vendor: delete

6 files changed, 0 insertions, 474 deletions

diff --git a/vendor/github.com/VividCortex/gohistogram/.gitignore b/vendor/github.com/VividCortex/gohistogram/.gitignore
deleted file mode 100644
index 4c51178c9..000000000
--- a/vendor/github.com/VividCortex/gohistogram/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-\#*
-.\#*
\ No newline at end of file
diff --git a/vendor/github.com/VividCortex/gohistogram/LICENSE b/vendor/github.com/VividCortex/gohistogram/LICENSE
deleted file mode 100644
index d23fea365..000000000
--- a/vendor/github.com/VividCortex/gohistogram/LICENSE
+++ /dev/null
@@ -1,19 +0,0 @@
-Copyright (c) 2013 VividCortex
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
diff --git a/vendor/github.com/VividCortex/gohistogram/README.md b/vendor/github.com/VividCortex/gohistogram/README.md
deleted file mode 100644
index eeb14d366..000000000
--- a/vendor/github.com/VividCortex/gohistogram/README.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# gohistogram - Histograms in Go
-
-![build status](https://circleci.com/gh/VividCortex/gohistogram.png?circle-token=d37ec652ea117165cd1b342400a801438f575209)
-
-This package provides [Streaming Approximate Histograms](https://vividcortex.com/blog/2013/07/08/streaming-approximate-histograms/)
-for efficient quantile approximations.
-
-The histograms in this package are based on the algorithms found in
-Ben-Haim & Yom-Tov's *A Streaming Parallel Decision Tree Algorithm*
-([PDF](http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf)).
-Histogram bins do not have a preset size. As values stream into
-the histogram, bins are dynamically added and merged.
-
-Another implementation can be found in the Apache Hive project (see
-[NumericHistogram](http://hive.apache.org/docs/r0.11.0/api/org/apache/hadoop/hive/ql/udf/generic/NumericHistogram.html)).
-
-An example:
-
-![histogram](http://i.imgur.com/5OplaRs.png)
-
-The accurate method of calculating quantiles (like percentiles) requires
-data to be sorted. Streaming histograms make it possible to approximate
-quantiles without sorting (or even individually storing) values.
-
-NumericHistogram is the more basic implementation of a streaming
-histogram. WeightedHistogram implements bin values as exponentially-weighted
-moving averages.
-
-A maximum bin size is passed as an argument to the constructor methods. A
-larger bin size yields more accurate approximations at the cost of increased
-memory utilization and performance.
-
-A picture of kittens:
-
-![stack of kittens](http://i.imgur.com/QxRTWAE.jpg)
-
-## Getting started
-
-### Using in your own code
-
-    $ go get github.com/VividCortex/gohistogram
-    
-```go
-import "github.com/VividCortex/gohistogram"
-```
-
-### Running tests and making modifications
-
-Get the code into your workspace:
-
-    $ cd $GOPATH
-    $ git clone git@github.com:VividCortex/gohistogram.git ./src/github.com/VividCortex/gohistogram
-
-You can run the tests now:
-
-    $ cd src/github.com/VividCortex/gohistogram
-    $ go test .
-
-## API Documentation
-
-Full source documentation can be found [here][godoc].
-
-[godoc]: http://godoc.org/github.com/VividCortex/gohistogram
-
-## Contributing
-
-We only accept pull requests for minor fixes or improvements. This includes:
-
-* Small bug fixes
-* Typos
-* Documentation or comments
-
-Please open issues to discuss new features. Pull requests for new features will be rejected,
-so we recommend forking the repository and making changes in your fork for your use case.
-
-## License
-
-Copyright (c) 2013 VividCortex
-
-Released under MIT License. Check `LICENSE` file for details.
diff --git a/vendor/github.com/VividCortex/gohistogram/histogram.go b/vendor/github.com/VividCortex/gohistogram/histogram.go
deleted file mode 100644
index ede21fd31..000000000
--- a/vendor/github.com/VividCortex/gohistogram/histogram.go
+++ /dev/null
@@ -1,23 +0,0 @@
-package gohistogram
-
-// Copyright (c) 2013 VividCortex, Inc. All rights reserved.
