Using MPSGraph.reductionSum on a specific axis - swift

I have the following function:
let mtlDevice = MTLCreateSystemDefaultDevice()!
let device = MPSGraphDevice(mtlDevice: mtlDevice)
let inputData: [UInt32] = [0, 45, 0, 0, 45, 81, 0, 54, 0, 0, 54, 81, 1, 45, 0, 1, 45, 81, 1, 54, 0, 1, 54, 81, 7, 63, 567, 7, 63, 648, 7, 72, 567, 7, 72, 648, 8, 63, 567, 8, 63, 648, 8, 72, 567, 8, 72, 648, 1, 9, 81, 1, 9, 162, 1, 18, 81, 1, 18, 162, 2, 9, 81, 2, 9, 162, 2, 18, 81, 2, 18, 162]
let inputTensor = MPSGraphTensorData(device: device, data: Data(bytes: inputData, count: inputData.count * 4),
shape: [3, 8, 3], dataType: .uInt32)
let graph = MPSGraph()
let inputPlaceholder = graph.placeholder(shape: [3, 8, 3], dataType: .uInt32, name: nil)
let output = graph.reductionSum(with: inputPlaceholder, axis: 2, name: nil)
let outputTensor = graph.run(feeds: [inputPlaceholder: inputTensor], targetTensors: [output], targetOperations: nil)[output]!
var outputData = [UInt32].init(repeating: 0, count: 3 * 8 * 1)
outputTensor.mpsndarray().readBytes(&outputData, strideBytes: nil)
print("output: \(outputTensor.shape) \(outputData)")
The intention is to reduce inputData, interpreted as a tensor of shape [3, 8, 3] along the third axis. That is, to obtain a tensor of shape [3, 8, 1] where each element is the sum of the 3 elements previously present in axis 2.
When I run the above code, I receive the following output:
output: [3, 8, 1] [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
This is surprising to me because it is obviously not the sum I was expecting.
Performing a similar operation in Tensorflow gives me the expected output:
data = tf.reshape(
[0, 45, 0, 0, 45, 81, 0, 54, 0, 0, 54, 81, 1, 45, 0, 1, 45, 81, 1, 54, 0, 1, 54, 81, 7, 63, 567, 7, 63, 648, 7, 72, 567, 7, 72, 648, 8, 63, 567, 8, 63, 648, 8, 72, 567, 8, 72, 648, 1, 9, 81, 1, 9, 162, 1, 18, 81, 1, 18, 162, 2, 9, 81, 2, 9, 162, 2, 18, 81, 2, 18, 162],
shape=[3, 8, 3])
tf.reduce_sum(data, axis=2)
Gives:
[[ 45, 126, 54, 135, 46, 127, 55, 136],
[637, 718, 646, 727, 638, 719, 647, 728],
[ 91, 172, 100, 181, 92, 173, 101, 182]]
as expected.
How can I obtain the same behaviour from reductionSum in MPSGraph? What explains the behaviour that I am observing from MPSGraph?

Related

Converting a byte array to image in Flutter?

I want to get a image from my REST API service however haven't found any documentation on how to decode the response body that would be a byte array to an image in Flutter? Anyone with some useful resources, please help...

Use this for your image widget: Image.memory(bytes). You can find more documentation on the Flutter dev website.

Since top rated answer use flutter/widget.dart, this use dart:ui only
Future<Image> tinypng() async {
final bytes = Uint8List.fromList([
137, 80, 78, 71, 13, 10, 26, 10, 0, 0, 0, 13, 73, 72, 68, 82, 0, 0, 0,
1, 0, 0, 0, 1, 8, 6, 0, 0, 0, 31, 21, 196, 137, 0, 0, 0, 10, 73, 68, 65,
84, 120, 156, 99, 0, 1, 0, 0, 5, 0, 1, 13, 10, 45, 180, 0, 0, 0, 0, 73,
69, 78, 68, 174, 66, 96, 130 // prevent dartfmt
]);
// copy from decodeImageFromList of package:flutter/painting.dart
final codec = await instantiateImageCodec(bytes);
final frameInfo = await codec.getNextFrame();
return frameInfo.image;
}

