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Lab 1: Markov Decision Processes - Problem 3 Lab InstructionsAll your answers should be written in this notebook. You shouldn't need to write or modify any other files.**You should execute every block of code to not miss any dependency.***This project was developed by Peter Chen, Rocky Duan, Pieter Abbeel for the Ber... | import numpy as np, numpy.random as nr, gym
import matplotlib.pyplot as plt
%matplotlib inline
np.set_printoptions(precision=3) | _____no_output_____ | MIT | Lab 1/ Problem 3 genouadr.ipynb | AdrianGScorp/deeprlbootcamp |
Problem 3: Sampling-based Tabular Q-LearningSo far we have implemented Value Iteration and Policy Iteration, both of which require access to an MDP's dynamics model. This requirement can sometimes be restrictive - for example, if the environment is given as a blackbox physics simulator, then we won't be able to read o... | from crawler_env import CrawlingRobotEnv
env = CrawlingRobotEnv()
print("We can inspect the observation space and action space of this Gym Environment")
print("-----------------------------------------------------------------------------")
print("Action space:", env.action_space)
print("It's a discrete space with %i ... | _____no_output_____ | MIT | Lab 1/ Problem 3 genouadr.ipynb | AdrianGScorp/deeprlbootcamp |
You will see the random controller can sometimes make progress but it won't get very far. Let's implement Tabular Q-Learning with $\epsilon$-greedy exploration to find a better policy piece by piece. | from collections import defaultdict
import random
# dictionary that maps from state, s, to a numpy array of Q values [Q(s, a_1), Q(s, a_2) ... Q(s, a_n)]
# and everything is initialized to 0.
q_vals = defaultdict(lambda: np.array([0. for _ in range(env.action_space.n)]))
print("Q-values for state (0, 0): %s" % q_va... | Test1 passed
Test2 passed
| MIT | Lab 1/ Problem 3 genouadr.ipynb | AdrianGScorp/deeprlbootcamp |
Next we will implement Q learning update. After we observe a transition $s, a, s', r$,$$\textrm{target}(s') = R(s,a,s') + \gamma \max_{a'} Q_{\theta_k}(s',a')$$$$Q_{k+1}(s,a) \leftarrow (1-\alpha) Q_k(s,a) + \alpha \left[ \textrm{target}(s') \right]$$ | def q_learning_update(gamma, alpha, q_vals, cur_state, action, next_state, reward):
"""
Inputs:
gamma: discount factor
alpha: learning rate
q_vals: q value table
cur_state: current state
action: action taken in current state
next_state: next state results from tak... | Itr 0 # Average speed: 0.05
Itr 50000 # Average speed: 2.03
Itr 100000 # Average speed: 3.37
Itr 150000 # Average speed: 3.37
Itr 200000 # Average speed: 3.37
Itr 250000 # Average speed: 3.37
| MIT | Lab 1/ Problem 3 genouadr.ipynb | AdrianGScorp/deeprlbootcamp |
After the learning is successful, we can visualize the learned robot controller. Remember we learn this just from interacting with the environment instead of peeking into the dynamics model! | env = CrawlingRobotEnv(render=True, horizon=500)
prev_state = env.reset()
ret = 0.
done = False
while not done:
action = np.argmax(q_vals[prev_state])
state, reward, done, info = env.step(action)
ret += reward
prev_state = state
# you can close the visualization GUI with the following method
env.close_... | _____no_output_____ | MIT | Lab 1/ Problem 3 genouadr.ipynb | AdrianGScorp/deeprlbootcamp |
Exercise 6-3 LSTMThe following two cells will create a LSTM cell with one neuron.We scale the output of the LSTM linear and add a bias.Then the output will be wrapped by a sigmoid activation.The goal is to predict a time series where every $n^{th}$ ($5^{th}$ in the current example) element is 1 and all others are 0.a)... | import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
tf.reset_default_graph()
tf.set_random_seed(12314)
epochs=50
zero_steps = 5
learning_rate = 0.01
lstm_neurons = 1
out_dim = 1
num_features = 1
batch_size = zero_steps
window_size = zero_steps*2
time_steps = 5
x = tf.placeholder(tf.float3... | loss 0.00001
input:
[0 0 0 0 1 0 0 0 0 1]
forget 0.0135
input gate 0.9997
cell cands 0.9994
out gate sig 1.0000
out gate tanh 0.7586
hidden state 0.7586
cell state 0.9928
pred 0.0000
input:
[0 0 0 1 0 0 0 0 1 0]
forget 0.8747
input gate 0.9860
cell cands -0.0476
out gate sig 1.0000
out gate tanh 0.6759
hidden st... | MIT | week6/lstm.ipynb | changkun/ws-18-19-deep-learning-tutorial |
2020L-WUM Praca domowa 2 Kod: **Bartłomiej Eljasiak** Załadowanie bibliotek Z tych bibliotek będziemy korzystać w wielu miejscach, jednak w niektórych fragmentach kodu znajdą się dodatkowe importowania, lecz w takich sytuacjach użytek załadowanej biblioteki jest ograniczony do 'chunku', w którym została załadowana. | import pandas as pd
import seaborn as sns
import numpy as np
import sklearn | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Wczytanie danych | # local version
_data=pd.read_csv('allegro-api-transactions.csv')
#online version
#_data = pd.read_csv('https://www.dropbox.com/s/360xhh2d9lnaek3/allegro-api-transactions.csv?dl=1')
cdata=_data.copy() | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Przyjrzenie się danym | cdata.head() | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Obróbka danych | len(cdata.it_location.unique())
cdata.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 420020 entries, 0 to 420019
Data columns (total 14 columns):
lp 420020 non-null int64
date 420020 non-null object
item_id 420020 non-null int64
categories 420020 non-null object
pay_option_on_d... | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Na pierwszy rzut oka nie mamy żadnych braków w danych, co znacznie ułatwia nam prace. Kodowanie zmiennych kategorycznych Wiemy, że naszym targetem będzie `price`, chcemy więc w tym akapicie zamienić wszystkie zmienne kategoryczne, z których w dalszej części kodu będziemy korzystać, na zmienne liczbowe. Skuteczna zam... | import category_encoders
y=cdata.price
te = category_encoders.target_encoder.TargetEncoder(cdata.it_location, smoothing=100)
encoded = te.fit_transform(cdata.it_location,y)
encoded | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Różne rodzaje zakodowania kolumny `main_category` One-hot Coding | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# integer encode
le = LabelEncoder()
integer_encoded = le.fit_transform(cdata.main_category)