-// Please see the LICENSE file for applicable license terms.
-
-// Histogram is the interface that wraps the Add and Quantile methods.
-type Histogram interface {
-	// Add adds a new value, n, to the histogram. Trimming is done
-	// automatically.
-	Add(n float64)
-
-	// Quantile returns an approximation.
-	Quantile(n float64) (q float64)
-
-	// String returns a string reprentation of the histogram,
-	// which is useful for printing to a terminal.
-	String() (str string)
-}
-
-type bin struct {
-	value float64
-	count float64
-}
diff --git a/vendor/github.com/VividCortex/gohistogram/numerichistogram.go b/vendor/github.com/VividCortex/gohistogram/numerichistogram.go
deleted file mode 100644
index 20dea740d..000000000
--- a/vendor/github.com/VividCortex/gohistogram/numerichistogram.go
+++ /dev/null
@@ -1,160 +0,0 @@
-package gohistogram
-
-// Copyright (c) 2013 VividCortex, Inc. All rights reserved.
-// Please see the LICENSE file for applicable license terms.
-
-import (
-	"fmt"
-)
-
-type NumericHistogram struct {
-	bins    []bin
-	maxbins int
-	total   uint64
-}
-
-// NewHistogram returns a new NumericHistogram with a maximum of n bins.
-//
-// There is no "optimal" bin count, but somewhere between 20 and 80 bins
-// should be sufficient.
-func NewHistogram(n int) *NumericHistogram {
-	return &NumericHistogram{
-		bins:    make([]bin, 0),
-		maxbins: n,
-		total:   0,
-	}
-}
-
-func (h *NumericHistogram) Add(n float64) {
-	defer h.trim()
-	h.total++
-	for i := range h.bins {
-		if h.bins[i].value == n {
-			h.bins[i].count++
-			return
-		}
-
-		if h.bins[i].value > n {
-
-			newbin := bin{value: n, count: 1}
-			head := append(make([]bin, 0), h.bins[0:i]...)
-
-			head = append(head, newbin)
-			tail := h.bins[i:]
-			h.bins = append(head, tail...)
-			return
-		}
-	}
-
-	h.bins = append(h.bins, bin{count: 1, value: n})
-}
-
-func (h *NumericHistogram) Quantile(q float64) float64 {
-	count := q * float64(h.total)
-	for i := range h.bins {
-		count -= float64(h.bins[i].count)
-
-		if count <= 0 {
-			return h.bins[i].value
-		}
-	}
-
-	return -1
-}
-
-// CDF returns the value of the cumulative distribution function
-// at x
-func (h *NumericHistogram) CDF(x float64) float64 {
-	count := 0.0
-	for i := range h.bins {
-		if h.bins[i].value <= x {
-			count += float64(h.bins[i].count)
-		}
-	}
-
-	return count / float64(h.total)
-}
-
-// Mean returns the sample mean of the distribution
-func (h *NumericHistogram) Mean() float64 {
-	if h.total == 0 {
-		return 0
-	}
-
-	sum := 0.0
-
-	for i := range h.bins {
-		sum += h.bins[i].value * h.bins[i].count
-	}
-
-	return sum / float64(h.total)
-}
-
-// Variance returns the variance of the distribution
-func (h *NumericHistogram) Variance() float64 {
-	if h.total == 0 {
-		return 0
-	}
-
-	sum := 0.0
-	mean := h.Mean()
-
-	for i := range h.bins {
-		sum += (h.bins[i].count * (h.bins[i].value - mean) * (h.bins[i].value - mean))
-	}
-
-	return sum / float64(h.total)
-}
-
-func (h *NumericHistogram) Count() float64 {
-	return float64(h.total)
-}
-
-// trim merges adjacent bins to decrease the bin count to the maximum value
-func (h *NumericHistogram) trim() {
-	for len(h.bins) > h.maxbins {
-		// Find closest bins in terms of value
-		minDelta := 1e99
-		minDeltaIndex := 0
-		for i := range h.bins {
-			if i == 0 {
-				continue
-			}
-
-			if delta := h.bins[i].value - h.bins[i-1].value; delta < minDelta {
-				minDelta = delta
-				minDeltaIndex = i
-			}
-		}
-
-		// We need to merge bins minDeltaIndex-1 and minDeltaIndex
-		totalCount := h.bins[minDeltaIndex-1].count + h.bins[minDeltaIndex].count
-		mergedbin := bin{
-			value: (h.bins[minDeltaIndex-1].value*
-				h.bins[minDeltaIndex-1].count +
-				h.bins[minDeltaIndex].value*
-					h.bins[minDeltaIndex].count) /
-				totalCount, // weighted average
-			count: totalCount, // summed heights
-		}
-		head := append(make([]bin, 0), h.bins[0:minDeltaIndex-1]...)