This will return a Widget
Image.memory(Uint8List);

I am getting an error Called "value % is not a member of scala.collection.immutable.Range.Inclusive" while filering

I am new to Scala, here i am trying to find the even numbers from 1 to 100, so while i am filtering,i am getting
scala.collection.immutable.Range.Inclusive
scala> var a = List(1 to 100)
a: List[scala.collection.immutable.Range.Inclusive] = List(Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100))
scala> a.filter(x => (x % 2 == 0))
<console>:26: error: value % is not a member of scala.collection.immutable.Range.Inclusive
a.filter(x => (x % 2 == 0))
^
scala> val b = a.filter(x => x % 2 == 0)
<console>:25: error: value % is not a member of scala.collection.immutable.Range.Inclusive
val b = a.filter(x => x % 2 == 0)
^

You're creating a list of Range, not a list with the ints in that range. For that, change it to:
val a = (1 to 10).toList
But #Tim's right, you can filter directly on the Range

You don't need to wrap the Range in a List, just do this:
val a = 1 to 100
a.filter(x => x % 2 == 0)

Intersection of Two Map rdd's in Scala

I have two RDD's, for example:
firstmapRDD - (0-14,List(0, 4, 19, 19079, 42697, 444, 42748))
secondmapRdd-(0-14,List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94))
I want to find the intersection.
I tried, var interResult = firstmapRDD.intersection(secondmapRdd), which shows no result in output file.
I also tried , cogrouping based on keys, mapRDD.cogroup(secondMapRDD).filter(x=>), but I don't know how to find the intersection between both the values, is it x=>x._1.intersect(x._2), Can someone help me with the syntax?
Even this throws a compile time error, mapRDD.cogroup(secondMapRDD).filter(x=>x._1.intersect(x._2))
var mapRDD = sc.parallelize(map.toList)
var secondMapRDD = sc.parallelize(secondMap.toList)
var interResult = mapRDD.intersection(secondMapRDD)
It may be because of ArrayBuffer[List[]] values, because of which the intersection is not working. Is there any hack to remove it?
I tried doing this
var interResult = mapRDD.cogroup(secondMapRDD).filter{case (_, (l,r)) => l.nonEmpty && r.nonEmpty }. map{case (k,(l,r)) => (k, l.toList.intersect(r.toList))}
Still getting an empty list!

Since you are looking intersect on values, you need to join both RDDs, get all the matched values, then do the intersect on values.
sample code:
val firstMap = Map(1 -> List(1,2,3,4,5))
val secondMap = Map(1 -> List(1,2,5))
val firstKeyRDD = sparkContext.parallelize(firstMap.toList, 2)
val secondKeyRDD = sparkContext.parallelize(secondMap.toList, 2)
val joinedRDD = firstKeyRDD.join(secondKeyRDD)
val finalResult = joinedRDD.map(tuple => {
val matchedLists = tuple._2
val intersectValues = matchedLists._1.intersect(matchedLists._2)
(tuple._1, intersectValues)
})
finalResult.foreach(println)
The output will be
(1,List(1, 2, 5))

WinBUGS "Array index is greater than array upper bound for a"

