TensorFlow |
Building a Small Model from ScratchBut before we continue, let's start defining the model:Step 1 will be to import tensorflow. | import tensorflow as tf | _____no_output_____ | MIT | Course2Part4Lesson4.ipynb | MBadriNarayanan/TensorFlow |
We then add convolutional layers as in the previous example, and flatten the final result to feed into the densely connected layers. Finally we add the densely connected layers. Note that because we are facing a two-class classification problem, i.e. a *binary classification problem*, we will end our network with a [*s... | model = tf.keras.models.Sequential([
# Note the input shape is the desired size of the image 300x300 with 3 bytes color
# This is the first convolution
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
# The second convolution
tf... | _____no_output_____ | MIT | Course2Part4Lesson4.ipynb | MBadriNarayanan/TensorFlow |
Adversarial Examples Let's start out by importing all the required libraries | import os
import sys
sys.path.append(os.path.join(os.getcwd(), "venv"))
import numpy as np
import torch
import torchvision.transforms as transforms
from matplotlib import pyplot as plt
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision.datasets import MN... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
MNIST Pytorch expects `Dataset` objects as input. Luckily, for MNIST (and few other datasets such as CIFAR and SVHN), torchvision has a ready made function to convert the dataset to a pytorch `Dataset` object. Keep in mind that these functions return `PIL` images so you will have to apply a transformation on them. | path = os.path.join(os.getcwd(), "MNIST")
transform = transforms.Compose([transforms.ToTensor()])
train_mnist = MNIST(path, train=True, transform=transform)
test_mnist = MNIST(path, train=False, transform=transform) | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Visualize Dataset Set `batch_size` to 1 to visualize the dataset. | batch_size = 1
train_set = DataLoader(train_mnist, batch_size=batch_size, shuffle=True)
test_set = DataLoader(test_mnist, batch_size=batch_size, shuffle=True)
num_images = 2
for i, (image, label) in enumerate(train_set):
if i == num_images:
break
#Pytorch returns batch_size x num_channels x 28 x 28
... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Train a Model Set `batch_size` to start training a model on the dataset. | batch_size = 64
train_set = DataLoader(train_mnist, batch_size=batch_size, shuffle=True) | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Define a `SimpleCNN` model to train on MNIST | def identity():
return lambda x: x
class CustomConv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
activation, stride):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size-2)
self.activation = a... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Create 4 model variations: identity_model: SimpleCNN model with identity activation functions relu_model: SimpleCNN model with relu activation functions sig_model: SimpleCNN model with sigmoid activation functions tanh_model: SimpleCNN model with tanh activation functions | identity_model = SimpleCNN()
relu_model = SimpleCNN(activation=nn.ReLU())
sig_model = SimpleCNN(activation=nn.Sigmoid())
tanh_model = SimpleCNN(activation=nn.Tanh()) | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Create a function to train the model | def train_model(model, train_set, num_epochs):
optimizer = torch.optim.Adam(lr=0.001, params=model.parameters())
for epoch in range(num_epochs):
epoch_accuracy, epoch_loss = 0, 0
train_set_size = 0
for images, labels in train_set:
batch_size = images.size(0)
image... | epoch 1: loss= 1.86, accuracy= 35.7
epoch 2: loss= 1.49, accuracy= 49.69
epoch 3: loss= 1.44, accuracy= 51.29
epoch 4: loss= 1.4, accuracy= 52.41
epoch 5: loss= 1.37, accuracy= 53.35
epoch 6: loss= 1.34, accuracy= 54.23
epoch 7: loss= 1.32, accuracy= 54.97
epoch 8: loss= 1.3, accuracy= 55.83
epoch 9: loss= 1.29, accura... | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Generating Adversarial Examples Now that we have a trained model, we can generate adversarial examples. Gradient Ascent Use Gradient Ascent to generate a targeted adversarial example. | def np_val(torch_var):
return torch_var.data.numpy()[0]
class AttackNet(nn.Module):
def __init__(self, model, image_size):
super().__init__()
self.model = model
self.params = nn.Parameter(torch.zeros(image_size), requires_grad=True)
def forward(self, image):
# clamp parame... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Define a `GradientAscent` object | gradient_ascent = GradientAscent(trained_model) | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Define a function to help plot the results | %matplotlib inline
def plot_results(image, perturbation, orig_pred, orig_con, adv_pred, adv_con):
plot_image = image.numpy()[0][0]
plot_perturbation = perturbation.data.numpy()[0][0]
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 10
fig_size[1] = 5
plt.rcParams["figure.figsize"] =... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Let's generate some adversarial examples! | num_images = 2
for i, (test_image, test_label) in enumerate(test_set):
if i == num_images:
break
target_classes = list(range(10))
target_classes.remove(test_label.numpy()[0])
target = np.random.choice(target_classes)
perturbation, (orig_con, orig_pred), (
adv_con, adv_pred) = gradie... | Adversarial attack succeeded after 3 steps!
| MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Fast Gradient Now let's use the Fast Gradient Sign Method to generate untargeted adversarial examples. | class FastGradient(object):
def __init__(self, model, confidence=0, alpha=0.1):
super().__init__()
self.model = model
self.confidence = confidence
self.alpha = alpha
def attack(self, image, label):
image_var = Variable(image, requires_grad=True)
target = Variable... | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Define a `FastGradient` object | fast_gradient = FastGradient(trained_model) | _____no_output_____ | MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Let's generate some adversarial examples! | num_images = 20
for i, (test_image, test_label) in enumerate(test_set):
if i == num_images:
break
perturbation, (orig_con, orig_pred), (
adv_con, adv_pred) = fast_gradient.attack(test_image, test_label)
if perturbation is not None:
plot_results(test_image, perturbation, orig_pred, o... | Adversarial attack failed
| MIT | Adversarial Examples.ipynb | wgharbieh/Adversarial-Examples |
Introduccion a la Inteligencia Artificial Veremos dos ejercicios con para entender el concepto de inteligencia artificial Objeto Rebotador En el siguiente ejercicio, realizaremos un objeto que al chocar con una de las paredes, este cambie de direccion y siga con su camino | !pip3 install ColabTurtle | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
Llamamos las librerias | import ColabTurtle.Turtle as robot
import random | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
Ahora el codigo principal. De momento el robot rebota y vuelve en la misma dirección.Lo que tienes que hacer es poner un inicio aleatorio y modificar el `if` dentro del lazo `while` de manera que este cambie en un solo eje. | robot.initializeTurtle(initial_speed=1)
pad = 15
max_w = robot.window_width() - pad
max_h = robot.window_height() - pad
robot.shape("circle")
robot.color("green")
robot.penup()
robot.goto(0 + pad, 200)
robot.dx = 10 # Velociad en x
robot.dy = 10 # Velociad en y
reflections = 0
# El numero de reflexiones puede se... | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
ChatBot Para el siguiente ejercicio, utilizaremos un ChatBot con definido por reglas. (Ejemplo tomado de: https://www.analyticsvidhya.com/blog/2021/07/build-a-simple-chatbot-using-python-and-nltk/) | !pip install nltk
import nltk
from nltk.chat.util import Chat, reflections | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
**Chat**: es la clase que contiene toda lo logica para processar el texto que el chatbot recibe y encontrar informacion util.**reflections**: es un diccionario que contiene entradas basica y sus correspondientes salidas. | print(reflections) | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
Comenzemos contruyendo las reglas. Las siguientes lineas generan un conjunto de reglas simples. | pairs = [
[
r"my name is (.*)",
["Hello %1, How are you today ?",]
],
[
r"hi|hey|hello",
["Hello", "Hey there",]
],
[
r"what is your name ?",
["I am a bot created by Analytics Vidhya. you can call me crazy!",]
],
[
r"how are you ?",
... | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
Después de definir las reglas, definimos la función para ejecutar el proceso de chat. | def chat(this_creator='Nelson Yalta'):
print(f"Hola!!! Yo soy un chatbot creado por {this_creator}, y estoy listo para sus servicios. Recuede que hablo inglés.")
