Mabwiser

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- Introduction of base random number generator class to abstract away RNG provider
- Currently, numpy random serves as the rng provider

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minor:
- Performance improvement to LinTS algorithm by pre-computing cholesky decomposition in fit.

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major:
- New Algorithm: Randomized Popularity (Popularity)
- This bandit uses a probabilistically weighted selection based on mean arm rewards
- Currently, it assumes that the rewards are non-negative

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minor:
- Correction for how Softmax normalizes exponents to prevent overflow.
- Removal of Simulator matplotlib backend specification to eliminate Mac OS error.

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major:
- New Algorithm: Linear Thompson Sampling (LinTS)
- LinTS implementation uses deterministic sampling of a multivariate normal distribution of the regression coefficients.
- Adapted from Agrawal, Shipra and Navin Goyal. "Thompson Sampling for Contextual Bandits with Linear Payoffs." ICML 2013.
- The implementation is based on an initial design by Anshuman Pradhan (anshuman.pradhanfmr.com) and Pramod R (pramod.rfmr.com)

minor:
- Scikit-learn version dependency on 0.22.0 or greater which provides a bug fix for sklearn.cluster.KMeans

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minor:
- Bug fix for partial fitting in UCB1 reported in 10 by harisankarh

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