I have a simple Bayesian hierachical model (linear mixed model with random intercepts) which should be easy to run. The problem is that after successfully loading the model and data, I get the following error when I try to compile the model, "array index is greater than array upper bound for a." It seems like this should be an easy fix, but I've repeatedly checked the indexing, and the data, and cannot find a problem. I have experimented on different example datasets, and don't have any issue, which makes me think something other then indexing is the problem.
Any suggestions would be greatly appreciated!
model{
for (i in 1:n) {
y[i] ~ dnorm(y.hat[i],tau.y)
y.hat[i]<- a[d[i]] + beta0 + beta1*x[i]
}
beta0 ~ dnorm(50, 0.001) I(0, )
beta1 ~ dnorm(0, 0.001)
tau.y <- pow(sigma.y,-2)
sigma.y ~ dunif(0,100)
for (j in 1:J){
a[j] ~ dnorm(0, .0001)
a.hat[j] <- mu.a
}
mu.a ~ dnorm(0, .0001)
tau.a <- pow(sigma.a, -2)
sigma.a ~ dunif(0, 100)
}
list( n = 904, J = 44, y = c( 61.46, 86, 75.11, 77.31, 65.7, 71.82, 61.42, 63.8, 45.53, 48.29,
43.77, 51.6, 80.1, 51.1, 65.9, 71.9, 66.73, 68.96, 86, 74.3, 72.9, 66.9, 54.4,
66, 70, 64, 78.6, 70.6, 80.2, 75.2, 63.6, 62.7, 71.9, 56.1, 59.6, 57.6, 61,
59.9, 57.2, 51.4, 62.8, 70, 60.7, 63.3, 60.4, 65.3, 75.45, 63.8, 68.21, 70.45,
59.28, 53.05, 43.6, 30.82, 79.74, 56.03, 66.88, 49.31, 64.17, 56.57, 54.37,
51.99, 49.56, 42.6, 48.52, 51.72, 59.43, 57.81, 57.29, 57.79, 67.14, 70, 61.38,
54.23, 63.41, 58.19, 67.23, 31.91, 40.95, 53.83, 49.84, 53.32, 61.2, 52.11,
53.83, 58.13, 54.21, 52.72, 49.12, 43.7, 50.2, 36.93, 59.7, 57.8, 50.7, 68.2,
47.2, 70.27, 90.75, 72.16, 75.05, 75.2, 56.58, 63.59, 43.53, 35.6, 49.19,
64.36, 45.77, 53.85, 30.92, 56.99, 85.54, 77.73, 65, 42.11, 47.47, 25.3, 34.82,
21.5, 72.76, 81.08, 77.18, 69.41, 58.54, 72.73, 59.45, 63, 63.9, 62.15, 55.58,
49.72, 47.11, 54.85, 47.63, 47.7, 49.75, 48.32, 59.15, 77.3, 73.7, 87.73, 55,
61.3, 52.2, 52.9, 48.3, 58.7, 53.48, 78.88, 77.43, 81.94, 73.24, 71.75, 64.99,
66.99, 63.82, 57.23, 54.15, 57.08, 54.35, 65.24, 78.26, 63.63, 68.72, 65.21,
71.4, 44.54, 57.89, 60.9, 51.3, 64.84, 54, 52.76, 91.03, 93.05, 75.1, 71.89,
77.68, 79.88, 74.01, 56.67, 59.06, 48.66, 67.09, 41.25, 61.18, 68.31, 46.74,
50.87, 72.13, 79.7, 77.18, 58.28, 44.42, 60.12, 66.9, 67.73, 77.59, 62.12,
63.01, 84.65, 100, 69.56, 62.48, 47.2, 72.83, 52.83, 68.04, 69.11, 63.91,
56.25, 57.43, 86.5, 65.44, 70, 69, 65, 68, 67, 54, 50, 46, 55, 42, 45, 49.7,
48.7, 40.1, 51.8, 51.9, 62.5, 50.04, 63.2, 53.8, 57.46, 35.5, 69.94, 87.04,
82.1, 73.44, 67.49, 76.53, 73.21, 59.37, 63.41, 63.54, 59.56, 58.93, 56.38,
51.99, 50.94, 53.63, 57.79, 50.8, 53.66, 50.12, 47.58, 51.46, 56.57, 60.64,
63.23, 60.7, 61.2, 64.03, 68.85, 77.96, 74.98, 56.38, 83.7, 62.29, 68.38,
40.18, 71.12, 37.69, 68.43, 53.69, 76.56, 71.15, 67.67, 45.75, 75.21, 48.96,
53, 52.7, 34.6, 60.09, 52.18, 65.92, 71, 60.95, 61.79, 57.6, 52.91, 59.26,
48.98, 60.66, 42.7, 52.8, 41.14, 56.52, 42.54, 36.26, 35.68, 74.61, 61.22,
69.58, 63.14, 58.76, 56.82, 46.7, 54.82, 48.04, 56.47, 60.6, 53.11, 67, 51.88,
45.41, 46.76, 83.91, 55.55, 22.41, 38.54, 44.98, 41.47, 39.44, 54.04, 55.9,
67.3, 56.4, 68, 67.7, 62.8, 46.1, 86.44, 55.7, 61.98, 66.12, 77.48, 65.55,
62.58, 77.43, 99.05, 57.97, 49.57, 60.8, 52.68, 77.39, 63.34, 66.21, 67.25,
83.61, 64.02, 63.07, 58.1, 57.29, 55.68, 41, 45.65, 49.31, 55.91, 56.47, 58.5,
68.64, 61.64, 78.46, 72.22, 40.86, 50.36, 47.65, 50.06, 64, 63.35, 61.06,
47.24, 39.05, 45.22, 40.13, 33.94, 31.59, 34.2, 52.5, 38.46, 50.89, 63.63,
43.12, 56.92, 53.04, 63.26, 61.13, 63.7, 44.6, 45.37, 55.28, 84.3, 66.2, 64,
84, 47.8, 36, 36.9, 37.6, 50.3, 55.9, 42.9, 47, 43.8, 29.7, 42.8, 36.2, 35.3,
33.8, 38.2, 35.9, 37.1, 30.6, 44.6, 49.3, 34.9, 37, 37.6, 26.7, 40.8, 40.45,
53.8, 45.12, 29.48, 47.19, 66.23, 63.68, 63.91, 55.36, 55.96, 44.9, 36.8,
46.47, 47.76, 40.35, 41.97, 42, 60, 47.2, 49.72, 34.69, 43.14, 51.18, 52.81,
51.02, 45.89, 46.9, 28.6, 44.48, 41.31, 66.09, 56.26, 47.43, 31.9, 54.59,
61.56, 67.03, 69.74, 61.55, 63.02, 50.9, 52.9, 50.68, 48.36, 41.53, 35.13, 50,
49.21, 59.69, 39.45, 56.26, 48.78, 50.65, 45.26, 47.19, 50, 30.2, 46.81, 56.3,
48.24, 37.56, 57.35, 50.4, 42.39, 44.31, 45.43, 61.9, 55.03, 55.19, 52.83,
46.51, 45.66, 55.14, 58.89, 56.74, 46.58, 62.51, 45.15, 51.56, 44.95, 48.11,
44.33, 58.3, 49.5, 54.3, 46.5, 53.9, 37.7, 43.51, 39.86, 58.43, 68.21, 54.07,
55.67, 71.11, 67.25, 72.5, 63.41, 51.36, 51.74, 53.44, 55.8, 58.79, 44.12,
31.4, 46.9, 43.6, 46.18, 36.24, 41.08, 47.85, 53.69, 37.53, 37.43, 40.3, 21.3,
42.49, 55.71, 62.2, 58.45, 63.45, 58.13, 53.59, 57.82, 55.94, 53.44, 50.86,
20.85, 27.08, 42.8, 34.29, 38.9, 34.59, 26.45, 72.34, 104.88, 73.93, 66.95,
75.72, 75.1, 53.9, 74.67, 73.97, 85.5, 85.38, 59.7, 59.36, 44.51, 62.25, 56.26,
62.77, 60.24, 55.13, 61.35, 66.21, 56.06, 60.46, 70.45, 50, 66.44, 48.24,
55.56, 71.13, 60.97, 37.94, 43.6, 36, 51.22, 57.84, 58.56, 57.14, 63.51, 50.35,
58.68, 62.46, 59.02, 62.23, 59.44, 66.82, 65.38, 53.92, 57.73, 43.34, 58.41,
65.06, 56.91, 49.01, 53.5, 48.6, 52.47, 46.04, 58.81, 39.29, 43.59, 77.4, 30.9,
50.5, 31.66, 125, 37.8, 53.91, 59.84, 50, 52.31, 39.53, 47.87, 39.58, 46.97,
56.16, 57.78, 44.52, 40.22, 52.56, 45.45, 47.17, 76, 91.95, 78.97, 64.81,
55.56, 53.1, 51.71, 55.9, 93.6, 74.62, 53.37, 57.1, 62.57, 40.58, 56.75, 61.58,
58.08, 61.59, 65.45, 64.1, 57.03, 56.5, 49.56, 50, 32.84, 21.69, 51.9, 28.59,
45.2, 28.17, 39.29, 68.6, 57.07, 61.11, 48.05, 56.43, 63.2, 71.71, 55.25,
50.18, 43.23, 41.67, 30.37, 37.5, 38.39, 32.8, 37.36, 39.39, 54.41, 64.94,
60.38, 52.36, 53.57, 59.87, 70.77, 56.8, 55.1, 47.46, 48.98, 61.2, 30.41, 38.4,
43.19, 76.56, 74.65, 57.64, 60.87, 81.58, 83.3, 63.41, 54.05, 51.48, 54.01,
48.4, 52.24, 49.9, 43.4, 47.17, 43.6, 36.6, 40, 36.46, 38.56, 47.99, 49.58,
54.2, 42.51, 41.6, 33.2, 43.95, 41.31, 86.05, 66.98, 53.75, 58.36, 45.73,
53.42, 55.88, 69.72, 73.8, 67.85, 54.74, 52.36, 61, 56.58, 40.82, 40.77, 42.42,
38.44, 52.79, 52.73, 35.2, 46.03, 62.01, 64.46, 56.4, 36, 46.79, 48.64, 66.67,
72.03, 65.42, 82.25, 74.69, 62.61, 61.21, 53.09, 56.19, 57.95, 57.21, 43.76,
66.5, 55.76, 57.69, 59.67, 74.96, 58.78, 67.3, 69.15, 68.33, 71.56, 75.29,
67.05, 68.86, 78.61, 59.19, 51.66, 56.31, 64.66, 47.29, 58.39, 60.22, 40, 41.4,
41.38, 46.76, 61.8, 61.4, 68.63, 87.7, 78.46, 70.51, 59.77, 65.42, 72.1, 59.38,
61.21, 46.28, 41.42, 54.18, 47.31, 45.51, 36.31, 51.03, 61.08, 25.08, 59.45,
58.81, 60.37, 50, 80, 61.5, 89.44, 88.1, 75, 61.9, 80.56, 42.86, 67.31, 85.71,
60, 59.4, 43.99, 64.52, 69.4, 55.14, 43.75, 75, 76.71, 52.93, 52.34, 45.21,
38.28, 50.91, 61.54, 61.11, 45.87, 47.6, 45.05, 44.43, 52.9, 34.52, 40.57,
40.5, 37.3, 38, 45.06, 33.71, 32.12, 48.4, 39.78, 39.86, 36.8, 31.8, 47.86,
41.78, 48, 60, 49.8, 55.2, 44.6, 60.4, 57.4, 65.9, 59.1, 52.2, 54.41, 62.32,
57.33, 68.17, 62.96, 50.33, 51.48, 48.73, 59.56, 61.96, 56.68, 32, 49.6, 45.8,
53.16, 39.58, 58.55, 54.7, 42.53, 39.59, 33.33, 69.67, 66.79, 44.14, 47.75,
65.38, 39.7, 76.97, 34) , d = c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15,
15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22,
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 22, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25,
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,
26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 29, 29, 29, 29, 29, 29, 29, 29,
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31,
31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,
32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35,
35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36,
36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37,
37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39,
39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40,
40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40,
40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41,
41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42,
42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43,
43, 43, 43, 43, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51,
51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,
51, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53,
53, 53, 53, 53, 53, 53, 53) , x = c( 0.54, 0.5350262, 0.554, 0.582, 0.624,
0.638, 0.652, 0.666, 0.6834799, 0.681, 0.682, 0.684, 0.685, 0.686, 0.687,
0.688, 0.689, 0.6918783, 10.13947, 10.252, 10.364, 10.476, 10.588, 10.7,
10.812, 10.924, 11.036, 11.148, 11.25654, 11.757, 12.254, 12.751, 13.248,
13.745, 14.242, 14.739, 15.236, 15.733, 16.22779, 16.232, 16.234, 16.236,
16.238, 16.24, 16.242, 16.244, 16.246, 16.248, 16.24809, 10.28, 10.28, 10.28,
10.28, 10.28, 10.28, 10.28, 10.28, 10.498, 10.716, 10.934, 11.152, 11.37,
11.588, 11.806, 12.242, 12.45755, 12.464, 12.466, 12.468, 12.472, 12.474,
12.476, 12.478, 12.48307, 20.445, 20.495, 20.57, 20.62, 20.67224, 21.01, 21.35,
21.69, 22.03, 22.37, 22.71, 23.05, 23.39, 23.73, 24.06687, 24.098, 24.126,
24.154, 24.182, 24.21, 24.266, 24.322, 24.34951, 2.517, 2.614, 2.711, 3.002,
3.099, 3.293, 3.386308, 3.584, 3.778, 3.972, 4.166, 4.748, 4.942, 5.329492,
5.34, 5.345, 5.35, 5.355, 5.36, 5.365, 5.37, 5.375, 5.384694, 8.443984, 8.604,
8.768, 8.932, 9.096, 9.26, 9.424, 9.752, 9.916, 10.07591, 10.391, 10.702,
