Feat/support learn & review limit

[L-M-Sherlock]

open-spaced-repetition/fsrs-optimizer#79

[aleksejrs]

figs = optimizer.find_optimal_retention(
    deck_size=557,  # number of cards to learn
    learn_span=365,  # days to learn
    max_cost_perday=1800,  # seconds to review and learn per day
    learn_limit_perday=float("inf"),  # number of cards to learn per day
    review_limit_perday=float("inf"),  # number of cards to review per day
    max_ivl=36500,  # days
    loss_aversion=2.5,  # forget cost is multiplied by this factor to simulate loss aversion
)

Learn Count per Day is 31-32? Is that a floating average?

learn_limit_perday=1.0

  0%|          | 0/365 [00:00<?, ?it/s]
 52%|█████▏    | 191/365 [00:00<00:00, 1899.90it/s]
                                                   /usr/local/lib/python3.10/dist-packages/fsrs_optimizer/fsrs_simulator.py:444: RuntimeWarning: invalid value encountered in double_scalars
  if abs(cost_per_memorization[i] - target2) < abs(
/usr/local/lib/python3.10/dist-packages/fsrs_optimizer/fsrs_simulator.py:445: RuntimeWarning: invalid value encountered in double_scalars
  cost_per_memorization[i + 1] - target2
/usr/local/lib/python3.10/dist-packages/fsrs_optimizer/fsrs_simulator.py:455: RuntimeWarning: invalid value encountered in double_scalars
  if abs(cost_per_memorization[i] - target3) < abs(
/usr/local/lib/python3.10/dist-packages/fsrs_optimizer/fsrs_simulator.py:456: RuntimeWarning: invalid value encountered in double_scalars
  cost_per_memorization[i + 1] - target3

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-16-c93e450f7804> in <cell line: 1>()
----> 1 figs = optimizer.find_optimal_retention(
      2     deck_size=557,  # number of cards to learn
      3     learn_span=365,  # days to learn
      4     max_cost_perday=1800,  # seconds to review and learn per day
      5     learn_limit_perday=1.0,  # number of cards to learn per day

5 frames

/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py in _validate_converted_limits(self, limit, convert)
   3568             if (isinstance(converted_limit, Real)
   3569                     and not np.isfinite(converted_limit)):
-> 3570                 raise ValueError("Axis limits cannot be NaN or Inf")
   3571             return converted_limit
   3572 

ValueError: Axis limits cannot be NaN or Inf

[L-M-Sherlock]

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