Empirical
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#include <DataNode.h>
Public Member Functions | |
DataNodeModule () | |
double | GetVariance () const |
Get the variance (squared deviation from the mean) of values added since the last reset. More... | |
double | GetStandardDeviation () const |
Get the standard deviation of values added since the last reset. More... | |
double | GetSkew () const |
double | GetKurtosis () const |
void | AddDatum (const VAL_TYPE &val) |
Add. More... | |
void | Reset () |
Reset this node (resets current stats to 0) More... | |
void | PrintDebug (std::ostream &os=std::cout) |
Print debug information (useful for figuring out which modifiers you included) More... | |
Protected Types | |
using | this_t = DataNodeModule< VAL_TYPE, data::Stats, MODS... > |
using | parent_t = DataNodeModule< VAL_TYPE, MODS... > |
using | base_t = DataNodeModule< VAL_TYPE > |
Protected Attributes | |
double | M2 |
The second moment of the distribution. More... | |
double | M3 |
The third moment of the distribution. More... | |
double | M4 |
The fourth moment of the distribution. More... | |
== data::Stats == Note: These statistics are calculated with the assumption that the data this node has recieved is the entire population of measurements we're interested in, not a sample.
Note 2: Kurtosis is calculated using Snedecor and Cochran (1967)'s formula. A perfect normal distribution has a kurtosis of 0.
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Add.
val | to this DataNode |
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Get the kurtosis of the values added since the last reset. This is another measurement that describes the shape of the distribution. High kurtosis means that there is more data in the tails of the distribution (i.e. the tails are "heavier"), whereas low kurtosis means that there is less data in the tails. We use Snedecor and Cochran (1967)'s formula to calculate kurtosis. Under this formula, a normal distribution has kurtosis of 0.
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Get the skewness of values added since the last reset. This measurement tells you about the shape of the distribution. For a unimodal distribution, negative skew means that the distribution has a longer/thicker tail to the left. Positive skew means that ths distribution has a longer/thicker tail to the right.
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Get the standard deviation of values added since the last reset.
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Get the variance (squared deviation from the mean) of values added since the last reset.
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Print debug information (useful for figuring out which modifiers you included)
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Reset this node (resets current stats to 0)
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The second moment of the distribution.
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The third moment of the distribution.
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The fourth moment of the distribution.