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Scala LazyList of finite range


By : CsPyDev
Date : July 29 2020, 09:00 AM
will be helpful for those in need I've been trying to create a LazyList of a series of items, blocks in this case, which have to be accessed in a 3-dimensional array like fashion. , Something like this?
code :
for {
  x <- LazyList.range(min.x, max.x+1)
  y <- LazyList.range(min.y, max.y+1)
  z <- LazyList.range(min.z, max.z+1)
} yield (x, y, z)
//res0: LazyList[(Int, Int, Int)] = LazyList(<not computed>)


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Finite Growable Array in Scala


By : Shhhh
Date : March 29 2020, 07:55 AM
help you fix your problem As others have discussed, you want a ring buffer. However, you also have to decide if you actually want all of the collections methods or not, and if so, what happens when you filter a ring buffer of maximum size N--does it keep its maximum size, or what?
If you're okay with merely being able to view your ring buffer as part of the collections hierarchy (but don't want to use collections efficiently to generate new ring buffers) then you can just:
code :
class RingBuffer[T: ClassManifest](maxsize: Int) {
  private[this] val buffer = new Array[T](maxsize+1)
  private[this] var i0,i1 = 0
  private[this] def i0up = { i0 += 1; if (i0>=buffer.length) i0 -= buffer.length }
  private[this] def i0dn = { i0 -= 1; if (i0<0) i0 += buffer.length }
  private[this] def i1up = { i1 += 1; if (i1>=buffer.length) i1 -= buffer.length }
  private[this] def i1dn = { i1 -= 1; if (i1<0) i1 += buffer.length }   
  private[this] def me = this

  def apply(i: Int) = {
    val j = i+i0
    if (j >= buffer.length) buffer(j-buffer.length) else buffer(j)
  }
  def size = if (i1<i0) buffer.length+i1-i0 else i1-i0
  def :+(t: T) = {
    buffer(i1) = t
    i1up; if (i1==i0) i0up
    this
  }
  def +:(t: T) = {
    i0dn; if (i0==i1) i1dn
    buffer(i0) = t
    this
  }
  def popt = {
    if (i1==i0) throw new java.util.NoSuchElementException
    i1dn; buffer(i1)
  }
  def poph = {
    if (i1==i0) throw new java.util.NoSuchElementException
    val ans = buffer(i0); i0up; ans
  }
  def seqView = new IndexedSeq[T] {
    def apply(i: Int) = me(i)
    def length = me.size
  }
}
val r = new RingBuffer[Int](4)
r :+ 7 :+ 9 :+ 2
r.seqView.mkString(" ")    // Prints 7 9 2
r.popt                     // Returns 2
r.poph                     // Returns 7
r :+ 6 :+ 5 :+ 4 :+ 3
r.seqView.mkString(" ")    // Prints 6 5 4 3 -- 7 fell off the end
0 +: 1 +: 2 +: r
r.seqView.mkString(" ")    // Prints 0 1 2 6 -- added to front; 3,4,5 fell off
r.seqView.filter(_>1)      // Vector(2,6)
class RingBufferImplicit[T: ClassManifest](ts: Traversable[T]) {
  def ring(maxsize: Int) = {
    val rb = new RingBuffer[T](maxsize)
    ts.foreach(rb :+ _)
    rb
  }
}
implicit def traversable2ringbuffer[T: ClassManifest](ts: Traversable[T]) = {
  new RingBufferImplicit(ts)
}
val rr = List(1,2,3,4,5).ring(4)
rr.seqView.mkString(" ")            // Prints 2,3,4,5

Finite sparse collection in Scala


By : user2986488
Date : March 29 2020, 07:55 AM
I wish this help you For the record, I don't think this is worth optimizing at all, but just for kicks, you could do something like this, that would be really efficient memory-wise :)
code :
def toBase64(c: Char): Int = c match {
   case '+' => 63
   case '/' => 64
   case x if 'A' to 'Z' contains x => c - 'A'
   case x if 'a' to 'z' contains x => c - 'a' + 26
   case x if '0' to '9' contains x => c - '0' + 52
   case _ => -1
}

ggplot2 error message: Error in seq.default(range[1], range[2], length.out = nframes) : 'from' must be a finite number


