Repository: https://github.com/nok/sklearn-porter
Documentation: sklearn.svm.NuSVC
In [1]:
import sys
sys.path.append('../../../../..')
In [2]:
from sklearn.datasets import load_iris
iris_data = load_iris()
X = iris_data.data
y = iris_data.target
print(X.shape, y.shape)
((150, 4), (150,))
In [3]:
from sklearn import svm
clf = svm.NuSVC(gamma=0.001, kernel='rbf', random_state=0)
clf.fit(X, y)
Out[3]:
NuSVC(cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',
max_iter=-1, nu=0.5, probability=False, random_state=0, shrinking=True,
tol=0.001, verbose=False)
In [4]:
from sklearn_porter import Porter
porter = Porter(clf, language='ruby')
output = porter.export()
print(output)
class NuSVC
def initialize (nClasses, nRows, vectors, coefficients, intercepts, weights, kernel, gamma, coef0, degree)
@nClasses = nClasses
@classes = Array.new(nClasses)
for i in 0 ... nClasses
@classes[i] = i
end
@nRows = nRows
@vectors = vectors
@coefficients = coefficients
@intercepts = intercepts
@weights = weights
@kernel = kernel.upcase
@gamma = gamma
@coef0 = coef0
@degree = degree
end
def predict (features)
kernels = Array.new(@vectors.length)
case @kernel
when "LINEAR"
for i in 0 ... @vectors.length
kernel = 0
for j in 0 ... @vectors[i].length
kernel += @vectors[i][j] * features[j]
end
kernels[i] = kernel
end
when 'POLY'
for i in 0 ... @vectors.length
kernel = 0
for j in 0 ... @vectors[i].length
kernel += @vectors[i][j] * features[j]
end
kernels[i] = (((@gamma * kernel) + @coef0) ** @degree)
end
when "RBF"
for i in 0 ... @vectors.length
kernel = 0
for j in 0 ... @vectors[i].length
kernel += ((@vectors[i][j] - features[j]) ** 2)
end
kernels[i] = Math.exp(-@gamma * kernel)
end
when 'SIGMOID'
for i in 0 ... @vectors.length
kernel = 0
for j in 0 ... @vectors[i].length
kernel += @vectors[i][j] * features[j]
end
kernels[i] = Math.tanh((@gamma * kernel) + @coef0)
end
end
starts = Array.new(@nRows, 0)
for i in 0 ... @nRows
if i != 0
start = 0
for j in 0 ... i
start += @weights[j]
end
starts[i] = start
else
starts[0] = 0
end
end
ends = Array.new(@nRows, 0)
for i in 0 ... @nRows
ends[i] = @weights[i] + starts[i]
end
if @nClasses == 2
for i in 0 ... kernels.length
kernels[i] = -kernels[i]
end
decision = 0
for k in starts[1] ... ends[1]
decision += kernels[k] * @coefficients[0][k]
end
for k in starts[0] ... ends[0]
decision += kernels[k] * @coefficients[0][k]
end
decision += @intercepts[0];
if decision > 0
return 0
end
return 1
end
decisions = Array.new(@intercepts.length, 0)
d = 0
for i in 0 ... @nRows
for j in i + 1 ... @nRows
tmp = 0
for k in starts[j] ... ends[j]
tmp += @coefficients[i][k] * kernels[k]
end
for k in starts[i] ... ends[i]
tmp += @coefficients[j - 1][k] * kernels[k]
end
decisions[d] = tmp + @intercepts[d]
d = d + 1
end
end
votes = Array.new(@intercepts.length, 0)
d = 0
for i in 0 ... @nRows
for j in i + 1 ... @nRows
votes[d] = decisions[d] > 0 ? i : j
d = d + 1
end
end
amounts = Array.new(@nClasses, 0)
for i in 0 ... votes.length
amounts[votes[i]] += 1
end
classVal = -1
classIdx = -1
for i in 0 ... amounts.length
if amounts[i] > classVal
classVal = amounts[i]
classIdx = i
end
end
return @classes[classIdx]
end
end
if ARGV.length == 4
# Features:
features = ARGV.collect { |i| i.to_f }
# Parameters:
vectors = [[4.9, 3.0, 1.4, 0.2], [4.6, 3.1, 1.5, 0.2], [5.4, 3.9, 1.7, 0.4], [5.0, 3.4, 1.5, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [5.7, 4.4, 1.5, 0.4], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5.0, 3.0, 1.6, 0.2], [5.0, 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [4.9, 3.1, 1.5, 0.2], [5.1, 3.4, 1.5, 0.2], [4.5, 2.3, 1.3, 0.3], [5.0, 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3.0, 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [5.3, 3.7, 1.5, 0.2], [7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4.0, 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1.0], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5.0, 2.0, 3.5, 1.0], [5.9, 3.0, 4.2, 1.5], [6.0, 2.2, 4.0, 1.0], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3.0, 4.5, 1.5], [5.8, 2.7, 4.1, 1.0], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4.0, 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.6, 3.0, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3.0, 5.0, 1.7], [6.0, 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1.0], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1.0], [5.8, 2.7, 3.9, 1.2], [6.0, 2.7, 5.1, 1.6], [5.4, 3.0, 4.5, 1.5], [6.0, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3.0, 4.1, 1.3], [5.5, 2.5, 4.0, 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3.0, 4.6, 1.4], [5.8, 2.6, 4.0, 1.2], [5.0, 2.3, 3.3, 1.0], [5.6, 2.7, 4.2, 1.3], [5.7, 3.0, 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3.0, 1.1], [5.7, 2.8, 4.1, 1.3], [5.8, 2.7, 5.1, 1.9], [6.3, 2.9, 5.6, 1.8], [4.9, 2.5, 4.5, 1.7], [6.5, 3.2, 5.1, 2.0], [6.4, 2.7, 5.3, 1.9], [5.7, 2.5, 5.0, 2.0], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3.0, 5.5, 1.8], [6.0, 2.2, 5.0, 1.5], [5.6, 2.8, 4.9, 2.0], [6.3, 2.7, 4.9, 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3.0, 4.9, 1.8], [7.2, 3.0, 5.8, 1.6], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [6.4, 3.1, 5.5, 1.8], [6.0, 3.0, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.7, 3.0, 5.2, 2.3], [6.3, 2.5, 5.0, 1.9], [6.5, 3.0, 5.2, 2.0], [6.2, 3.4, 5.4, 2.3], [5.9, 3.0, 5.1, 1.8]]
coefficients = [[4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 0.0, 4.680538527007988, 0.0, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 4.680538527007988, 0.0, -0.0, -0.0, -0.0, -4.680538527007988, -0.0, -0.0, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -4.680538527007988, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -0.0, -0.0, -0.0, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -0.0, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -4.680538527007988, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -0.0, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -0.0, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366, -2.1228182659346366], [0.0, 0.0, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 0.0, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 2.1228182659346366, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 47.52934177369389, 47.52934177369389, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -0.0, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -47.52934177369389, -0.0, -47.52934177369389]]
intercepts = [0.09572808365772528, 0.049757317370245795, -0.08418168966801846]
weights = [28, 49, 27]
# Prediction:
clf = NuSVC.new 3, 3, vectors, coefficients, intercepts, weights, "rbf", 0.001, 0.0, 3
estimation = clf.predict features
puts estimation
end
Content source: nok/sklearn-porter
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