The provided code defines a method named `applyDecay` that applies...
September 1, 2025 at 04:23 PM
The provided code defines a method named applyDecay
that applies an exponential decay calculation to the given input value
.
Here is what the code does in detail:
Method:
private double applyDecay(double value, double deltaTime, double decayBase)
=> value * Math.Pow(decayBase, deltaTime / 1000);
Explanation:
-
Parameters:
value
- The initial value to which the decay is applied.deltaTime
- A duration (likely in milliseconds) representing the time over which decay is applied.decayBase
- A base factor (between 0 and 1 for decay) used to calculate the decay rate.
-
Logic:
- The method uses the formula for exponential decay:
[
\text{NewValue} = \text{InitialValue} \times (\text{Decay Base})^{(\text{deltaTime} / 1000)}
]
Math.Pow(decayBase, deltaTime / 1000)
computes how much the value decays, wheredeltaTime / 1000
converts the time from milliseconds to seconds.- The resulting decay factor is multiplied with the initial
value
, reducing it accordingly.
- The method uses the formula for exponential decay:
[
\text{NewValue} = \text{InitialValue} \times (\text{Decay Base})^{(\text{deltaTime} / 1000)}
]
-
Purpose:
- This function computes a decayed or diminished value based on a specified base of decay and a time interval in milliseconds. It can be used in scenarios such as physics simulations (e.g., damping), financial calculations, or reducing effects over time (e.g., cooldown effects in games).
Example Usage:
double decayedValue = applyDecay(100.0, 2000, 0.9);
- If
value = 100.0
,deltaTime = 2000
(ms), anddecayBase = 0.9
:- Time is converted to 2 seconds (
deltaTime / 1000
). - Decay factor is ((0.9)^2 = 0.81).
- Final output:
100.0 * 0.81 = 81.0
.
- Time is converted to 2 seconds (
In summary, this method calculates an exponentially decayed value over time using a decay factor (decayBase
) raised to the power of the elapsed time (in seconds).
Generate your own explanations
Download our vscode extension
Read other generated explanations
Built by @thebuilderjr
Sponsored by beam analytics
Read our terms and privacy policy
Forked from openai-quickstart-node