forked from kscience/kmath
fix problem with imports
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@ -10,7 +10,6 @@ import space.kscience.kmath.nd.ShapeND
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.nd.component1
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import space.kscience.kmath.nd.component1
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
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@ -135,7 +134,7 @@ class TestLmAlgorithm {
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assertEquals(1, result.example_number)
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assertEquals(1, result.example_number)
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assertEquals(0.9131368192633, (result.result_chi_sq * 1e13).roundToLong() / 1e13)
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assertEquals(0.9131368192633, (result.result_chi_sq * 1e13).roundToLong() / 1e13)
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assertEquals(3.7790980 * 1e-7, (result.result_lambda * 1e13).roundToLong() / 1e13)
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assertEquals(3.7790980 * 1e-7, (result.result_lambda * 1e13).roundToLong() / 1e13)
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assertEquals(result.typeOfConvergence, LinearOpsTensorAlgebra.TypeOfConvergence.inParameters)
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assertEquals(result.typeOfConvergence, TypeOfConvergence.inParameters)
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val expectedParameters = BroadcastDoubleTensorAlgebra.fromArray(
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val expectedParameters = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(20.527230909086, 9.833627103230, 0.997571256572, 50.174445822506)
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(20.527230909086, 9.833627103230, 0.997571256572, 50.174445822506)
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).as2D()
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).as2D()
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