CodPy Book
Chapter 2:
Chapter 2: Basic notions on reproducing kernels
2.2 Reproducing kernels and transformation maps
2.3 1D Periodic Function Extrapolation
2.3.2 Applying maps to kernels
2.4 2D Periodic Function Extrapolation
2.6.2 A study of the discrepancy functional
Chapter 3:
Chapter 3: Discrete operators based on reproducing kernels
3.3.2 Partition of unity
3.4.1 Gradient Operator
3.4.2 Divergence Operator
3.4.3 Inverse Laplace operator
3.4.4 Integral operator - inverse gradient operator
3.4.5 Integral operator - inverse divergence operator
3.4.6 Leray-orthogonal operator
3.4.7 Leray operator and Helmholtz-Hodge decomposition
Chapter 4:
Chapter 4: Clustering strategies
4.2 Clustering
Chapter 5:
Chapter 5: Optimal transport and statistical kernel methods
5.2 Optimal Transport: LSAP (Linear Sum Assignment Problem)
5.3.3 Kernel Conditional Density
5.3.4 Kernel Conditional Expectation
5.6.a Application of OT in Disitribution Sampling : 1D
5.6.b Application of OT in Disitribution Sampling : 2D
5.6.c Application of OT in Disitribution Sampling : High-Dimensional case
5.6.d Exploration Data Analysis of the Latent Space: Spherical Data
5.6.e Exploration Data Analysis of the Latent Space: Spherical Data - 2
5.7. Conditional Sampling
Chapter 6:
Chapter 6: Applications to machine learning : supervised, unsupervised and generative methods
6.1 Supervised learning: reproducibility illustration with housing prices
6.2 Supervised learning: benchmarks of methods with MNIST
6.3 Unsupervised learning: Clustering - MNIST
6.4 Unsupervised learning: Clustering - Fraud detection
6.4.1 Generating complex distributions
6.4.1 Generating complex distributions from celebA dataset
6.4.5 Conditioning on discrete distributions of celebA dataset
6.5 Unsupervised learning: Clustering - Stock Clustering
6.7. Conditional Sampling
Chapter 7:
Chapter 7: Application to partial differential equations
7.01 Inverse Laplace Operator
7.02 Denoising
7.03 Heat Equation
7.04 Lagrange Heat Equation
7.05 Convex Hull Algorithm
7.06 Automatic Differentiation
Chapter 8:
KQLearning
Chapter 8: Reinforcement learning.
8.1 Using KAgents
8.2 Experiments - Cartpole
8.3 Experiments - LunarLander
Chapter 9:
Chapter 9: Mathematical Finance.
9.01 Free time series modeling
9.02 Random walks
9.03 ARMA(p,1) model
9.04 GARCH(1,1) model
9.05 Lagrange interpolation model
9.06 Additive noise map
9.07 Conditionning model
9.08 Stochastic volatility model
9.09 Heston Process - Reproducibility
9.10 Heston Process - Path comparison
9.11 Heston Process - Intraday interpolation
9.12 Price extrapolation using KRR and Taylor
9.13 Greeks output after correction
9.13 Reverse Stress Test
CodPy Book
Index
Index
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B
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C
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G
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K
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M
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O
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P
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R
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T
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U
_
__call__() (codpy.KQLearning.KController method)
__init__() (codpy.KQLearning.KAgent method)
B
bellman_optimal_action() (codpy.KQLearning.KAgent method)
C
codpy.KQLearning
module
compute_returns() (codpy.KQLearning.KAgent method)
G
GamesKernel (class in codpy.KQLearning)
GamesKernelClassifier (class in codpy.KQLearning)
get_advantages() (codpy.KQLearning.KActorCritic method)
(codpy.KQLearning.PolicyGradient method)
get_derivatives_policy_state_action_value_function() (codpy.KQLearning.KAgent method)
get_function() (codpy.KQLearning.KController method)
K
KActorCritic (class in codpy.KQLearning)
KAgent (class in codpy.KQLearning)
KController (class in codpy.KQLearning)
KQLearning (class in codpy.KQLearning)
KQLearningHJB (class in codpy.KQLearning)
M
module
codpy.KQLearning
O
optimal_states_values_function() (codpy.KQLearning.KAgent method)
(codpy.KQLearning.KQLearningHJB method)
P
PolicyGradient (class in codpy.KQLearning)
R
rl_hot_encoder() (in module codpy.KQLearning)
T
train() (codpy.KQLearning.KController method)
U
update_probabilities() (codpy.KQLearning.KAgent method)