-		tail := append([]bin{mergedbin}, h.bins[minDeltaIndex+1:]...)
-		h.bins = append(head, tail...)
-	}
-}
-
-// String returns a string reprentation of the histogram,
-// which is useful for printing to a terminal.
-func (h *NumericHistogram) String() (str string) {
-	str += fmt.Sprintln("Total:", h.total)
-
-	for i := range h.bins {
-		var bar string
-		for j := 0; j < int(float64(h.bins[i].count)/float64(h.total)*200); j++ {
-			bar += "."
-		}
-		str += fmt.Sprintln(h.bins[i].value, "\t", bar)
-	}
-
-	return
-}
diff --git a/vendor/github.com/VividCortex/gohistogram/weightedhistogram.go b/vendor/github.com/VividCortex/gohistogram/weightedhistogram.go
deleted file mode 100644
index 16eed3719..000000000
--- a/vendor/github.com/VividCortex/gohistogram/weightedhistogram.go
+++ /dev/null
@@ -1,190 +0,0 @@
-// Package gohistogram contains implementations of weighted and exponential histograms.
-package gohistogram
-
-// Copyright (c) 2013 VividCortex, Inc. All rights reserved.
-// Please see the LICENSE file for applicable license terms.
-
-import "fmt"
-
-// A WeightedHistogram implements Histogram. A WeightedHistogram has bins that have values
-// which are exponentially weighted moving averages. This allows you keep inserting large
-// amounts of data into the histogram and approximate quantiles with recency factored in.
-type WeightedHistogram struct {
-	bins    []bin
-	maxbins int
-	total   float64
-	alpha   float64
-}
-
-// NewWeightedHistogram returns a new WeightedHistogram with a maximum of n bins with a decay factor
-// of alpha.
-//
-// There is no "optimal" bin count, but somewhere between 20 and 80 bins should be
-// sufficient.
-//
-// Alpha should be set to 2 / (N+1), where N represents the average age of the moving window.
-// For example, a 60-second window with an average age of 30 seconds would yield an
-// alpha of 0.064516129.
-func NewWeightedHistogram(n int, alpha float64) *WeightedHistogram {
-	return &WeightedHistogram{
-		bins:    make([]bin, 0),
-		maxbins: n,
-		total:   0,
-		alpha:   alpha,
-	}
-}
-
-func ewma(existingVal float64, newVal float64, alpha float64) (result float64) {
-	result = newVal*(1-alpha) + existingVal*alpha
-	return
-}
-
-func (h *WeightedHistogram) scaleDown(except int) {
-	for i := range h.bins {
-		if i != except {
-			h.bins[i].count = ewma(h.bins[i].count, 0, h.alpha)
-		}
-	}
-}
-
-func (h *WeightedHistogram) Add(n float64) {
-	defer h.trim()
-	for i := range h.bins {
-		if h.bins[i].value == n {
-			h.bins[i].count++
-
-			defer h.scaleDown(i)
-			return
-		}
-
-		if h.bins[i].value > n {
-
-			newbin := bin{value: n, count: 1}
-			head := append(make([]bin, 0), h.bins[0:i]...)
-
-			head = append(head, newbin)
-			tail := h.bins[i:]
-			h.bins = append(head, tail...)
-
-			defer h.scaleDown(i)
-			return
-		}
-	}
-
-	h.bins = append(h.bins, bin{count: 1, value: n})
-}
-
-func (h *WeightedHistogram) Quantile(q float64) float64 {
-	count := q * h.total
-	for i := range h.bins {
-		count -= float64(h.bins[i].count)
-
-		if count <= 0 {
-			return h.bins[i].value
-		}
-	}
-
-	return -1
-}
-
-// CDF returns the value of the cumulative distribution function
-// at x
-func (h *WeightedHistogram) CDF(x float64) float64 {
-	count := 0.0
-	for i := range h.bins {
-		if h.bins[i].value <= x {
-			count += float64(h.bins[i].count)
-		}
-	}
-
-	return count / h.total
-}
-
-// Mean returns the sample mean of the distribution
-func (h *WeightedHistogram) Mean() float64 {
-	if h.total == 0 {
-		return 0
-	}
-
-	sum := 0.0
-
-	for i := range h.bins {
-		sum += h.bins[i].value * h.bins[i].count
-	}
-
-	return sum / h.total
-}
-
-// Variance returns the variance of the distribution
-func (h *WeightedHistogram) Variance() float64 {
-	if h.total == 0 {
-		return 0
-	}
-
-	sum := 0.0
-	mean := h.Mean()
-
-	for i := range h.bins {
-		sum += (h.bins[i].count * (h.bins[i].value - mean) * (h.bins[i].value - mean))
-	}
-
-	return sum / h.total
-}
-
-func (h *WeightedHistogram) Count() float64 {
-	return h.total
-}
-
-func (h *WeightedHistogram) trim() {
-	total := 0.0
-	for i := range h.bins {
-		total += h.bins[i].count
-	}
-	h.total = total
-	for len(h.bins) > h.maxbins {
-
-		// Find closest bins in terms of value
-		minDelta := 1e99
-		minDeltaIndex := 0
-		for i := range h.bins {
-			if i == 0 {
-				continue
-			}
-
-			if delta := h.bins[i].value - h.bins[i-1].value; delta < minDelta {
-				minDelta = delta
-				minDeltaIndex = i
-			}
-		}
-
-		// We need to merge bins minDeltaIndex-1 and minDeltaIndex
-		totalCount := h.bins[minDeltaIndex-1].count + h.bins[minDeltaIndex].count
-		mergedbin := bin{
-			value: (h.bins[minDeltaIndex-1].value*
-				h.bins[minDeltaIndex-1].count +
-				h.bins[minDeltaIndex].value*
-					h.bins[minDeltaIndex].count) /
-				totalCount, // weighted average
-			count: totalCount, // summed heights
-		}
-		head := append(make([]bin, 0), h.bins[0:minDeltaIndex-1]...)
-		tail := append([]bin{mergedbin}, h.bins[minDeltaIndex+1:]...)
-		h.bins = append(head, tail...)
-	}
-}
-
-// String returns a string reprentation of the histogram,
-// which is useful for printing to a terminal.
-func (h *WeightedHistogram) String() (str string) {
-	str += fmt.Sprintln("Total:", h.total)
-
-	for i := range h.bins {
-		var bar string
-		for j := 0; j < int(float64(h.bins[i].count)/float64(h.total)*200); j++ {
-			bar += "."
-		}
-		str += fmt.Sprintln(h.bins[i].value, "\t", bar)
-	}
-
-	return
-}