Fix typos (#74)

README.md

@@ -132,7 +132,7 @@ alpha colors, this means the RGB values are lost (set to 0) and it's impossible
 to recover them. In such a case `MakeColor` will return `false` as its second value.
 
 ### Comparing colors
-In the RGB color space, the Euclidian distance between colors *doesn't* correspond
+In the RGB color space, the Euclidean distance between colors *doesn't* correspond
 to visual/perceptual distance. This means that two pairs of colors which have the
 same distance in RGB space can look much further apart. This is fixed by the
 CIE-L\*a\*b\*, CIE-L\*u\*v\* and CIE-L\*C\*h° color spaces.
@@ -201,7 +201,7 @@ What you see is that HSV is really bad: it adds some green, which is not present
 in the original colors at all! RGB is much better, but it stays light a little
 too long. LUV and LAB both hit the right lightness but LAB has a little more
 color. HCL works in the same vein as HSV (both cylindrical interpolations) but
-it does it right in that there is no green appearing and the lighthness changes
+it does it right in that there is no green appearing and the lightness changes
 in a linear manner.
 
 While this seems all good, you need to know one thing: When interpolating in any
@@ -309,11 +309,11 @@ generating this picture in `doc/colorgens/colorgens.go`.
 
 ### Getting random palettes
 As soon as you need to generate more than one random color, you probably want
-them to be distinguishible. Playing against an opponent which has almost the
+them to be distinguishable. Playing against an opponent which has almost the
 same blue as I do is not fun. This is where random palettes can help.
 
 These palettes are generated using an algorithm which ensures that all colors
-on the palette are as distinguishible as possible. Again, there is a `Fast`
+on the palette are as distinguishable as possible. Again, there is a `Fast`
 method which works in HSV and is less perceptually uniform and a non-`Fast`
 method which works in CIE spaces. For more theory on `SoftPalette`, check out
 [I want hue](http://tools.medialab.sciences-po.fr/iwanthue/theory.php). Yet

hsluv.go

@@ -116,7 +116,7 @@ func (col Color) HPLuv() (h, s, l float64) {
 	return LuvLChToHPLuv(col.LuvLChWhiteRef(hSLuvD65))
 }
 
-// DistanceHSLuv calculates Euclidan distance in the HSLuv colorspace. No idea
+// DistanceHSLuv calculates Euclidean distance in the HSLuv colorspace. No idea
 // how useful this is.
 //
 // The Hue value is divided by 100 before the calculation, so that H, S, and L

soft_palettegen.go

@@ -78,7 +78,7 @@ func SoftPaletteExWithRand(colorsCount int, settings SoftPaletteSettings, rand R
 
 	// The actual k-means/medoid iterations
 	for i := 0; i < settings.Iterations; i++ {
-		// Reassing the samples to clusters, i.e. to their closest mean.
+		// Reassigning the samples to clusters, i.e. to their closest mean.
 		// By the way, also check if any sample is used as a medoid and if so, mark that.
 		for isample, sample := range samples {
 			samples_used[isample] = false
@@ -99,7 +99,7 @@ func SoftPaletteExWithRand(colorsCount int, settings SoftPaletteSettings, rand R
 
 		// Compute new means according to the samples.
 		for imean := range means {
-			// The new mean is the average of all samples belonging to it..
+			// The new mean is the average of all samples belonging to it.
 			nsamples := 0
 			newmean := lab_t{0.0, 0.0, 0.0}
 			for isample, sample := range samples {