chat = Chat(pairs, reflections)
chat.converse()
chat() | _____no_output_____ | MIT | day_1/Actividad_02.ipynb | Fhrozen/2021_clubes_ciencia_speech |
Pi Estimation Using Monte Carlo In this exercise, we will use MapReduce and a Monte-Carlo-Simulation to estimate $\Pi$.If we are looking at this image from this [blog](https://towardsdatascience.com/how-to-make-pi-part-1-d0b41a03111f), we see a unit circle in a unit square::
def mapper(self, _, line):
for x in range(100):
x = uniform(-1,1)
y = uniform(-1,1)
in_circle = x*x + y*y <=1
yield None, in_c... | _____no_output_____ | MIT | V3/v3_exercises_material/solutions/4_Pi-Estimation/Pi_Solution.ipynb | fred1234/BDLC_FS22 |
Another ApproachComputing the mean in the mapper | %%writefile pi.py
#!/usr/bin/python3
from mrjob.job import MRJob
from random import uniform
class MyJob(MRJob):
def mapper(self, _, line):
num_samples = 100
in_circles_list = []
for x in range(num_samples):
x = uniform(-1,1)
y = uniform(-1,1)
... | _____no_output_____ | MIT | V3/v3_exercises_material/solutions/4_Pi-Estimation/Pi_Solution.ipynb | fred1234/BDLC_FS22 |
Running the JobUnfortunately, the library does not work without an input file. I guess this comes from the fact that the hadoop streaming library also does not support this feature, see [stack overflow](https://stackoverflow.com/questions/22821005/hadoop-streaming-job-with-no-input-file).We fake the number of mappers ... | !python pi.py /data/dataset/text/small.txt
!python pi.py /data/dataset/text/holmes.txt | _____no_output_____ | MIT | V3/v3_exercises_material/solutions/4_Pi-Estimation/Pi_Solution.ipynb | fred1234/BDLC_FS22 |
Download and prepare the MS-COCO datasetYou will use the [MS-COCO dataset](http://cocodataset.org/home) to train our model. The dataset contains over 82,000 images, each of which has at least 5 different caption annotations. The code below downloads and extracts the dataset automatically.**Caution: large download ahea... | # Download caption annotation files
annotation_folder = '/annotations/'
if not os.path.exists(os.path.abspath('.') + annotation_folder):
annotation_zip = tf.keras.utils.get_file('captions.zip',
cache_subdir=os.path.abspath('.'),
origi... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Preprocess the images using InceptionV3Next, you will use InceptionV3 (which is pretrained on Imagenet) to classify each image. You will extract features from the last convolutional layer.First, you will convert the images into InceptionV3's expected format by:* Resizing the image to 299px by 299px* [Preprocess the im... | def load_image(image_path):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, (299, 299))
img = tf.keras.applications.inception_v3.preprocess_input(img)
return img, image_path | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Initialize InceptionV3 and load the pretrained Imagenet weightsNow you'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. The shape of the output of this layer is ```8x8x2048```. You use the last convolutional layer because you are using attention in this... | image_model = tf.keras.applications.InceptionV3(include_top=False,
weights='imagenet')
new_input = image_model.input
hidden_layer = image_model.layers[-1].output
image_features_extract_model = tf.keras.Model(new_input, hidden_layer) | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Caching the features extracted from InceptionV3You will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but also memory intensive, requiring 8 \* 8 \* 2048 floats per image. At the time of writing, this exceeds the memory limitations of Colab (currently 1... | # Get unique images
encode_train = sorted(set(img_name_vector))
# Feel free to change batch_size according to your system configuration
image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)
image_dataset = image_dataset.map(
load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(16)
for img,... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Preprocess and tokenize the captions* First, you'll tokenize the captions (for example, by splitting on spaces). This gives us a vocabulary of all of the unique words in the data (for example, "surfing", "football", and so on).* Next, you'll limit the vocabulary size to the top 5,000 words (to save memory). You'll re... | # Find the maximum length of any caption in our dataset
def calc_max_length(tensor):
return max(len(t) for t in tensor)
# Choose the top 5000 words from the vocabulary
top_k = 5000
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k,