11.013, 11.324, 11.635, 12.257, 12.568, 12.879, 13.19029, 13.211, 13.232,
13.253, 13.274, 13.295, 13.316, 13.337, 13.358, 13.379, 13.3975, 10.23494,
10.26, 10.29, 10.32, 10.35, 10.41, 10.47, 10.52766, 11.038, 11.546, 12.054,
12.308, 12.816, 13.06939, 13.582, 14.094, 14.606, 15.118, 15.63, 16.142,
16.654, 17.166, 17.678, 18.19218, 10.25687, 10.412, 10.716, 10.868, 11.172,
11.324, 11.628, 11.945, 12.11, 12.44, 12.605, 12.77, 12.935, 13.1, 13.265,
13.4293, 13.499, 13.568, 13.637, 13.706, 13.775, 13.844, 13.913, 13.982,
14.051, 14.12134, 31.87835, 31.952, 32.024, 32.06, 32.096, 32.23761, 32.322,
32.527, 32.609, 32.821, 32.992, 33.163, 33.505, 33.676, 34.018, 34.189, 8.564,
8.568, 8.572, 8.58, 8.588, 8.596, 8.596099, 8.625, 8.675, 8.7, 8.725, 8.775,
8.8, 8.825, 8.96, 9.29, 9.4, 9.51, 9.62, 9.73, 9.84, 9.950349, 21.62867, 21.88,
22.13, 22.38, 22.63, 22.88, 23.13, 23.38, 23.63, 23.88, 24.12896, 24.482,
24.834, 25.186, 25.538, 25.89, 26.242, 26.594, 27.298, 27.6469, 28.052, 28.253,
28.454, 28.655, 28.856, 29.057, 29.258, 29.459, 29.65551, 16.064, 16.092,
16.12, 16.148, 16.176, 16.206, 16.238, 16.286, 16.302, 16.318, 16.334,
16.34918, 16.382, 16.414, 16.446, 16.478, 16.51, 16.542, 16.574, 16.606,
16.638, 16.66627, 15.708, 15.864, 16.098, 16.254, 16.40707, 16.424, 16.438,
16.452, 16.466, 16.48, 16.494, 16.508, 16.522, 16.536, 16.55052, 16.816,
17.082, 17.348, 17.614, 17.88, 18.146, 18.412, 18.678, 18.944, 19.20852,
17.284, 17.328, 17.372, 17.416, 17.45741, 23.314, 23.572, 24.088, 28.775,
30.237, 31.699, 32.43322, 32.979, 33.528, 34.077, 34.626, 35.175, 35.724,
36.273, 36.822, 37.371, 37.92301, 36.728, 36.977, 37.314, 37.441, 37.695,
38.076, 38.3302, 38.686, 38.864, 39.22, 39.576, 39.932, 13.29665, 13.53, 13.76,
13.99, 14.22, 14.68, 15.14, 15.59616, 16.44, 17.28, 18.12, 19.79912, 19.922,
20.044, 20.105, 20.166, 20.288, 20.349, 20.41163, 13.208, 13.252, 13.296,
13.34, 13.384, 13.428, 13.472, 13.516, 13.56361, 13.746, 13.932, 14.118,
14.304, 14.49, 14.676, 14.862, 15.048, 15.234, 15.42321, 15.54, 15.66, 15.78,
15.9, 16.02, 16.14, 16.26, 16.38, 16.5, 16.6215, 17.40591, 17.485, 17.56,
17.635, 17.71, 17.785, 17.86, 17.935, 18.01, 18.085, 18.16367, 18.167, 18.174,
18.181, 18.188, 18.195, 18.202, 18.209, 18.216, 18.223, 18.2299, 18.23, 18.23,
18.23, 18.23, 18.23, 18.23, 18.23, 18.23, 18.2299, 9.266, 9.738, 9.974, 10.21,
10.446, 10.682, 10.918, 11.154, 11.38546, 11.586, 11.782, 11.978, 12.174,
12.37, 12.566, 12.762, 12.958, 13.154, 13.355, 13.365, 13.38, 13.395, 13.41,
13.425, 13.44, 13.455, 13.47, 13.485, 13.49869, 7.424, 7.472, 7.496, 7.52,
7.544, 7.568, 7.592, 7.616, 7.641526, 7.678, 7.716, 7.754, 7.792, 7.83, 7.868,
7.906, 7.944, 7.982, 8.02042, 8.085, 8.15, 8.215, 8.28, 8.345, 8.41, 8.475,
8.54, 8.605, 8.666293, 7.663, 8.116, 11.196, 11.249, 11.302, 11.355, 11.408,
11.461, 11.514, 11.567, 11.61951, 12.205, 12.79, 13.375, 13.96, 14.545, 15.13,
15.715, 16.3, 16.885, 17.46967, 17.484, 17.498, 17.512, 17.526, 17.54, 17.554,
17.568, 17.582, 17.596, 17.61099, 4.495, 4.745, 4.995, 5.12, 5.245, 5.37,