print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(categories='auto',sparse=False)
integer_encoded = integer_en... | [12 18 6 ... 18 5 15]
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
| Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Zamieniliśmy w ten sposób kolumnę kategoryczną na 26 kolumn o wartościach 0 lub 1. Nie jest to złe rozwiązanie, jednak stanowczo zwiększa rozmiar naszej ramki i wydłża czas uczenia się modelu. Prawdopodobnie może istnieć lepsze rozwiązanie. Helmert CodingDocumentation: [scikit-learn](http://contrib.scikit-learn.org/ca... | # Pobieramy go z category_encoders
helmert_encoder = category_encoders.HelmertEncoder()
helmert_encoded = helmert_encoder.fit_transform(cdata.main_category)
#showing only first 5 encoded rows
print(helmert_encoded.loc[1:5,:].transpose()) | 1 2 3 4 5
intercept 1.0 1.0 1.0 1.0 1.0
main_category_0 1.0 0.0 0.0 1.0 1.0
main_category_1 -1.0 2.0 0.0 -1.0 -1.0
main_category_2 -1.0 -1.0 3.0 -1.0 -1.0
main_category_3 -1.0 -1.0 -1.0 -1.0 -1.0
main_category_4 -1.0 -1.0 -1.0 -1.0 -1.0
main_category_5 -1.0 -1.0... | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Backward Difference CodingDocumentation: [scikit-learn](http://contrib.scikit-learn.org/categorical-encoding/backward_difference.htmlbackward-difference-coding) | # Pobieramy go z category_encoders
back_diff_encoder = category_encoders.BackwardDifferenceEncoder()
back_diff_encoded = back_diff_encoder.fit_transform(cdata.main_category)
#showing only first 5 encoded rows
print(back_diff_encoded.loc[1:5,:].transpose()) | 1 2 3 4 5
intercept 1.000000 1.000000 1.000000 1.000000 1.000000
main_category_0 0.037037 0.037037 0.037037 0.037037 0.037037
main_category_1 -0.925926 0.074074 0.074074 -0.925926 -0.925926
main_category_2 -0.888889 -0.888889 0.111111 -0.88... | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Uzupełnianie braków Wybranie danych z ramkiDalej będziemy pracowac tylko na 3 kolumnach danych, ograniczę więc je dla przejżystości poczynań. | data_selected= cdata.loc[:,['price','it_seller_rating','it_quantity']]
data_selected.head() | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Usunięcie danych z kolumny Dane z kolumny będziemy usuwać funkcją `df.column.sample(frac)` gdzie `frac` będzie oznaczać procent danych, które chcemy zatrzymać. Gwarantuje nam to w miarę losowe usunięcie danych, które powinno być wystarczające do dalszych działań. | cdata.price.sample(frac=0.9)
| _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Ocena skutecznosci imputacjiDo oceny skuteczności podanych algorytmów imputacji danych musimy przyjąć jakis sposób liczenia ich. Zgodnie z sugestią prowadzącej skorszystam z [RMSE](https://en.wikipedia.org/wiki/Root_mean_square) czyli root mean square error. Nazwa powinna przybliżyć sposób, którm RMSE jest wyznaczane,... | from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.metrics import mean_squared_error
def test_imputation(imputer,iterations=10):
_resoults=[]
# we always use the same data, so it's taken globally
for i in range(iterations):
test_data ... | _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
I to niech będzie przykład działania takiej funkcji | imputer = IterativeImputer(max_iter=10,random_state=0)
RMSE_list = test_imputation(imputer,20)
print("Średnie RMSE wynosi", round(np.mean(RMSE_list)))
print('Odchylenie standardowe RMSE wynosi: ', round(np.std(RMSE_list)))
RMSE_list | Średnie RMSE wynosi 6659.0
Odchylenie standardowe RMSE wynosi: 64.0
| Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Odchylenie standardowe jest dosyć małe, więc metodę imputacji uważam za skuteczną. Chętnym polecam przetestowanie tej funkcji dla innych typów imputacji oraz zmiennej liczbie iteracji. Usuwanie danych z wielu kolumn Powtórzmy uprzedni przykład dodając drobną modyfikacje. Tym razem będziemy usuwali dane zarówno z `it_... | def test_imputation2(imputer,iterations=10):
_resoults=[]
# we always use the same data, so it's taken globally
for i in range(iterations):
test_data = data_selected.copy()
test_data.it_seller_rating = test_data.it_seller_rating.sample(frac = 0.9)
test_data.it_quantity = test_data.i... | Średnie RMSE wynosi 7953.0
Odchylenie standardowe RMSE wynosi: 60.0
| Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Tak jak moglibyśmy się spodziewać, średni błąd jest większy w przypadku gdy usuneliśmy więcej danych. Ponownie zachęcam do powtórzenia obliczeń i sprawdzenia wyników. Spojrzenie na imputacje typu `IterativeImputer` Wykorzystałem pewien szczególny sposób imputacji danych tzn `IterativeImputer`, o którym nie wspomniałe... | upper_iter_limit = 30
lower_iter_limit = 5
imputation_iterations = 10
mean_RMSE ={
"single": [],
"multi": [],
}
for imputer_iterations in range(lower_iter_limit,upper_iter_limit,2):
_resoults_single = []
_resoults_multi = []
imputer = IterativeImputer(max_iter=imputer_iterations,random_state=0)
... | max_iter: 5 / 30
max_iter: 7 / 30
max_iter: 9 / 30
max_iter: 11 / 30
max_iter: 13 / 30
max_iter: 15 / 30
max_iter: 17 / 30
max_iter: 19 / 30
max_iter: 21 / 30
max_iter: 23 / 30
max_iter: 25 / 30
max_iter: 27 / 30
max_iter: 29 / 30
| Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Przyjrzyjmy się wynikom. | mean_RMSE
| _____no_output_____ | Apache-2.0 | Prace_domowe/Praca_domowa2/Grupa1/EljasiakBartlomiej/main.ipynb | niladrem/2020L-WUM |
Classifying Fashion-MNISTNow it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 97% ... | import torch
from torchvision import datasets, transforms
import helper
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# Download and load the training data
trainset = datasets.Fa... | _____no_output_____ | MIT | intro-to-pytorch/Part 4 - Fashion-MNIST (Exercises).ipynb | GeoffKriston/deep-learning-v2-pytorch |
Here we can see one of the images. | image, label = next(iter(trainloader))
helper.imshow(image[0,:]); | _____no_output_____ | MIT | intro-to-pytorch/Part 4 - Fashion-MNIST (Exercises).ipynb | GeoffKriston/deep-learning-v2-pytorch |
Building the networkHere you should define your network. As with MNIST, each image is 28x28 which is a total of 784 pixels, and there are 10 classes. You should include at least one hidden layer. We suggest you use ReLU activations for the layers and to return the logits or log-softmax from the forward pass. It's up t... | # TODO: Define your network architecture here