By : mysee1989
Date : March 29 2020, 07:55 AM
I wish this helpful for you The problem is that the transition_time function needs to receive a variable in your data that tells it what year it is, but you haven't given it one.
Essentially, your data is in the wrong format for this. You need to start by switching from a wide-format dataframe to a long-format dataframe.
code :
library(tidyr)
library(dplyr)
library(ggplot2)
library(gganimate)

df <- europe.gdp                                                    %>% 
      group_by(`Country/Region`)                                    %>%
      gather(key = "Year", value = "CO2", 
             -`Country/Region`, -`GDP per Capita`, -CO2.per.capita) %>% 
      as.data.frame
df$GDP <- rep(europe.gdp$`GDP per Capita`, 40)
df$Year <- as.numeric(df$Year)
ggplot(data = df, 
       aes(x     = GDP, 
           y     = CO2, 
           size  = CO2, 
           color = `Country/Region`)) + 
  geom_point()                        + 
  scale_x_log10()                     +
  theme_bw()                          +
  labs(title = 'Year: {frame_time}', 
       x     = 'GDP per capita', 
       y     = 'CO2 emissions/ tonnes per capita') + 
  transition_time(Year) + ease_aes('linear')
europe.gdp <- structure(list(`Country/Region` = c("Albania", "Austria", "Belarus", 
"Belgium", "Bosnia", "Bulgaria", "Croatia", "Cyprus", "Czech Republic", 
"Denmark", "Estonia", "Finland", "France", "Germany"), `1971` = c(3.9, 
48.7, 0, 116.8, 0, 62.8, 0, 1.8, 151, 55, 0, 39.8, 431.9, 978.6
), `1972` = c(4.5, 50.5, 0, 126.7, 0, 64.8, 0, 2.2, 150, 57.1, 
0, 43.7, 448.6, 1003.2), `1973` = c(3.9, 54, 0, 132.7, 0, 66.6, 
0, 2.3, 147.1, 56, 0, 48, 484.8, 1053.1), `1974` = c(4.2, 51.3, 
0, 130.6, 0, 67.7, 0, 1.8, 146.3, 49.8, 0, 44.5, 464.6, 1028.5
), `1975` = c(4.5, 50.2, 0, 115.6, 0, 72.2, 0, 1.7, 152.6, 52.5, 
0, 44.4, 430.6, 975.5), `1976` = c(4.9, 54.3, 0, 124.5, 0, 72.1, 
0, 2, 157.4, 58.1, 0, 50.5, 469.3, 1032.2), `1977` = c(5.2, 51.8, 
0, 123.5, 0, 74.8, 0, 2.1, 166.9, 59.7, 0, 50.2, 455.3, 1017.2
), `1978` = c(6.2, 54.5, 0, 129, 0, 77.9, 0, 2.3, 163, 59.2, 
0, 54.7, 474.7, 1055.9), `1979` = c(7.5, 57.2, 0, 132.3, 0, 81.1, 
0, 2.5, 172.5, 62.7, 0, 54.4, 481.8, 1103.6), `1980` = c(7.6, 
55.7, 0, 125.7, 0, 83.8, 0, 2.6, 165.8, 62.5, 0, 55.2, 461.4, 
1055.6), `1981` = c(6.4, 52.8, 0, 115.5, 0, 79.9, 0, 2.5, 166.5, 
52.5, 0, 46, 414.1, 1022.3), `1982` = c(6.7, 51, 0, 109.3, 0, 
81.5, 0, 2.6, 169.3, 54.6, 0, 44.5, 396.7, 982.3), `1983` = c(7.3, 
51.1, 0, 100.6, 0, 80.2, 0, 2.7, 170.5, 51.3, 0, 43.2, 381, 983.9
), `1984` = c(7.6, 52.9, 0, 102.6, 0, 78.3, 0, 2.8, 173.1, 52.9, 
0, 44.4, 369.5, 1006.1), `1985` = c(7.2, 54.3, 0, 101.9, 0, 81.1, 
0, 2.8, 173.1, 60.5, 0, 48.6, 360.3, 1014.6), `1986` = c(7.2, 
53.2, 0, 102.6, 0, 82.1, 0, 3.1, 173.1, 61.1, 0, 49.5, 347.8, 
1016.3), `1987` = c(7.5, 54.2, 0, 102.8, 0, 83.1, 0, 3.6, 174.2, 
59.3, 0, 53.8, 342.3, 1007.2), `1988` = c(7.6, 52.1, 0, 104.6, 
0, 82.1, 0, 3.6, 170.8, 55.5, 0, 53.1, 340.5, 1001.2), `1989` = c(7.2, 
52.5, 0, 105.9, 0, 81.4, 0, 3.8, 163.5, 49.8, 0, 52.9, 355.9, 
976.8), `1990` = c(6.3, 56.4, 124.5, 107.9, 23.7, 74.8, 21.6, 
