cinneesol

## multiple_changepoint_marginalization.ipynb

      
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## list.md

      
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                Quantopian Lectures Saved
              
          
    Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]

Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]

Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]

Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]

Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

  
## robust_splines_sklearn.py
#!/usr/bin/env python
"""
Robust B-Spline regression with scikit-learn
"""

import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate as si
from sklearn.base import TransformerMixin
from sklearn.pipeline import make_pipeline

## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
	#!/usr/bin/env python
	"""
	Robust B-Spline regression with scikit-learn
	"""

	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.interpolate as si
	from sklearn.base import TransformerMixin
	from sklearn.pipeline import make_pipeline
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward