vendor containers/image v1.4

This requires some additional changes to the dependencies since the
progress-bar library has been changed to github.com/vbauerster/mpb.

Please refer to the following link for the release notes:
https://github.com/containers/image/releases/tag/v1.4

Signed-off-by: Valentin Rothberg <rothberg@redhat.com>

vendor/github.com/VividCortex/ewma/README.md

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+# EWMA [![GoDoc](https://godoc.org/github.com/VividCortex/ewma?status.svg)](https://godoc.org/github.com/VividCortex/ewma) ![Build Status](https://circleci.com/gh/VividCortex/moving_average.png?circle-token=1459fa37f9ca0e50cef05d1963146d96d47ea523)
+
+This repo provides Exponentially Weighted Moving Average algorithms, or EWMAs for short, [based on our
+Quantifying Abnormal Behavior talk](https://vividcortex.com/blog/2013/07/23/a-fast-go-library-for-exponential-moving-averages/).
+
+### Exponentially Weighted Moving Average
+
+An exponentially weighted moving average is a way to continuously compute a type of
+average for a series of numbers, as the numbers arrive. After a value in the series is
+added to the average, its weight in the average decreases exponentially over time. This
+biases the average towards more recent data. EWMAs are useful for several reasons, chiefly
+their inexpensive computational and memory cost, as well as the fact that they represent
+the recent central tendency of the series of values.
+
+The EWMA algorithm requires a decay factor, alpha. The larger the alpha, the more the average
+is biased towards recent history. The alpha must be between 0 and 1, and is typically
+a fairly small number, such as 0.04. We will discuss the choice of alpha later.
+
+The algorithm works thus, in pseudocode:
+
+1. Multiply the next number in the series by alpha.
+2. Multiply the current value of the average by 1 minus alpha.
+3. Add the result of steps 1 and 2, and store it as the new current value of the average.
+4. Repeat for each number in the series.
+
+There are special-case behaviors for how to initialize the current value, and these vary
+between implementations. One approach is to start with the first value in the series;
+another is to average the first 10 or so values in the series using an arithmetic average,
+and then begin the incremental updating of the average. Each method has pros and cons.
+
+It may help to look at it pictorially. Suppose the series has five numbers, and we choose
+alpha to be 0.50 for simplicity. Here's the series, with numbers in the neighborhood of 300.
+
+![Data Series](https://user-images.githubusercontent.com/279875/28242350-463289a2-6977-11e7-88ca-fd778ccef1f0.png)
+
+Now let's take the moving average of those numbers. First we set the average to the value
+of the first number.
+
+![EWMA Step 1](https://user-images.githubusercontent.com/279875/28242353-464c96bc-6977-11e7-9981-dc4e0789c7ba.png)
+
+Next we multiply the next number by alpha, multiply the current value by 1-alpha, and add
+them to generate a new value.
+
+![EWMA Step 2](https://user-images.githubusercontent.com/279875/28242351-464abefa-6977-11e7-95d0-43900f29bef2.png)
+
+This continues until we are done.
+
+![EWMA Step N](https://user-images.githubusercontent.com/279875/28242352-464c58f0-6977-11e7-8cd0-e01e4efaac7f.png)
+
+Notice how each of the values in the series decays by half each time a new value
+is added, and the top of the bars in the lower portion of the image represents the
+size of the moving average. It is a smoothed, or low-pass, average of the original
+series.
+
+For further reading, see [Exponentially weighted moving average](http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) on wikipedia.
+
+### Choosing Alpha
+
+Consider a fixed-size sliding-window moving average (not an exponentially weighted moving average)
+that averages over the previous N samples. What is the average age of each sample? It is N/2.
+
+Now suppose that you wish to construct a EWMA whose samples have the same average age. The formula
+to compute the alpha required for this is: alpha = 2/(N+1). Proof is in the book
+"Production and Operations Analysis" by Steven Nahmias.
+
+So, for example, if you have a time-series with samples once per second, and you want to get the
+moving average over the previous minute, you should use an alpha of .032786885. This, by the way,
+is the constant alpha used for this repository's SimpleEWMA.
+
+### Implementations
+
+This repository contains two implementations of the EWMA algorithm, with different properties.
+
+The implementations all conform to the MovingAverage interface, and the constructor returns
+that type.
+
+Current implementations assume an implicit time interval of 1.0 between every sample added.
+That is, the passage of time is treated as though it's the same as the arrival of samples.
+If you need time-based decay when samples are not arriving precisely at set intervals, then
+this package will not support your needs at present.
+
+#### SimpleEWMA
+
+A SimpleEWMA is designed for low CPU and memory consumption. It **will** have different behavior than the VariableEWMA
+for multiple reasons. It has no warm-up period and it uses a constant
+decay.  These properties let it use less memory.  It will also behave
+differently when it's equal to zero, which is assumed to mean
+uninitialized, so if a value is likely to actually become zero over time,
+then any non-zero value will cause a sharp jump instead of a small change.
+
+#### VariableEWMA
+
+Unlike SimpleEWMA, this supports a custom age which must be stored, and thus uses more memory.
+It also has a "warmup" time when you start adding values to it. It will report a value of 0.0
+until you have added the required number of samples to it. It uses some memory to store the
+number of samples added to it. As a result it uses a little over twice the memory of SimpleEWMA.
+
+## Usage
+
+### API Documentation
+
+View the GoDoc generated documentation [here](http://godoc.org/github.com/VividCortex/ewma).
+
+```go
+package main
+import "github.com/VividCortex/ewma"
+
+func main() {
+  samples := [100]float64{
+    4599, 5711, 4746, 4621, 5037, 4218, 4925, 4281, 5207, 5203, 5594, 5149,
+  }
+
+  e := ewma.NewMovingAverage()       //=> Returns a SimpleEWMA if called without params
+  a := ewma.NewMovingAverage(5)      //=> returns a VariableEWMA with a decay of 2 / (5 + 1)
+
+  for _, f := range samples {
+    e.Add(f)
+    a.Add(f)
+  }
+
+  e.Value() //=> 13.577404704631077
+  a.Value() //=> 1.5806140565521463e-12
+}
+```
+
+## 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
+
+This repository is Copyright (c) 2013 VividCortex, Inc. All rights reserved.
+It is licensed under the MIT license. Please see the LICENSE file for applicable license terms.