// normFunc implements Func and normalizes the traces to a mean of 0 and a
// standard deviation of 1.0. If a second optional number is passed in to
// norm() then that is used as the minimum standard deviation that is
// normalized, otherwise it defaults to config.MIN_STDDEV.
func (NormFunc) Eval(ctx *Context, node *Node) ([]*types.PerfTrace, error) {
if len(node.Args) > 2 || len(node.Args) == 0 {
return nil, fmt.Errorf("norm() takes one or two arguments.")
}
if node.Args[0].Typ != NodeFunc {
return nil, fmt.Errorf("norm() takes a function as its first argument.")
}
minStdDev := config.MIN_STDDEV
if len(node.Args) == 2 {
if node.Args[1].Typ != NodeNum {
return nil, fmt.Errorf("norm() takes a number as its second argument.")
}
var err error
minStdDev, err = strconv.ParseFloat(node.Args[1].Val, 64)
if err != nil {
return nil, fmt.Errorf("norm() stddev not a valid number %s : %s", node.Args[1].Val, err)
}
}
traces, err := node.Args[0].Eval(ctx)
if err != nil {
return nil, fmt.Errorf("norm() failed evaluating argument: %s", err)
}
for _, tr := range traces {
vec.Norm(tr.Values, minStdDev)
}
return traces, nil
}
// NewFullTrace takes data you would find in a Trace and returns a
// ClusterableTrace usable for kmeans clustering.
func NewFullTrace(key string, values []float64, params map[string]string, minStdDev float64) *ClusterableTrace {
norm := make([]float64, len(values))
copy(norm, values)
vec.Fill(norm)
vec.Norm(norm, minStdDev)
return &ClusterableTrace{
Key: key,
Values: norm,
Params: params,
}
}