oov_token="<unk>",... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Split the data into training and testing | # Create training and validation sets using an 80-20 split
img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector,
cap_vector,
test_size=0.2,
... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Create a tf.data dataset for training | # Feel free to change these parameters according to your system's configuration
BATCH_SIZE = 64
BUFFER_SIZE = 1000
embedding_dim = 256
units = 512
vocab_size = top_k + 1
num_steps = len(img_name_train) // BATCH_SIZE
# Shape of the vector extracted from InceptionV3 is (64, 2048)
# These two variables represent that vec... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
ModelFun fact: the decoder below is identical to the one in the example for [Neural Machine Translation with Attention](../sequences/nmt_with_attention.ipynb).The model architecture is inspired by the [Show, Attend and Tell](https://arxiv.org/pdf/1502.03044.pdf) paper.* In this example, you extract the features from t... | class BahdanauAttention(tf.keras.Model):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self, features, hidden):
# features(CNN_encoder output) shape ==... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Checkpoint | checkpoint_path = "/gdrive/My Drive/checkpoints/train"
if not os.path.exists(checkpoint_path):
os.mkdir(checkpoint_path)
ckpt = tf.train.Checkpoint(encoder=encoder,
decoder=decoder,
optimizer = optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Training* You extract the features stored in the respective `.npy` files and then pass those features through the encoder.* The encoder output, hidden state(initialized to 0) and the decoder input (which is the start token) is passed to the decoder.* The decoder returns the predictions and the decoder hidden state.* T... | # adding this in a separate cell because if you run the training cell
# many times, the loss_plot array will be reset
loss_plot = []
@tf.function
def train_step(img_tensor, target):
loss = 0
# initializing the hidden state for each batch
# because the captions are not related from image to image
hidden = decod... | _____no_output_____ | Apache-2.0 | train_captioning_model.ipynb | sidphbot/visual-to-audio-aid-for-visually-impaired |
Predict stateHere is the current implementation of the `predict_state` function. It takes in a state (a Python list), and then separates those into position and velocity to calculate a new, predicted state. It uses a constant velocity motion model.**In this exercise, we'll be improving this function, and using matrix ... | # The current predict state function
# Predicts the next state based on a motion model
def predict_state(state, dt):
# Assumes a valid state had been passed in
x = state[0]
velocity = state[1]
# Assumes a constant velocity model
new_x = x + velocity*dt
# Create and return the new, pred... | _____no_output_____ | MIT | Object Tracking and Localization/Representing State and Motion/Matrix Multiplication/Matrix_Multiplication.ipynb | brand909/Computer-Vision |
Matrix operationsYou've been given a matrix class that can create new matrices and performs one operation: multiplication. In our directory this is called `matrix.py`.Similar to the Car class, we can use this to initialize matrix objects. | # import the matrix file
import matrix
# Initialize a state vector
initial_position = 0 # meters
velocity = 50 # m/s
# Notice the syntax for creating a state column vector ([ [x], [v] ])
# Commas separate these items into rows and brackets into columns
initial_state = matrix.Matrix([ [initial_position],
... | _____no_output_____ | MIT | Object Tracking and Localization/Representing State and Motion/Matrix Multiplication/Matrix_Multiplication.ipynb | brand909/Computer-Vision |
Transformation matrixNext, define the state transformation matrix and print it out! | # Define the state transformation matrix
dt = 1
tx_matrix = matrix.Matrix([ [1, dt],
[0, 1] ])
print(tx_matrix) | [[1 1 ]
[0 1 ]
]
| MIT | Object Tracking and Localization/Representing State and Motion/Matrix Multiplication/Matrix_Multiplication.ipynb | brand909/Computer-Vision |
TODO: Modify the predict state function to use matrix multiplicationNow that you know how to create matrices, modify the `predict_state` function to work with them!Note: you can multiply a matrix A by a matrix B by writing `A*B` and it will return a new matrix. | # The current predict state function
def predict_state_mtx(state, dt):