5.495, 5.624713, 5.658, 5.696, 5.734, 5.772, 5.81, 5.848, 5.886, 5.924, 5.962,
6.004034, 6.305, 6.61, 6.915, 7.22, 7.525, 7.83, 8.135, 8.44, 8.745, 9.054572,
10.14, 10.43, 10.71782, 11.13, 11.335, 11.54, 11.745, 11.95, 12.77435, 14.808,
16.846, 18.884, 19.903, 20.922, 21.941, 22.95846, 37.338, 37.401, 37.527,
37.814, 38.038, 38.598, 39.258, 39.532, 40.354, 40.902, 41.44835, 23.081,
23.294, 23.507, 23.71729, 24.077, 24.434, 24.791, 25.148, 25.505, 25.862,
26.219, 26.576, 26.933, 27.2913, 27.299, 27.308, 27.317, 27.326, 27.335,
27.344, 27.353, 27.362, 27.371, 27.38188, 23.306, 23.788, 24.27268, 24.57,
25.17, 25.47, 25.77, 26.07, 26.37, 26.67, 26.97, 27.2698, 27.577, 27.884,
28.191, 28.498, 28.805, 29.112, 29.419, 29.726, 30.033, 30.34219, 16.134,
24.71, 24.71, 24.71, 24.71, 24.71, 24.71, 31.94, 32.16, 32.38, 32.6, 32.82,
33.26, 33.48, 33.70422, 34.137, 34.574, 35.011, 35.448, 36.322, 37.196, 37.633,
38.07169, 38.072, 38.076, 38.078, 38.08, 38.082, 38.084, 38.086, 38.088,
38.08755, 46.095, 46.406, 46.717, 47.028, 47.339, 47.65467, 23.06, 23.1,
23.14316, 23.622, 23.863, 24.104, 24.345, 24.586, 25.54903, 25.694, 25.838,
26.27, 26.414, 26.558, 26.702, 26.846, 26.99223, 23.826, 23.863, 23.90286,
24.322, 24.533, 24.744, 24.955, 25.166, 26.01203, 26.094, 26.178, 26.346,
26.514, 26.598, 26.682, 26.766, 26.8545, 14.18, 14.365, 14.54838, 15.208,
15.537, 15.866, 16.195, 16.524, 17.511, 17.84261, 18.423, 21.338, 21.921,
22.504, 23.66658, 19.814, 23.12309, 24.952, 25.41, 26.326, 27.242, 23.696,
24.058, 24.42368, 24.597, 24.774, 24.951, 25.128, 25.305, 25.482, 25.659,
25.836, 26.013, 26.19191, 26.201, 26.212, 26.234, 26.245, 26.256, 26.267,
26.278, 26.289, 26.30484, 23.55656, 24.102, 24.373, 24.644, 24.915, 25.186,
25.457, 25.728, 25.999, 26.27382, 26.632, 26.994, 27.356, 27.718, 28.08,
28.442, 29.166, 29.528, 29.88617, 30.124, 30.358, 30.826, 31.06, 31.294,
31.528, 31.762, 31.996, 32.22849, 2.742582, 2.774, 2.808, 2.842, 2.876, 2.91,
2.978, 3.046, 3.144, 3.272, 3.528, 3.656, 3.95, 4.41, 4.64, 4.87, 5.1, 5.33,
5.56, 5.79, 6.017525, 16.63745, 16.884, 17.128, 17.372, 17.494, 17.738,
17.86075, 18.016, 18.172, 18.328, 18.484, 18.796, 19.108, 19.42257, 19.64,
19.86, 20.08, 20.3, 20.52438, 9.794, 9.868, 9.942, 10.016, 10.09, 10.238,
10.386, 10.597, 10.871, 11.145, 11.282, 11.419, 11.556, 11.693, 11.82756,
11.97, 12.11, 12.39, 12.53, 12.67, 12.95, 13.09, 13.2299, 23.206, 26.195,
26.778, 27.944, 28.527, 29.10834, 29.503, 29.896, 30.682, 31.075, 31.468,
31.861, 32.254, 32.647, 33.04166, 21.83835, 21.946, 21.999, 22.052, 22.105,
22.158, 22.211, 22.264, 22.317, 22.368, 22.622, 22.874, 23.126, 23.378, 23.63,
23.882, 24.134, 24.386, 24.638, 24.88723, 24.933, 24.976, 25.062, 25.105,
25.148, 25.191, 25.234, 25.277, 25.32213, 18.59, 18.81, 19.03, 19.25, 19.47,
19.68829, 20.517, 21.344, 22.171, 22.998, 23.825, 24.652, 25.479, 26.306,
27.133, 27.96245, 28.009, 28.058, 28.107, 28.156, 28.205, 28.254, 28.303,
28.352, 28.401, 28.44787, 18.224, 18.652, 18.866, 19.08125, 19.785, 20.49,
21.9, 22.605, 23.31, 24.015, 24.72, 25.425, 26.13229) )