from torch import nn
n_hidden1 = 1024
n_hidden2 = 128
n_hidden3 = 64
model = nn.Sequential(nn.Linear(784,n_hidden1),
nn.ReLU(),
nn.Linear(n_hidden1,n_hidden2),
nn.ReLU(),
nn.Linear(n_hid... | _____no_output_____ | MIT | intro-to-pytorch/Part 4 - Fashion-MNIST (Exercises).ipynb | GeoffKriston/deep-learning-v2-pytorch |
Train the networkNow you should create your network and train it. First you'll want to define [the criterion](http://pytorch.org/docs/master/nn.htmlloss-functions) ( something like `nn.CrossEntropyLoss`) and [the optimizer](http://pytorch.org/docs/master/optim.html) (typically `optim.SGD` or `optim.Adam`).Then write t... | # TODO: Create the network, define the criterion and optimizer
from torch import optim
criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(),lr=0.007)
# TODO: Train the network here
data = iter(trainloader)
epochs = 10
for e in range(epochs):
running_loss = 0
for images,labels in trainloader:
... | _____no_output_____ | MIT | intro-to-pytorch/Part 4 - Fashion-MNIST (Exercises).ipynb | GeoffKriston/deep-learning-v2-pytorch |
differential_privacy.analysis.rdp_accountant | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | _____no_output_____ | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
dp query | # Copyright 2018, The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | _____no_output_____ | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
gausian query | # Copyright 2018, The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... |
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
| Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
our_dp_optimizer | # Copyright 2018, The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | _____no_output_____ | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
gan.ops | """
Most codes from https://github.com/carpedm20/DCGAN-tensorflow
"""
import math
import numpy as np
import tensorflow as tf
if "concat_v2" in dir(tf):
def concat(tensors, axis, *args, **kwargs):
return tf.concat_v2(tensors, axis, *args, **kwargs)
else:
def concat(tensors, axis, *args, **kwargs):
... | _____no_output_____ | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
OUR DP CGAN | # -*- coding: utf-8 -*-
from __future__ import division
from keras.datasets import cifar10
from mlxtend.data import loadlocal_mnist
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRe... | Using TensorFlow backend.
| Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
gan.utils | """
Most codes from https://github.com/carpedm20/DCGAN-tensorflow
"""
from __future__ import division
import scipy.misc
import numpy as np
from six.moves import xrange
import matplotlib.pyplot as plt
import os, gzip
import tensorflow as tf
import tensorflow.contrib.slim as slim
from keras.datasets import cifar1... | _____no_output_____ | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
Main | import tensorflow as tf
import os
base_dir = "./"
out_dir = base_dir + "mnist_clip1_sigma0.6_lr0.55"
if not os.path.exists(out_dir):
os.mkdir(out_dir)
gpu_options = tf.GPUOptions(visible_device_list="0")
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_o... | Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and ... | Apache-2.0 | Advanced_DP_CGAN/AdvancedDPCGAN.ipynb | reihaneh-torkzadehmahani/DP-CGAN |
URL: http://matplotlib.org/examples/pylab_examples/quiver_demo.htmlMost examples work across multiple plotting backends, this example is also available for:* [Matplotlib - quiver_demo](../matplotlib/quiver_demo.ipynb) | import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('bokeh') | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/quiver_demo.ipynb | ppwadhwa/holoviews |
Define data | xs, ys = np.arange(0, 2 * np.pi, .2), np.arange(0, 2 * np.pi, .2)
X, Y = np.meshgrid(xs, ys)
U = np.cos(X)
V = np.sin(Y)
# Convert to magnitude and angle
mag = np.sqrt(U**2 + V**2)
angle = (np.pi/2.) - np.arctan2(U/mag, V/mag) | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/quiver_demo.ipynb | ppwadhwa/holoviews |
Plot | label = 'Arrows scale with plot width, not view'
opts.defaults(opts.VectorField(height=400, width=500))
vectorfield = hv.VectorField((xs, ys, angle, mag))
vectorfield.relabel(label)
label = "pivot='mid'; every third arrow"
vf_mid = hv.VectorField((xs[::3], ys[::3], angle[::3, ::3], mag[::3, ::3], ))
points = hv.Poin... | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/quiver_demo.ipynb | ppwadhwa/holoviews |
**PINN eikonal solver for a portion of the Marmousi model** | from google.colab import drive
drive.mount('/content/gdrive')
cd "/content/gdrive/My Drive/Colab Notebooks/Codes/PINN_isotropic_eikonal_R1"
!pip install sciann==0.5.4.0
!pip install tensorflow==2.2.0
#!pip install keras==2.3.1
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.axe... | 0.005852164264049101
0.19346281698941364
| MIT | codes/script5.ipynb | umairbinwaheed/PINN_eikonal |
GRIP June'21 - The Sparks Foundation Data Science and Business Analytics Author: Smriti Gupta Task 1: **Prediction using Supervised ML** * Predict the percentage of an student based on the no. of study hours. * What will be predicted score if a student studies for 9.25 hrs/ day? * _LANGUAGE:_ Python* _DATASET:_ http:... | # Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config Completer.use_jedi = False
# Reading data from remote link
url = "https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv"
df =... | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Data Visualization | # 2-D graph to establish relationship between the Data and checking for linearity
sns.set_style('darkgrid')
df.plot(x='Hours', y='Scores', style='o')
plt.title('Hours vs Percentage')
plt.xlabel('Hours Studied')
plt.ylabel('Percentage Score')
plt.show() | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Data Preprocessing | X = df.iloc[:, :-1].values
y = df.iloc[:, 1].values | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
LINEAR REGRESSION MODEL Splitting Dataset into training and test sets: | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Training the Model | from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
print('TRAINING COMPLETED.') | TRAINING COMPLETED.
| MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Predicting the Score | y_predict = regressor.predict(X_test)
prediction = pd.DataFrame({'Hours': [i[0] for i in X_test], 'Predicted Scores': [k for k in y_predict]})
prediction
print(regressor.intercept_)
print(regressor.coef_)
# Plotting the regression line
line = regressor.coef_*X+regressor.intercept_
# Plotting for the test data
plt.sc... | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Checking the Accuracy Scores for training and test set | print('Test Score')
print(regressor.score(X_test, y_test))
print('Training Score')
print(regressor.score(X_train, y_train)) | Test Score
0.9480612939203932
Training Score
0.953103139564599
| MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Comparing Actual Scores and Predicted Scores | data= pd.DataFrame({'Actual': y_test,'Predicted': y_predict})
data
# Visualization comparing Actual Scores and Predicted Scores
plt.scatter(X_test, y_test, color = colors[1])
plt.plot(X_test, y_predict, color = colors[0])
plt.title("Hours Studied Vs Percentage (Test Dataset)")
plt.xlabel("Hour")
plt.ylabel... | _____no_output_____ | MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
Model Evaluation Metrics | #Checking the efficiency of model
from sklearn import metrics
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_predict))
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_predict))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_predict))) | Mean Absolute Error: 4.451593449522768
Mean Squared Error: 25.188194900366135
Root Mean Squared Error: 5.018784205399365
| MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
What will be predicted score if a student studies for 9.25 hrs/ day? | hours = 9.25
ans = regressor.predict([[hours]])
print("No of Hours = {}".format(hours))
print("Predicted Score = {}".format(ans[0])) | No of Hours = 9.25
Predicted Score = 93.71476919815156
| MIT | Task 1- Prediction using Supervised ML/Linear Regression.ipynb | smritig19/GRIP_Internship_Tasks |
import pandas as pd
import time
import os.path
import warnings
warnings.filterwarnings('ignore')
# install DenMune clustering algorithm using pip command from the offecial Python repository, PyPi
# from https://pypi.org/project/denmune/
!pip install denmune
# now import it
from denmune import DenMune
# clone datasets... | Plotting train data
| BSD-3-Clause | kaggle/detecting-non-groundtruth-datasets.ipynb | fossabot/denmune-clustering-algorithm | |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
!git clone https://github.com/yohanesnuwara/ccs-gundih | Cloning into 'ccs-gundih'...
remote: Enumerating objects: 94, done.[K
remote: Counting objects: 1% (1/94)[K
remote: Counting objects: 2% (2/94)[K
remote: Counting objects: 3% (3/94)[K
remote: Counting objects: 4% (4/94)[K
remote: Counting objects: 5% (5/94)[K
remote: Counting objects: 6% (6/94)[K
rem... | MIT | main/00_gundih_historical_production_data.ipynb | yohanesnuwara/bsc-thesis-carbon-capture-storage | |
Visualize the Historical Production Data | # Read simulation result
col = np.array(['Date', 'Days', 'WSAT', 'OSAT', 'GSAT', 'GMT', 'OMR', 'GMR', 'GCDI', 'GCDM', 'WCD',
'WGR', 'WCT', 'VPR', 'VPT', 'VIR', 'VIT', 'WPR', 'OPR', 'GPR', 'WPT', 'OPT', 'GPT',
'PR', 'GIR', 'GIT', 'GOR'])
case1 = pd.read_excel(r'/content/ccs-gundih/data/CASE... | _____no_output_____ | MIT | main/00_gundih_historical_production_data.ipynb | yohanesnuwara/bsc-thesis-carbon-capture-storage |
Dry-Gas Reservoir Analysis | !git clone https://github.com/yohanesnuwara/reservoir-engineering
# calculate gas z factor and FVF
import os, sys
sys.path.append('/content/reservoir-engineering/Unit 2 Review of Rock and Fluid Properties/functions')
from pseudoprops import pseudoprops
from dranchuk_aboukassem import dranchuk
from gasfvf import gasfv... | _____no_output_____ | MIT | main/00_gundih_historical_production_data.ipynb | yohanesnuwara/bsc-thesis-carbon-capture-storage |
1A.1 - Deviner un nombre aléatoire (correction)On reprend la fonction introduite dans l'énoncé et qui permet de saisir un nombre. | import random
nombre = input("Entrez un nombre")
nombre | _____no_output_____ | MIT | _doc/notebooks/td1a/pp_exo_deviner_un_nombre_correction.ipynb | Jerome-maker/ensae_teaching_cs |
**Q1 :** Ecrire une jeu dans lequel python choisi aléatoirement un nombre entre 0 et 100, et essayer de trouver ce nombre en 10 étapes. | n = random.randint(0,100)
appreciation = "?"
while True:
var = input("Entrez un nombre")
var = int(var)
if var < n :
appreciation = "trop bas"
print(var, appreciation)
else :
appreciation = "trop haut"
print(var, appreciation)
if var == n:
appreciat... | Entrez un nombre7
7 trop bas
Entrez un nombre10
10 trop bas
Entrez un nombre100
100 trop haut
Entrez un nombre50
50 trop bas
Entrez un nombre75
75 trop haut
Entrez un nombre60
60 trop haut
Entrez un nombre55
55 trop haut
Entrez un nombre53
53 trop haut
Entrez un nombre52
52 trop haut
Entrez un nombre51
51 trop haut
51 ... | MIT | _doc/notebooks/td1a/pp_exo_deviner_un_nombre_correction.ipynb | Jerome-maker/ensae_teaching_cs |
**Q2 :** Transformer ce jeu en une fonction ``jeu(nVies)`` où ``nVies`` est le nombre d'itérations maximum. | import random
n = random.randint(0,100)
vies = 10
appreciation = "?"