## TODO: Assume that the state passed in is a Matrix object
## Using a constant velocity model and a transformation matrix
## Create and return the new, predicted state!
tx_matrix = matrix.Matrix([ [1, dt],
... | _____no_output_____ | MIT | Object Tracking and Localization/Representing State and Motion/Matrix Multiplication/Matrix_Multiplication.ipynb | brand909/Computer-Vision |
Test cellHere is an initial state vector and dt to test your function with! | # initial state variables
initial_position = 10 # meters
velocity = 30 # m/s
# Initial state vector
initial_state = matrix.Matrix([ [initial_position],
[velocity] ])
print('The initial state is: ' + str(initial_state))
# after 2 seconds make a prediction using the new function
stat... | State after 3 more seconds is: [[160.0 ]
[30.0 ]
]
Final state after 3 more seconds is: [[250.0 ]
[30.0 ]
]
| MIT | Object Tracking and Localization/Representing State and Motion/Matrix Multiplication/Matrix_Multiplication.ipynb | brand909/Computer-Vision |
Copyright 2021 The TensorFlow Authors. | #@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... | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Simple TFX Pipeline Tutorial using Penguin dataset***A Short tutorial to run a simple TFX pipeline.*** Note: We recommend running this tutorial in a Colab notebook, with no setup required! Just click "Run in Google Colab".View on TensorFlow.orgRun in Google ColabView source on GitHubDownload notebook In this notebook... | try:
import colab
!pip install --upgrade pip
except:
pass | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Install TFX | !pip install -U tfx | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Did you restart the runtime?If you are using Google Colab, the first time that you runthe cell above, you must restart the runtime by clickingabove "RESTART RUNTIME" button or using "Runtime > Restartruntime ..." menu. This is because of the way that Colabloads packages. Check the TensorFlow and TFX versions. | import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__)) | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Set up variablesThere are some variables used to define a pipeline. You can customize thesevariables as you want. By default all output from the pipeline will begenerated under the current directory. | import os
PIPELINE_NAME = "penguin-simple"
# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where c... | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Prepare example dataWe will download the example dataset for use in our TFX pipeline. The dataset weare using is[Palmer Penguins dataset](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)which is also used in other[TFX examples](https://github.com/tensorflow/tfx/tree/master/tfx/examples/penguin).There... | import urllib.request
import tempfile
DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_... | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Take a quick look at the CSV file. | !head {_data_filepath} | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
You should be able to see five values. `species` is one of 0, 1 or 2, and allother features should have values between 0 and 1. Create a pipelineTFX pipelines are defined using Python APIs. We will define a pipeline whichconsists of following three components.- CsvExampleGen: Reads in data files and convert them to TF... | _trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_meta... | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Now you have completed all preparation steps to build a TFX pipeline. Write a pipeline definitionWe define a function to create a TFX pipeline. A `Pipeline` objectrepresents a TFX pipeline which can be run using one of pipelineorchestration systems that TFX supports. | def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
module_file: str, serving_model_dir: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a three component penguin pipeline with TFX."""
# Brings data into the pipeline.
example_gen = tfx.co... | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Run the pipelineTFX supports multiple orchestrators to run pipelines.In this tutorial we will use `LocalDagRunner` which is included in the TFXPython package and runs pipelines on local environment.We often call TFX pipelines "DAGs" which stands for directed acyclic graph.`LocalDagRunner` provides fast iterations for ... | tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
module_file=_trainer_module_file,
serving_model_dir=SERVING_MODEL_DIR,
metadata_path=METADATA_PATH)) | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
You should see "INFO:absl:Component Pusher is finished." at the end of thelogs if the pipeline finished successfully. Because `Pusher` component is thelast component of the pipeline.The pusher component pushes the trained model to the `SERVING_MODEL_DIR` whichis the `serving_model/penguin-simple` directory if you did n... | # List files in created model directory.
