This notebook uses the Accord library for math, statistics, vision, and machine learning. It should be split up into many notebooks, but this is currently for testing.
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from System import Array
import Accord
import Accord.Math
import clr
clr.ImportExtensions(Accord.Math)
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Accord.Math.Matrix.Indices(0, 10)
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from Accord.Math import Matrix
v = Matrix.Identity(3)
v
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I = Array.CreateInstance(float, 2, 2)
I[0,0] = 1
I
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u = Array[float]([1, 6])
v = Array[float]([-2, 10])
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u.InnerProduct(v)
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outer = u.OuterProduct(v)
outer
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I.Multiply(u)
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I.Multiply(v)
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from Accord.Math.Decompositions import SingularValueDecomposition
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i = SingularValueDecomposition(I)
i.Diagonal
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Principal Component Analysis Example¶
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from Processing import *
size(500, 500)
background(0)
smooth()
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points = []
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def drawPoint():
fill(255, 0, 0)
noStroke()
points.append([mouseX(), mouseY()])
ellipse(mouseX(), mouseY(), 5, 5)
onMouseDragged += drawPoint
loop()
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def clear():
global points
points = []
background(0)
onKeyPressed += clear
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points
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data = Array.CreateInstance(float, len(points), len(points[0]))
for i in range(len(points)):
for j in range(len(points[i])):
data[i,j] = points[i][j]
print("# of points: ", data.Length)
from Accord.Statistics.Analysis import *
analysis = DescriptiveAnalysis(data)
ux,uy = analysis.Means
pca = PrincipalComponentAnalysis(data, AnalysisMethod.Center)
# Compute the Principal Component Analysis
pca.Compute()
stroke(0, 255,0)
strokeWeight(5)
for pc in pca.Components:
x,y = pc.Eigenvector.Multiply(pc.Eigenvalue**0.5)
line(ux - x, uy - y, ux + x, uy + y)
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get()
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for v in analysis.CovarianceMatrix:
print(v)
Accord Image Processing¶
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from AForge.Imaging.Filters import *
from Accord.Imaging.Filters import *
from AForge.Imaging import Image
from Graphics import *
from System.Drawing import Bitmap
from System.Drawing.Imaging import PixelFormat
p = Picture("http://roboteducation.org/images/header_home.jpg")
image = p.toBitmap() #Image.Clone(p.toBitmap(), PixelFormat.Format24bppRgb)
calico.display(p)
# Create a new Gabor filter
filter = GaborFilter()
# Apply the filter
output = filter.Apply(image)
calico.display(Picture(output))
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filter = GaussianBlur( 4, 11 )
output = filter.Apply( image )
calico.display(Picture(image))
calico.display(Picture(output))