while vies > 0:
var = input("Entrez un nombre")
var = int(var)
if var < n :
appreciation = "trop bas"
print(vies, var, appreciation)
else :
appreciation = "trop haut"
print(vies, var, appreciation)
... | Entrez un nombre50
10 50 trop bas
Entrez un nombre75
9 75 trop haut
Entrez un nombre60
8 60 trop bas
Entrez un nombre65
7 65 trop bas
Entrez un nombre70
6 70 trop bas
Entrez un nombre73
5 73 trop haut
Entrez un nombre72
4 72 trop haut
Entrez un nombre71
3 71 trop haut
3 71 bravo !
| MIT | _doc/notebooks/td1a/pp_exo_deviner_un_nombre_correction.ipynb | Jerome-maker/ensae_teaching_cs |
**Q3 :** Adapter le code pour faire une classe joueur avec une méthode jouer, où un joueur est défini par un pseudo et son nombre de vies. Faire jouer deux joueurs et déterminer le vainqueur. | class joueur:
def __init__(self, vies, pseudo):
self.vies = vies
self.pseudo = pseudo
def jouer(self):
appreciation = "?"
n = random.randint(0,100)
while self.vies > 0:
message = appreciation + " -- " + self.pseudo + " : " + str(self.vies) + " vies restan... | ? -- joueur 1 : 10 vies restantes. Nombre choisi : 50
3 50 trop haut
trop haut -- joueur 1 : 9 vies restantes. Nombre choisi : 25
3 25 trop haut
trop haut -- joueur 1 : 8 vies restantes. Nombre choisi : 10
3 10 trop haut
trop haut -- joueur 1 : 7 vies restantes. Nombre choisi : 5
3 5 trop bas
trop bas -- joueur 1 : 6 v... | MIT | _doc/notebooks/td1a/pp_exo_deviner_un_nombre_correction.ipynb | Jerome-maker/ensae_teaching_cs |
Run sims to understand sensitivity of 'contact_tracing_constant' | ctc_range = [0.1 * x for x in range(11)]
dfs_ctc_pre_reopen = run_sensitivity_sims(pre_reopen_params, param_to_vary='contact_tracing_constant',
param_values = ctc_range, trajectories_per_config=250, time_horizon=100)
dfs_ctc_post_reopen = run_sensitivity_sims(reopen_params, param_to... | _____no_output_____ | MIT | boqn/group_testing/notebooks/.ipynb_checkpoints/cornell_reopen_analysis_sensitivity_contact_tracing_effectiveness-checkpoint.ipynb | RaulAstudillo06/BOQN |
Artificial Intelligence Nanodegree Machine Translation ProjectIn this notebook, sections that end with **'(IMPLEMENTATION)'** in the header indicate that the following blocks of code will require additional functionality which you must provide. Please be sure to read the instructions carefully! IntroductionIn this not... | %load_ext autoreload
%aimport helper, tests
%autoreload 1
import collections
import helper
import numpy as np
import project_tests as tests
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.layers import GRU, Input, Dense, T... | Using TensorFlow backend.
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Verify access to the GPUThe following test applies only if you expect to be using a GPU, e.g., while running in a Udacity Workspace or using an AWS instance with GPU support. Run the next cell, and verify that the device_type is "GPU".- If the device is not GPU & you are running from a Udacity Workspace, then save you... | from tensorflow.python.client import device_lib
print(device_lib.list_local_devices()) | [name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15440441665772238793
, name: "/gpu:0"
device_type: "GPU"
memory_limit: 357433344
locality {
bus_id: 1
}
incarnation: 3061658136091710780
physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:04.0"
]
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
DatasetWe begin by investigating the dataset that will be used to train and evaluate your pipeline. The most common datasets used for machine translation are from [WMT](http://www.statmt.org/). However, that will take a long time to train a neural network on. We'll be using a dataset we created for this project tha... | # Load English data
english_sentences = helper.load_data('data/small_vocab_en')
# Load French data
french_sentences = helper.load_data('data/small_vocab_fr')
print('Dataset Loaded') | Dataset Loaded
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
FilesEach line in `small_vocab_en` contains an English sentence with the respective translation in each line of `small_vocab_fr`. View the first two lines from each file. | for sample_i in range(2):
print('small_vocab_en Line {}: {}'.format(sample_i + 1, english_sentences[sample_i]))
print('small_vocab_fr Line {}: {}'.format(sample_i + 1, french_sentences[sample_i])) | small_vocab_en Line 1: new jersey is sometimes quiet during autumn , and it is snowy in april .
small_vocab_fr Line 1: new jersey est parfois calme pendant l' automne , et il est neigeux en avril .
small_vocab_en Line 2: the united states is usually chilly during july , and it is usually freezing in november .
small... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
From looking at the sentences, you can see they have been preprocessed already. The puncuations have been delimited using spaces. All the text have been converted to lowercase. This should save you some time, but the text requires more preprocessing. VocabularyThe complexity of the problem is determined by the comple... | english_words_counter = collections.Counter([word for sentence in english_sentences for word in sentence.split()])
french_words_counter = collections.Counter([word for sentence in french_sentences for word in sentence.split()])
print('{} English words.'.format(len([word for sentence in english_sentences for word in se... | 1823250 English words.
227 unique English words.
10 Most common words in the English dataset:
"is" "," "." "in" "it" "during" "the" "but" "and" "sometimes"
1961295 French words.
355 unique French words.
