mirror of
https://github.com/logos-blockchain/logos-blockchain-specs.git
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* cryptarchia/ghost: prep for move to weight based fork choice * cryptarchia/ghost: remove common_prefix_len helper * cryptarchia/ghost: common_prefix_depth returns depth of both chains * cryptarchia/ghost: fix chain density calculation * cryptarchia/ghost: maxvalid_bg uses block ids rather than chains * cryptarchia/ghost: unimported_orphans returns orphans w.r.t. to tip * cryptarchia/ghost: remove redundant check * cryptarchia/ghost: rewrite unimported_orphan w/ common_prefix_depths * cryptarchia/ghost: validate_header w.r.t. block parent * cryptachia/ghost: rewrite on_block to remove dependency on Chain * cryptarchia/ghost: remove Chain abstraction * cryptarchia/ghost: remove local / fork naming in common_prefix_depth * cryptarchia/ghost: rewrite common_prefix_depth in terms of iter_chain * cryptarchia/ghost: impl GHOST fork choice rule * cryptarchia/ghost: integrate GHOST with maxvalid fork choice * cryptarchia: remove unused imports * cryptarchia: cleanup * cryptarchia: cleanup * cryptarchia: remove height from ledger state * cryptachia/ghost: update fork choice rule comments * cryptarchia: switch back to longest chain * cryptarchia: update tests * cryptarchia: remove debug log
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
from unittest import TestCase
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import numpy as np
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from .cryptarchia import Leader, EpochState, LedgerState, Coin, phi, Slot
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from .test_common import mk_config
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class TestLeader(TestCase):
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def test_slot_leader_statistics(self):
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epoch = EpochState(
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stake_distribution_snapshot=LedgerState(block=None),
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nonce_snapshot=LedgerState(block=None, nonce=b"1010101010"),
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inferred_total_active_stake=1000,
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)
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coin = Coin(sk=0, value=10)
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f = 0.05
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l = Leader(
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config=mk_config([coin]).replace(active_slot_coeff=f),
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coin=coin,
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)
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# We'll use the Margin of Error equation to decide how many samples we need.
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# https://en.wikipedia.org/wiki/Margin_of_error
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margin_of_error = 1e-4
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p = phi(f=f, alpha=10 / epoch.total_active_stake())
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std = np.sqrt(p * (1 - p))
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Z = 3 # we want 3 std from the mean to be within the margin of error
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N = int((Z * std / margin_of_error) ** 2)
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# After N slots, the measured leader rate should be within the
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# interval `p +- margin_of_error` with high probabiltiy
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leader_rate = (
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sum(
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l.try_prove_slot_leader(epoch, Slot(slot), bytes(32)) is not None
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for slot in range(N)
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)
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/ N
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)
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assert (
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abs(leader_rate - p) < margin_of_error
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), f"{leader_rate} != {p}, err={abs(leader_rate - p)} > {margin_of_error}"
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