initial commit

matrix_test.nim

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+from random import randomize, random
+from utils import `...`
+import matrix
+from matrix import det
+from vector import createVector, newVector, `-`, abs, norm
+import unittest
+
+suite "Nim linear algebra library":
+    echo "suite setup: run once before the tests"
+    randomize()
+    var mtx : Matrix[float64]
+  
+    setup:
+        echo "run before each test"
+        let DIM = 100
+        var numbers = newSeq[float64](DIM * DIM)
+        for i in 0...len(numbers):
+            numbers[i] = float64(random(-DIM..DIM))
+        mtx = newMatrix[float64](DIM,DIM,numbers)
+  
+    teardown:
+        echo "run after each test"
+
+    test "LU decomposition":
+        var lu = mtx.clone()
+        let pivot = lu.lu()
+
+    test "Linear system solve":
+        # let nums = [2.0,1.0,3.0,2.0,6.0,8.0,6.0,8.0,18.0]
+        # let nums = [-3.0, -1.0, 0.0, -2.0, 0.0, 2.0, -3.0, -1.0, 0.0]
+        let DIM = 100
+        var numbers = newSeq[float64](DIM * DIM)
+        for i in 0...len(numbers):
+            numbers[i] = float64(random(-DIM..DIM))
+
+        var mtx = newMatrix[float64](DIM,DIM,numbers)
+        var lu = mtx.clone()
+        let pivot = lu.lu()
+        var b = newVector[float64](DIM)
+        for i in 0...len(b):
+            b[i] = float64(random(DIM))
+
+        let x = lu.lu_solve(b, pivot)
+        let error  = (mtx * x) - b
+
+        check(error.norm() < 1e-5)
+        # var mtx2 = mtx.clone
+        # for i in 0...10000:
+        #     var add  = random(-DIM..DIM).float64()
+        #     discard mtx2 += add
+
+        # echo mtx
+        # mtx[1,1] += 10000.0
+        # echo mtx
+        # echo mtx.det()
+
+        # echo lu
+
+        # give up and stop if this fails
+  
+    echo "suite teardown: run once after the tests"
+
+# let m1 = newMatrix[float](3,3,[1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0])
+# var m2 = m1.clone()
+# let m3 = m2.clone()
+# m2[2,1] = -25
+# echo m1 + m2
+# echo m2
+# echo m1.det()
+
+# let nums = [-63, 3, 70, -23, 55,
+#         -100, -37, 81, -98, 84,
+#         -36, -45, -70, 98, -18,
+#         -15, 92, 82, 85, -2,
+#         45, 54, -22, 27, 0
+# ]
+
+# let s = nums.map(proc(n : int) : float = float(n))
+# let s2 = nums.map(n => float(n))
+
+# let m4 = newMatrix[float](5,5,s)
+# echo m4
+# echo m4.det
+
+# var m5 = m4.clone()
+# var pivot = m5.lu()
+# let b = createVector[float](1.0,2.0,3.0,4.0,5.0)
+# let x = m5.lu_solve(b, pivot)
+# echo x
+# proc new(T: typedesc): ref T =
+#   echo "allocating "
+#   new(result)
+
+# var n = new Vector[int]
+
+# type
+#   Index = distinct int
+
+# proc `==` (a, b: Index): bool {.borrow.}
+
+# var af = (0, 0.Index)
+# var b = (0, 0.Index)
+
+# echo af == b # works!
+
+
+# type Person = ref object of RootObj
+#     name : string
+#     age : int
+
+# type Employee = ref object of Person
+#     salary: int
+
+
+# type RecordType = tuple or object
+
+# proc printFields(rec: RecordType) =
+#   for key, value in fieldPairs(rec):
+#     echo key, " = ", value
+
+# proc printFields(rec: ref[RecordType]) =
+#   for key, value in fieldPairs(rec[]):
+#     echo key, " = ", value
+
+
+# let p = Person(name : "Walter", age : 28)
+# let e = Employee(name : "Walter", age : 28, salary: 45000)
+
+# let people : seq[Person] = @[p, e]
+
+# for person in people:
+#     printFields person
+
+# let DIM = 5
+# var numbers = newSeq[float](DIM * DIM)
+# for i in 0...len(numbers):
+#     numbers[i] = float(random(DIM))
+
+# let m = newMatrix[float](DIM,DIM, numbers)
+
+# var m2 = new(Matrix[float])
+# m2[] = m.clone()
+# echo m2[]
+# m2[][0,0] = 300
+# echo m2[]
+
+# let inverse = m2[].invert()
+
+# echo m
+# echo m.det
+# echo m * inverse
+# echo inverse * m
+
+# let nums = [2.0,1.0,3.0,2.0,6.0,8.0,6.0,8.0,18.0]
+# let nums = [-3.0, -1.0, 0.0, -2.0, 0.0, 2.0, -3.0, -1.0, 0.0]
+# let DIM = 600
+# var numbers = newSeq[float64](DIM * DIM)
+# for i in 0...len(numbers):
+#     numbers[i] = float64(random(-DIM..DIM))
+
+# var mtx = newMatrix[float64](DIM,DIM,numbers)
+# var mtx2 = mtx.clone
+# for i in 0...10000:
+#     var add  = random(-DIM..DIM).float64()
+#     discard mtx2 += add
+
+# echo mtx
+# mtx[1,1] += 10000.0
+# echo mtx
+# echo mtx.det()
+
+# var lu = mtx.clone()
+# let pivot = lu.lu()
+# # echo lu
+
+# var b = newVector[float64](DIM)
+# for i in 0...len(b):
+#     b[i] = float64(random(DIM))
+
+# let x = lu.lu_solve(b, pivot)
+# let error  = (mtx * x) - b
+
+# echo abs(error)