3.8, 155.1, 50.4, 36.1, 54.4, 352.3, 949.7), `1991` = c(4.4, 
60.6, 119.4, 113.3, 21.2, 56.4, 15.7, 4.4, 140.9, 60.5, 32.1, 
55.9, 379.6, 924.8), `1992` = c(2.8, 55.7, 98.8, 112.3, 15.6, 
54.1, 15.2, 4.7, 131.4, 54.8, 23.5, 53.7, 368, 886.5), `1993` = c(2.3, 
56, 82.9, 109.8, 13.1, 55.1, 15.8, 4.9, 126.7, 57.1, 18, 54.8, 
348.9, 879.9), `1994` = c(2.3, 56.2, 70.2, 115.5, 3, 52.5, 15, 
5.3, 120.2, 61, 17.8, 61.4, 344.4, 868.5), `1995` = c(1.9, 59.4, 
61.4, 115.2, 3.2, 53.2, 15.8, 5.2, 123.7, 58, 16.1, 56, 353.8, 
867.8), `1996` = c(1.9, 63.1, 62.7, 121.3, 4.1, 53.8, 15.6, 5.5, 
125.6, 71.2, 17, 62.2, 368.6, 896.5), `1997` = c(1.4, 62.4, 61.8, 
118.5, 8.3, 50.9, 17.3, 5.7, 124, 61.6, 16.5, 60.1, 361.7, 865.8
), `1998` = c(1.7, 62.9, 59.3, 120.9, 10.5, 48.7, 18.4, 5.8, 
117.6, 57.7, 16, 56.8, 385.3, 858.9), `1999` = c(3, 61.4, 57.6, 
117.4, 10.2, 42.8, 18.3, 6, 110.9, 54.6, 14.9, 56.1, 377.7, 826.9
), `2000` = c(3.1, 61.7, 58.7, 118.6, 13.5, 42.1, 17.7, 6.3, 
121.9, 50.6, 14.6, 55.1, 376.9, 825), `2001` = c(3.3, 65.9, 57.8, 
119.1, 13.3, 44.8, 18.6, 6.2, 121.4, 52.2, 15.1, 60.3, 383.8, 
843.3), `2002` = c(3.8, 67.4, 59.2, 111.9, 14, 42, 19.6, 6.3, 
117.2, 51.9, 14.6, 63, 375.9, 830.7), `2003` = c(4, 72.6, 60.7, 
119.5, 14.3, 46.3, 21, 7, 120.7, 57.1, 16.6, 70.8, 385.2, 839.8
), `2004` = c(4.3, 73.7, 63, 116.5, 15, 45.4, 20.4, 6.9, 121.8, 
51.6, 16.7, 67.2, 385.4, 840.8), `2005` = c(4.1, 74.6, 62.1, 
112.6, 15.6, 45.9, 20.8, 7, 119.6, 48.3, 16.9, 55.2, 388.4, 809
), `2006` = c(4, 72.5, 66.2, 109.6, 17.2, 47.3, 20.8, 7.1, 120.7, 
56, 15.5, 66.8, 379.6, 820.9), `2007` = c(4, 70, 64, 105.6, 18.2, 
50.4, 22.1, 7.3, 122, 51.4, 19.3, 65, 373.1, 796.3), `2008` = c(3.9, 
70.6, 64.5, 111, 19.9, 49, 21, 7.6, 117.3, 48.4, 17.7, 57, 370.2, 
800.1), `2009` = c(3.5, 63.5, 62.3, 100.7, 19.4, 42.2, 19.8, 
7.5, 110.1, 46.7, 14.7, 55, 351.4, 747.1), `2010` = c(3.8, 69.3, 
65.3, 106.4, 19.9, 43.8, 19, 7.2, 114.5, 47, 18.5, 62.9, 357.8, 
761.6), `GDP per Capita` = c(5626, 56259, 6575, 51237, 6140, 
9811, 15533, 30521, 26114, 66196, 25260, 54869, 46493, 53276), 
    CO2.per.capita = c(186.9, 2358.2, 1482.4, 4586.8, 293.2, 
    2495, 389.5, 176.5, 5690, 2218.2, 388.2, 2171.1, 15679.1, 
    37054)), row.names = c(NA, -14L), class = "data.frame")

MLE error in R: non-finite finite-difference value/ value in 'vmmin' is not finite


By : Grant Redfearn
Date : March 29 2020, 07:55 AM
wish of those help Your Loss variable is negative. In R, raising negative values to a fractional power (i.e. set$Loss^alpha where alpha is non-integer) returns NaN values. (The only general alternative is to return a complex-valued answer, which you probably don't want.) Did you mean to code Loss as positive rather than negative? Or maybe you want -abs(set$Loss^alpha) ?
As a general purpose debugging tip, it helps to add

What's the difference between LazyList and Stream in Scala?


By : Wolfgang Laval
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further NthPortal, a contributor to LazyList, states in Update and improve LazyList docs #7842
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