!find {SERVING_MODEL_DIR} | _____no_output_____ | Apache-2.0 | docs/tutorials/tfx/penguin_simple.ipynb | suryaavala/tfx |
Using a Different Corpuszh_segment makes it easy to use a different corpus for word segmentation.If you simply want to "teach" the algorithm a single phrase it doesn't know then read [this StackOverflow answer](http://stackoverflow.com/questions/20695825/english-word-segmentation-in-nlp).Now, let's get a new corpus. F... | import requests
response = requests.get('https://www.gutenberg.org/ebooks/1342.txt.utf-8')
text = response.text
print len(text) | 717573
| Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
Great. We've got a new corpus for `zh_segment`. Now let's look at what parts of the API we need to change. There's one function and two dictionaries: `zh_segment.clean`, `zh_segment.bigram_counts` and `zh_segment.unigram_counts`. We'll work on these in reverse. | import zh_segment
print type(zh_segment.unigram_counts), type(zh_segment.bigram_counts)
print zh_segment.unigram_counts.items()[:3]
print zh_segment.bigram_counts.items()[:3] | [('biennials', 37548.0), ('verplank', 48349.0), ('tsukino', 19771.0)]
[('personal effects', 151369.0), ('basic training', 294085.0), ('it absolutely', 130505.0)]
| Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
Ok, so `zh_segment.unigram_counts` is just a dictionary mapping unigrams to their counts. Let's write a method to tokenize our text. | import re
def tokenize(text):
pattern = re.compile('[a-zA-Z]+')
return (match.group(0) for match in pattern.finditer(text))
print list(tokenize("Wait, what did you say?")) | ['Wait', 'what', 'did', 'you', 'say']
| Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
Now we'll build our dictionaries. | from collections import Counter
zh_segment.unigram_counts = Counter(tokenize(text))
def pairs(iterable):
iterator = iter(iterable)
values = [next(iterator)]
for value in iterator:
values.append(value)
yield ' '.join(values)
del values[0]
zh_segment.bigram_counts = Counter(pairs(to... | _____no_output_____ | Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
That's it.Now, by default, `zh_segment.segment` lowercases all input and removes punctuation. In our corpus we have capitals so we'll also have to change the `clean` function. Our heaviest hammer is to simply replace it with the identity function. This will do no sanitation of the input to `segment`. | def identity(value):
return value
zh_segment.clean = identity
zh_segment.segment('wantofawife') | _____no_output_____ | Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
If you find this behaves poorly then you may need to change the `zh_segment.TOTAL` variable to reflect the total of all unigrams. In our case that's simply: | zh_segment.TOTAL = float(sum(zh_segment.unigram_counts.values())) | _____no_output_____ | Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment |
zh_segment doesn't require any fancy machine learning training algorithms. Simply update the unigram and bigram count dictionaries and you're ready to go. | _____no_output_____ | Apache-2.0 | docs/using-a-different-corpus.ipynb | wuhaifengdhu/zh_segment | |
Checking Container Dwell TimesThis works with the CSV export of ConFlowGen. Import libraries | import os
import pathlib
import ipywidgets as widgets
import pandas as pd
from IPython.display import Markdown
import matplotlib.pyplot as plt
from matplotlib import gridspec | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Select input data | folder_of_this_jupyter_notebook = pathlib.Path.cwd()
export_folder = os.path.join(
folder_of_this_jupyter_notebook,
os.pardir,
os.pardir,
os.pardir,
"conflowgen",
"data",
"exports"
)
folders = [
folder
for folder in os.listdir(export_folder)
if os.path.isdir(
os.path.join... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Load containers | path_to_containers = os.path.join(
path_to_selected_exported_content,
"containers.csv"
)
print(f"Opening {path_to_containers}")
df_containers = pd.read_csv(path_to_containers, index_col="id", dtype={
"delivered_by_truck": "Int64",
"picked_up_by_truck": "Int64",
"delivered_by_large_scheduled_vehicle"... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Check number of large scheduled vehicles (deep sea vessels, feeders, barges, and trains). | df_containers.groupby(by="delivered_by_large_scheduled_vehicle").count() | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Load scheduled vehiclesLoad the vehicles to enrich the information regarding the arrival and departure of the containers. | path_to_deep_sea_vessels = os.path.join(
path_to_selected_exported_content,
"deep_sea_vessels.csv"
)
path_to_feeders = os.path.join(
path_to_selected_exported_content,
"feeders.csv"
)
path_to_barges = os.path.join(
path_to_selected_exported_content,
"barges.csv"
)
path_to_trains = os.path.joi... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Plot arrival pattern. | plt.figure(figsize=(15, 3))