The error occurs as in y.hat[i] <- you allow a to take values from d (anything from 1,..,53 given in the data) which can be greater than the your upper bound for a (J = 44 given in the data).

Histogram from two vectors in Matlab

Thanks in advance for the help.
I have two sets of parallel vectors:
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 55];
x_count = [7721, 6475, 3890, 2138, 1152, 784, 674, 492, 424, 365, 309, 302, 232, 250, 220, 208, 190, 162, 144, 134, 97, 93, 89, 97, 92, 85, 77, 87, 64, 75, 72, 82, 61, 48, 46, 44, 35, 20, 28, 20, 21, 10, 6, 8, 4, 4, 4, 3, 1, 1];
y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 55];
y_count = [88, 40, 24, 12, 8, 5, 1, 1, 1, 100];
where x, y are the categories, and x_count, y_count are the frequency of each categories. x and y can be of unequal lengths, and need not contain the same categories.
I want to create a side-by-side bar/histogram plot, where the x-axis is the categories, placed side-by-side like this: side by side multiply histogram in matlab. The frequency counts go along the y-axis.
I've tried googling around, but still stuck on this. If someone could help, that would be great. The solution in side by side multiply histogram in matlab works only if x and y have the same length, but mine's not.

You can try this:
% create unique bins
bins = unique([x y]);
% create vectors with zeros same size as bins
xBins = zeros(size(bins));
yBins = zeros(size(bins));
% fill in counts in the respective spots
xBins(ismember(x, bins)) = x_count;
yBins(ismember(y, bins)) = y_count;
bar(bins, [xBins' yBins']);