10 Most common words in the French dataset:
"est" "." "," "en" "il" "les" "mais" "et" "la" "parfois"
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
For comparison, _Alice's Adventures in Wonderland_ contains 2,766 unique words of a total of 15,500 words. PreprocessFor this project, you won't use text data as input to your model. Instead, you'll convert the text into sequences of integers using the following preprocess methods:1. Tokenize the words into ids2. Add p... | def tokenize(x):
"""
Tokenize x
:param x: List of sentences/strings to be tokenized
:return: Tuple of (tokenized x data, tokenizer used to tokenize x)
"""
# TODO: Implement
tokenizer = Tokenizer()
tokenizer.fit_on_texts(x)
return tokenizer.texts_to_sequences(x), tokenizer
tests.... | {'the': 1, 'quick': 2, 'a': 3, 'brown': 4, 'fox': 5, 'jumps': 6, 'over': 7, 'lazy': 8, 'dog': 9, 'by': 10, 'jove': 11, 'my': 12, 'study': 13, 'of': 14, 'lexicography': 15, 'won': 16, 'prize': 17, 'this': 18, 'is': 19, 'short': 20, 'sentence': 21}
Sequence 1 in x
Input: The quick brown fox jumps over the lazy dog .
... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Padding (IMPLEMENTATION)When batching the sequence of word ids together, each sequence needs to be the same length. Since sentences are dynamic in length, we can add padding to the end of the sequences to make them the same length.Make sure all the English sequences have the same length and all the French sequences h... | def pad(x, length=None):
"""
Pad x
:param x: List of sequences.
:param length: Length to pad the sequence to. If None, use length of longest sequence in x.
:return: Padded numpy array of sequences
"""
# TODO: Implement
if length is None:
length = max([len(sentence) for sentence ... | Sequence 1 in x
Input: [1 2 4 5 6 7 1 8 9]
Output: [1 2 4 5 6 7 1 8 9 0]
Sequence 2 in x
Input: [10 11 12 2 13 14 15 16 3 17]
Output: [10 11 12 2 13 14 15 16 3 17]
Sequence 3 in x
Input: [18 19 3 20 21]
Output: [18 19 3 20 21 0 0 0 0 0]
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Preprocess PipelineYour focus for this project is to build neural network architecture, so we won't ask you to create a preprocess pipeline. Instead, we've provided you with the implementation of the `preprocess` function. | def preprocess(x, y):
"""
Preprocess x and y
:param x: Feature List of sentences
:param y: Label List of sentences
:return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer)
"""
preprocess_x, x_tk = tokenize(x)
preprocess_y, y_tk = tokenize(y)
preprocess_x = pad(pr... | Data Preprocessed
Max English sentence length: 15
Max French sentence length: 21
English vocabulary size: 199
French vocabulary size: 344
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
ModelsIn this section, you will experiment with various neural network architectures.You will begin by training four relatively simple architectures.- Model 1 is a simple RNN- Model 2 is a RNN with Embedding- Model 3 is a Bidirectional RNN- Model 4 is an optional Encoder-Decoder RNNAfter experimenting with the four si... | def logits_to_text(logits, tokenizer):
"""
Turn logits from a neural network into text using the tokenizer
:param logits: Logits from a neural network
:param tokenizer: Keras Tokenizer fit on the labels
:return: String that represents the text of the logits
"""
index_to_words = {id: word for... | `logits_to_text` function loaded.
| MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Model 1: RNN (IMPLEMENTATION)A basic RNN model is a good baseline for sequence data. In this model, you'll build a RNN that translates English to French. | def simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
"""
Build and train a basic RNN on x and y
:param input_shape: Tuple of input shape
:param output_sequence_length: Length of output sequence
:param english_vocab_size: Number of unique English words in the ... | Train on 110288 samples, validate on 27573 samples
Epoch 1/10
110288/110288 [==============================] - 12s 109us/step - loss: 2.5907 - acc: 0.4812 - val_loss: nan - val_acc: 0.5582
Epoch 2/10
110288/110288 [==============================] - 10s 91us/step - loss: 1.6567 - acc: 0.5862 - val_loss: nan - val_acc: 0... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Model 2: Embedding (IMPLEMENTATION)You've turned the words into ids, but there's a better representation of a word. This is called word embeddings. An embedding is a vector representation of the word that is close to similar words in n-dimensional space, where the n represents the size of... | def embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
"""
Build and train a RNN model using word embedding on x and y
:param input_shape: Tuple of input shape
:param output_sequence_length: Length of output sequence
:param english_vocab_size: Number of unique E... | Train on 110288 samples, validate on 27573 samples
Epoch 1/10
110288/110288 [==============================] - 12s 104us/step - loss: 3.3620 - acc: 0.4088 - val_loss: nan - val_acc: 0.4603
Epoch 2/10
110288/110288 [==============================] - 11s 102us/step - loss: 2.5504 - acc: 0.4842 - val_loss: nan - val_acc: ... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Model 3: Bidirectional RNNs (IMPLEMENTATION)One restriction of a RNN is that it can't see the future input, only the past. This is where bidirectional recurrent neural networks come in. They are able to see the future data. | def bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
"""
Build and train a bidirectional RNN model on x and y
:param input_shape: Tuple of input shape
:param output_sequence_length: Length of output sequence
:param english_vocab_size: Number of unique English wor... | Train on 110288 samples, validate on 27573 samples
Epoch 1/10
110288/110288 [==============================] - 12s 106us/step - loss: 3.3402 - acc: 0.4192 - val_loss: nan - val_acc: 0.4769
Epoch 2/10
110288/110288 [==============================] - 11s 101us/step - loss: 2.4795 - acc: 0.4908 - val_loss: nan - val_acc: ... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Model 4: Encoder-Decoder (OPTIONAL)Time to look at encoder-decoder models. This model is made up of an encoder and decoder. The encoder creates a matrix representation of the sentence. The decoder takes this matrix as input and predicts the translation as output.Create an encoder-decoder model in the cell below. | def encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
"""
Build and train an encoder-decoder model on x and y
:param input_shape: Tuple of input shape
:param output_sequence_length: Length of output sequence
:param english_vocab_size: Number of unique English ... | Train on 110288 samples, validate on 27573 samples
Epoch 1/10