x, y, z = [], [], []
y_axis = []
y_scaling_factor = 2
for i, (name, df) in enumerate(scheduled_vehicle_dfs.items()):
y_axis.append((i/y_scaling_factor, name))
if len(df) == 0:
continue
arrivals_and_capacity = df[["scheduled_arrival", "moved_capacity"]]
for _, row i... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Transform data to check how many containers are delivered and picked up by which vehicle. | vehicle_to_teu_to_deliver = {}
vehicle_to_teu_to_pickup = {}
for i, container in df_containers.iterrows():
teu = container["length"] / 20
assert 1 <= teu <= 2.5
if container["delivered_by"] != "truck":
vehicle = container["delivered_by_large_scheduled_vehicle"]
if vehicle not in vehicle_to... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Add transformed data to vehicles. | s_delivery = pd.Series(vehicle_to_teu_to_deliver)
s_pickup = pd.Series(vehicle_to_teu_to_pickup)
df_large_scheduled_vehicle["capacity_delivery"] = s_delivery
df_large_scheduled_vehicle["capacity_pickup"] = s_pickup
df_large_scheduled_vehicle
for large_scheduled_vehicle_id in df_large_scheduled_vehicle.index:
delive... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
Load trucks | path_to_trucks = os.path.join(
path_to_selected_exported_content,
"trucks.csv"
)
assert os.path.isfile(path_to_trucks)
df_truck = pd.read_csv(
path_to_trucks, index_col=0,
parse_dates=[
# Pickup
"planned_container_pickup_time_prior_berthing",
"realized_container_pickup_time",
... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
This is the probability of the truck to show up at any given hour of the week (by index). | delivered_and_picked_up_by_large_vessels_df = df_containers.loc[
~pd.isna(df_containers["picked_up_by_large_scheduled_vehicle"])
].join(
df_large_scheduled_vehicle, on="picked_up_by_large_scheduled_vehicle", rsuffix="_picked_up"
).loc[
~pd.isna(df_containers["delivered_by_large_scheduled_vehicle"])
].join(
... | _____no_output_____ | MIT | example-usage/Jupyter Notebook/output data inspection/checking container dwell times.ipynb | 1grasse/conflowgen |
2019 Formula One World Championship A Formula One season consists of a series of races, known as Grands Prix (French for ''grand prizes' or 'great prizes''), which take place worldwide on purpose-built circuits and on public roads. The results of each race are evaluated using a points system to determine two annu... | #A Probability Distribution; an {outcome: probability} mapping.
# Make probabilities sum to 1.0; assert no negative probabilities
class ProbDist(dict):
"""A Probability Distribution; an {outcome: probability} mapping."""
def __init__(self, mapping=(), **kwargs):
self.update(mapping, **kwargs)
... | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
Question Set 1what is the Probability Distribution for each F1 driver to win the Singaporean Grand Prix? What is the Probability Distribution for each F1 driver to win both the Singaporean and Russian Grand Prix?What is the probability for Mercedes to win both races?What is the probability for Mercedes to win at least... | SGP = ProbDist(LH=284,VB=221,CL=182,MV=185,SV=169,PG=65,CS=58,AA=34,DR=34,DK=33,NH=31,LN=25,KR=31,SP=27,LS=19,KM=18,RG=8,AG=3,RK=1,
GR=0)
print ("The probability of each driver winnning Singaporean Grand Prix ")
SGP #Driver standing divided by / total of all driver standings, SGP returns total probability as 1 | The probability of each driver winnning Singaporean Grand Prix
| MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
2. What is the Probability Distribution for each F1 driver to win both the Singaporean and Russian Grand Prix? | SGP = ProbDist(
LH=284,VB=221,CL=182,MV=185,SV=169,PG=65,CS=58,AA=34,DR=34,DK=33,NH=31,LN=25,KR=31,SP=27,LS=19,KM=18,
RG=8,AG=3,RK=1,GR=0) # data taken on saturday before race starts for Singapore
RGP = ProbDist(
LH=296,VB=231,CL=200,MV=200,SV=194,PG=69,CS=58,AA=42,DR=34,DK=33,NH=33,LN=31,KR=31,SP=27,LS=1... | The probability of each driver winnning Singaporean Grand Prix and Russian Grand Prix
| MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
3. What is the probability for Mercedes to win both races? | def mercedes_T(outcome): return outcome == "VB" or outcome == "LH"
mercedesWinningSGPRace = p(mercedes_T, SGP)
#calculate probability of mercedes winning Singapore Frand Pix
def mercedes_T(outcome): return outcome == "VB" or outcome == "LH"
mercedesWinningRGPRace = p(mercedes_T, RGP)
#calculate probability of mercedes ... | The probability of mercedes winnning both the races
| MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
4. What is the probability for Mercedes to win at least one race? | def p(event, space):
"""The probability of an event, given a sample space of outcomes.
event: a collection of outcomes, or a predicate that is true of outcomes in the event.
space: a set of outcomes or a probability distribution of {outcome: frequency} pairs."""
