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# library | |
from pytube import YouTube | |
mario_video = YouTube('https://www.youtube.com/watch?v=lXMJt5PP3kM') | |
# viewing available video formats | |
print('Title:', mario_video.title, '---') | |
stream = mario_video.streams.filter(file_extension = "mp4").all() | |
for i in stream: | |
print(i) |
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import quandl | |
# authentication ---- | |
quandl_key = 'key' # paste your own API key here :) | |
quandl.ApiConfig.api_key = quandl_key | |
df = quandl.get('WIKI/MSFT', start_date="2000-01-01", end_date="2017-12-31") | |
df = df.loc[:, ['Adj. Close']] | |
df.columns = ['adj_close'] | |
# create simple and log returns, multiplied by 100 for convenience |
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# parameters for the analysis | |
effect_size = 0.8 | |
alpha = 0.05 # significance level | |
power = 0.8 | |
power_analysis = TTestIndPower() | |
sample_size = power_analysis.solve_power(effect_size = effect_size, | |
power = power, | |
alpha = alpha) |
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# power vs. number of observations | |
fig = plt.figure() | |
ax = fig.add_subplot(2,1,1) | |
fig = TTestIndPower().plot_power(dep_var='nobs', | |
nobs= np.arange(2, 200), | |
effect_size=np.array([0.2, 0.5, 0.8]), | |
alpha=0.01, | |
ax=ax, title='Power of t-Test' + '\n' + r'$\alpha = 0.01$') | |
ax.get_legend().remove() |
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# for this part I assume significance level of 0.05 | |
@np.vectorize | |
def power_grid(x,y): | |
power = TTestIndPower().solve_power(effect_size = x, | |
nobs1 = y, | |
alpha = 0.05) | |
return power | |
X,Y = np.meshgrid(np.linspace(0.01, 1, 51), |
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# connect to Google Drive | |
from google.colab import drive | |
drive.mount('/content/gdrive') |
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!pip install gdown | |
# create directory for storing data | |
!mkdir -p data | |
# download zip file with training set | |
!gdown https://drive.google.com/uc?id=1z_vO2muBgzNGIa7JtY8OPmaeUC348jj4 && unzip -qq training_set.zip -d data/training_set | |
!rm training_set.zip | |
# download zip with test set |
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# 1. defining parameters ---- | |
# number of subprocesses to use for data loading | |
num_workers = 0 | |
# number of samples to load per batch | |
batch_size = 32 | |
# % of training set to use as validation | |
valid_size = 0.2 | |
# define transformations that will be applied to images |
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# inspect loaded images ---- | |
# obtain one batch of training images | |
dataiter = iter(train_loader) | |
data, target = dataiter.next() | |
data = data.numpy() # convert images to numpy for display | |
# plot the images in the batch, along with the corresponding labels | |
fig = plt.figure(figsize=(20, 10)) | |
# display 10 images |
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# create the class containing the architecture of the network (inherits from nn.Module) | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
# define the layers | |
# cheatsheet | |
# nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, |
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