110288/110288 [==============================] - 12s 106us/step - loss: 3.3761 - acc: 0.4076 - val_loss: nan - val_acc: 0.4515
Epoch 2/10
110288/110288 [==============================] - 11s 100us/step - loss: 2.5353 - acc: 0.4798 - val_loss: nan - val_acc: ... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Model 5: Custom (IMPLEMENTATION)Use everything you learned from the previous models to create a model that incorporates embedding and a bidirectional rnn into one model. | def model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
"""
Build and train a model that incorporates embedding, encoder-decoder, and bidirectional RNN on x and y
:param input_shape: Tuple of input shape
:param output_sequence_length: Length of output sequence
:p... | Final Model Loaded
Train on 110288 samples, validate on 27573 samples
Epoch 1/10
110288/110288 [==============================] - 91s 824us/step - loss: 2.4410 - acc: 0.4865 - val_loss: nan - val_acc: 0.5841
Epoch 2/10
110288/110288 [==============================] - 89s 807us/step - loss: 1.4055 - acc: 0.6253 - val_lo... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Prediction (IMPLEMENTATION) | def final_predictions(x, y, x_tk, y_tk):
"""
Gets predictions using the final model
:param x: Preprocessed English data
:param y: Preprocessed French data
:param x_tk: English tokenizer
:param y_tk: French tokenizer
"""
# TODO: Train neural network using model_final
model = model_fin... | Train on 110288 samples, validate on 27573 samples
Epoch 1/12
110288/110288 [==============================] - 81s 733us/step - loss: 2.3220 - acc: 0.5011 - val_loss: nan - val_acc: 0.5999
Epoch 2/12
110288/110288 [==============================] - 79s 713us/step - loss: 1.3477 - acc: 0.6375 - val_loss: nan - val_acc: ... | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
SubmissionWhen you're ready to submit, complete the following steps:1. Review the [rubric](https://review.udacity.com/!/rubrics/1004/view) to ensure your submission meets all requirements to pass2. Generate an HTML version of this notebook - Run the next cell to attempt automatic generation (this is the recommended m... | # Save before you run this cell!
!!jupyter nbconvert *.ipynb | _____no_output_____ | MIT | machine_translation.ipynb | jay-thakur/machine_translation |
Unit 5 - Financial Planning | # Initial imports
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
from MCForecastTools import MCSimulation
# date here
from datetime import date
%matplotlib inline
# Load .env enviroment variables
load_dotenv() | _____no_output_____ | Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Part 1 - Personal Finance Planner Collect Crypto Prices Using the `requests` Library | # Set current amount of crypto assets
# YOUR CODE HERE!
my_btc = 1.2
my_eth = 5.3
# Crypto API URLs
btc_url = "https://api.alternative.me/v2/ticker/Bitcoin/?convert=CAD"
eth_url = "https://api.alternative.me/v2/ticker/Ethereum/?convert=CAD"
# response_data = requests.get(create_deck_url).json()
# response_data
btc_re... | The current value of your 1.2 BTC is $60097.20
The current value of your 5.3 ETH is $11897.07
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Collect Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds) | # Current amount of shares
# Create two variables named my_agg and my_spy and set them equal to 200 and 50, respectively.
# YOUR CODE HERE!
my_agg = 200
my_spy = 50
# Set Alpaca API key and secret
# YOUR CODE HERE!
alpaca_api_key = os.getenv("ALPACA_API_KEY")
alpaca_secret_key = os.getenv("ALPACA_SECRET_KEY")
# Create... | The current value of your 50 SPY shares is $20865.50
The current value of your 200 AGG shares is $22908.00
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Savings Health Analysis | # Set monthly household income
# YOUR CODE HERE!
monthly_income = 12000
# Create savings DataFrame
# YOUR CODE HERE!
df_savings = pd.DataFrame([my_btc_value+ my_eth_value, my_spy_value + my_agg_value], columns = ['amount'], index = ['crypto', 'shares'])
# Display savings DataFrame
display(df_savings)
# Plot savings p... | Congratulations! You have enough money in your emergency fund.
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Part 2 - Retirement Planning Monte Carlo Simulation Hassan's Note for some reason AlPaca would not let me get more than 1000 records. To get 5 years data (252 * 5), I had to break it up into two reads and concat the data. | # Set start and end dates of five years back from today.
# Sample results may vary from the solution based on the time frame chosen
start_date1 = pd.Timestamp('2015-08-07', tz='America/New_York').isoformat()
end_date1 = pd.Timestamp('2017-08-07', tz='America/New_York').isoformat()
start_date2 = pd.Timestamp('2017-08-08... | _____no_output_____ | Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Retirement Analysis | # delete
# Fetch summary statistics from the Monte Carlo simulation results
# YOUR CODE HERE!
# Print summary statistics
# YOUR CODE HERE!
# Fetch summary statistics from the Monte Carlo simulation results
# YOUR CODE HERE!
tbl = MC_even_dist.summarize_cumulative_return()
# Print summary statistics
print (tbl) | count 500.000000
mean 9.683146
std 6.514698
min 1.320640
25% 5.124632
50% 8.047245
75% 12.513634
max 51.017155
95% CI Lower 2.421887
95% CI Upper 25.440141
Name: 7560, dtype: float64
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Calculate the expected portfolio return at the 95% lower and upper confidence intervals based on a `$20,000` initial investment. | # delete
# # Set initial investment
# initial_investment = 20000
# # Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $20,000
# # YOUR CODE HERE!
# # Print results
# print(f"There is a 95% chance that an initial investment of ${initial_investment} in the portfo... | There is a 95% chance that an initial investment of $20000 in the portfolio over the next 30 years will end within in the range of $48437.75 and $508802.81
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Calculate the expected portfolio return at the `95%` lower and upper confidence intervals based on a `50%` increase in the initial investment. | # delete
# # Set initial investment
# initial_investment = 20000 * 1.5
# # Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $30,000
# # YOUR CODE HERE!
# # Print results
# print(f"There is a 95% chance that an initial investment of ${initial_investment} in the ... | There is a 95% chance that an initial investment of $30000.0 in the portfolio over the next 30 years will end within in the range of $72656.62 and $763204.22
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Optional Challenge - Early Retirement Five Years Retirement Option | # Configuring a Monte Carlo simulation to forecast 5 years cumulative returns