# if event is a predicate it, "un... | The probability of mercedes winnning at least one of the races
| MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
Question Set 2 If Mercedes wins the first race, what is the probability that Mercedes wins the next one? If Mercedes wins at least one of these two races, what is the probability Mercedes wins both races? How about Ferrari, Red Bull, and Renault? Solution If Mercedes wins the first race, what is the probability that... | SGP = ProbDist(
LH=284,VB=221,CL=182,MV=185,SV=169,PG=65,CS=58,AA=34,DR=34,DK=33,NH=31,LN=25,KR=31,SP=27,LS=19,KM=18,
RG=8,AG=3,RK=1,GR=0)
RGP = ProbDist(
LH=296,VB=231,CL=200,MV=200,SV=194,PG=69,CS=58,AA=42,DR=34,DK=33,NH=33,LN=31,KR=31,SP=27,LS=19,KM=18,
RG=8,AG=4,RK=1,GR=0)
Weather = ProbDist(RA=1, ... | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
If Ferrari wins the first race, what is the probability that Ferrari wins the next one? | def Ferrari_WinBoth(outcome): return 'CL CL' in outcome or 'CL SV' in outcome or 'SV SV' in outcome or 'SV CL' in outcome
def Ferrari_Win(outcome): return 'CL' in outcome or 'SV' in outcome
p(Ferrari_WinBoth, such_that(Ferrari_Win,bothSGPRGP)) | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
If RedBull wins the first race, what is the probability that RedBull wins the next one | def RedBull_WinBoth(outcome): return 'MV MV' in outcome or 'MV AA' in outcome or 'AA AA' in outcome or 'AA MV' in outcome
def RedBull_Win(outcome): return 'MV' in outcome or 'AA' in outcome
p(RedBull_WinBoth, such_that(RedBull_Win,bothSGPRGP)) | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
If Renault wins the first race, what is the probability that Renault wins the next one? | def Renault_WinBoth(outcome): return 'DR DR' in outcome or 'DR NH' in outcome or 'NH NH' in outcome or 'NH DR' in outcome
def Renault_Win(outcome): return 'DR' in outcome or 'NH' in outcome
p(Renault_WinBoth, such_that(Renault_Win,bothSGPRGP)) | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
Question Set 3Mercedes wins one of these two races on a rainy day. What is the probability Mercedes wins both races, assuming races can be held on either rainy, sunny, cloudy, snowy or foggy days? Assume that rain, sun, clouds, snow, and fog are the only possible weather conditions on race tracks. Solution Mercedes ... | #create Probability Distribution for given Weather Condtions wher p(weather) will be 0.20
GivenFiveWeatherConditons = ProbDist(
RainyDay=1,
SunnyDay=1,
CloudyDay=1,
SnowyDay=1,
FoggyDay=1
)
GivenFiveWeatherConditons
#perfoms joint probabilities on SGP & weather and RGP & weather probability distributions Respectively
d... | _____no_output_____ | MIT | Formula One World Championship Analysis.ipynb | mbadamve/Machine-Learning-projects |
import pandas as pd
from typing import List
from datetime import datetime
from random import randint
from dateutil.relativedelta import *
factionName: List[str] = []
systemName: List[str] = []
population: List[int] = []
influence: List[float] = []
updated: List[datetime] = []
factionName: List[str]... | _____no_output_____ | BSD-3-Clause | notebooks/ANN.ipynb | HausReport/ClubRaiders | |
WELCOME BACK TO SACNAS' PYTHON WORKSHOP hosted by Luis and Emily Introduction:Sign in and grab food! Import the necessary libraries/packages:There are tools you can download into your code to implement. Think of these like your toolbox, to create plots, analyze data, and endless possibilities | import pandas as pd
import numpy as np
# visualization libraries
import matplotlib.pyplot as plt
| _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus |
Import data and assign it to a variable called tips | url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv'
| _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus |
.describe()Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
For loops a for-loop is a control flow statement for specifying iteration, which allows code to be executed repeatedly. | x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
| _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus |
Working with our datasetRecap on worksheet. What day are people more likely to tip? | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
How can we automate a print statement using a For loop? | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
How do we store these values? | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
Lists, Arrays , Dictionaries | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
Plotting a bar graphparameters are what you need inside the paranthesis for the function you are calling to work. plt.bar(x coordinate, height) | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
Who tips more? Smokers or non smokers? | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
Who tips more? Women or Men | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus | |
Plot the total_bill column using a histogram | _____no_output_____ | MIT | Copy_of_Workshop2Final.ipynb | feliz10/papus |
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