# YOUR CODE HERE!
MC_even_dist = MCSimulation(
portfolio_data = df_stock_data,
weights = [.4, .6],
num_simulation = 500,
num_trading_days = 252*5
)
# Running a Monte Carlo simulation to forecast 5 years cumulative returns
# YO... | There is a 95% chance that an initial investment of $60000 in the portfolio over the next 5 years will end within in the range of $51278.36 and $141959.07
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
Ten Years Retirement Option | # Configuring a Monte Carlo simulation to forecast 10 years cumulative returns
# YOUR CODE HERE!
MC_even_dist = MCSimulation(
portfolio_data = df_stock_data,
weights = [.4, .6],
num_simulation = 500,
num_trading_days = 252*10
)
# Running a Monte Carlo simulation to forecast 10 years cumulative returns
#... | There is a 95% chance that an initial investment of $60000 in the portfolio over the next 10 years will end within in the range of $55174.9 and $241011.77
| Apache-2.0 | Scratch Folder/financial-planner-v4.ipynb | HassanAlam55/ColumbiaFintechHW5API |
!pip install h5py pyyaml
import tensorflow as tf
from tensorflow import keras
import urllib.request
print('Beginning file download with urllib2...')
url = 'https://drive.google.com/file/d/1Z0dlwhtS03lYhHv-f3ZT7nf_PgbIXBMB/view?usp=sharing'
urllib.request.urlretrieve(url, 'basic_CNN.h5')
basic_CNN = keras.models.load... | _____no_output_____ | Apache-2.0 | mBSUS_XRay_Test_Program.ipynb | rjthompson22/bMSUS_XRay_Model | |
Abstract Factory Design Pattern >An abstract factory is a generative design pattern that allows you to create families of related objects without getting attached to specific classes of created objects. The pattern is being implemented by creating an abstract class (for example - Factory), which is represented as an i... | class AbstractFactory:
def create_chair(self):
raise NotImplementedError()
def create_sofa(self):
raise NotImplementedError()
def create_table(self):
raise NotImplementedError()
class Chair:
def __init__(self, name):
self._name = name
def __... | victorian chair
victorian sofa
victorian table
modern chair
modern sofa
modern table
futuristic chair
futuristic sofa
futuristic table
| MIT | Other/Creational Design Pattern - Abstract Factory.ipynb | deepaksood619/Python-Competitive-Programming |
Example - https://py.checkio.org/mission/army-units/solve/ | class Army:
def train_swordsman(self, name):
return NotImplementedError()
def train_lancer(self, name):
return NotImplementedError()
def train_archer(self, name):
return NotImplementedError()
class Swordsman:
def __init__(self, soldier_type, name, army_type):
... | Knight Jaks, European swordsman
Knight Jaks, European swordsman
Raubritter Harold, European lancer
Ranger Robin, European archer
Coding complete? Let's try tests!
| MIT | Other/Creational Design Pattern - Abstract Factory.ipynb | deepaksood619/Python-Competitive-Programming |
Cosine prediction Data preparationIn this notebook, we will use fost to predict a cosine curve. Let's import `pandas` and `numpy` first. | import pandas as pd
import numpy as np | _____no_output_____ | MIT | examples/1. Cosine prediction.ipynb | meng-zha/FOST |
Next, generate cosine data. | train_df = pd.DataFrame()
#we don't have actual timestamp in this case, use a default one
train_df['Date'] = [pd.datetime(year=2000,month=1,day=1)+pd.Timedelta(days=i) for i in range(2000)]
train_df['TARGET'] = [np.cos(i/2) for i in range(2000)]
#there is not a 'Node' concept in this dataset, thus all the Node are 0
t... | _____no_output_____ | MIT | examples/1. Cosine prediction.ipynb | meng-zha/FOST |
FOST only supports file path as input, save the data to `train.csv` | train_df.to_csv('train.csv',index=False) | _____no_output_____ | MIT | examples/1. Cosine prediction.ipynb | meng-zha/FOST |
Load fost to predict future | import fostool
from fostool.pipeline import Pipeline
#predict future 10 steps
lookahead = 10
fost = Pipeline(lookahead=lookahead, train_path='train.csv')
#fit in one line
fost.fit()
#get predict result
res = fost.predict()
#plot prediction
fost.plot(res, lookback_size=lookahead) | _____no_output_____ | MIT | examples/1. Cosine prediction.ipynb | meng-zha/FOST |
Pytorch Basic | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from IPython.display import clear_output
torch.cuda.is_available() | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Device | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Hyper Parameter | input_size = 784
hidden_size = 500
num_class = 10
epochs = 5
batch_size = 100
lr = 0.001 | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Load MNIST Dataset | train_dataset = torchvision.datasets.MNIST(root='../data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='../data',
... | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Simple Model | class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_class):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size,hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_class)
def forward(self, x):
out = self.... | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Loss and Optimizer | criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr) | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Train | total_step = len(train_loader)
for epoch in range(epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1,28*28).to(device)
labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_g... | EPOCH [5/5] STEP [600/600] Loss 0.0343)
| MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Test | with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predic... | Accuracy of the network on the 10000 test images: 97.85 %
| MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
save | torch.save(model.state_dict(), 'model.ckpt') | _____no_output_____ | MIT | my_notebook/1_basic_forward.ipynb | jjeamin/pytorch-tutorial |
Importar librerías y series de datos | import time
start = time.time()
#importar datos y librerias
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from scipy import signal
from sklearn.linear_model import LinearRegression
from statsmodels.tsa.seasonal import seasonal_decompose
from scipy.stats import box... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Establecer las funciones a utilizar | def kronecker(data1:'Dataframe 1',data2:'Dataframe 2'):
x=0
data1_kron=data1[x:x+1]
data2_kron=data2[x:x+1]
Combinacion=np.kron(data1_kron,data2_kron)
Combinacion=pd.DataFrame(Combinacion)
for x in range(1,len(data1)):
data1_kron=data1[x:x+1]
data2_kron=data2[x:x+1]
kron=... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
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