<p> Minimize Conjugate-Gradient </p>

0b53a3ea-c7fb-49d2-948d-a6849b901ddb 12 4 5 decimal

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Two segments ... two DE Oxyhemoglobin

α,β from Smath => Genfit Oxyhemoglobin Reconciliation The last argument sets itself as the title of the Formated Label XY 0 1.94 3.88 5.81 8.53 12.79 16.28 19.38 23.64 31.01 40.7 50 62.02 72.87 79.07 92.64 0 1.37 4.57 8.22 14.15 26.94 40.18 53.42 66.67 79.91 89.04 93.61 95.89 96.8 97.26 97.72 2 16 mat transpose : X XY 1 col : Y XY 2 col : 1 2 mat x α xfr_1 α 4 el x α 3 el - Heaviside * α 2 el - α 1 el + α 1 el x α 3 el - ( - α 1 el / exp * - α 2 el x α 3 el - ( - α 2 el / exp * + α 2 el α 1 el - / * - : x β xfr_2 β 4 el x β 3 el - Heaviside * β 2 el - β 1 el + β 1 el x β 3 el - ( - β 1 el / exp * - β 2 el x β 3 el - ( - β 2 el / exp * + β 2 el β 1 el - / * - : α 12.5 11.423 0 99 1 4 mat transpose : β 9 8.75 3.15 97 1 4 mat transpose : 1 2 mat X1 x α xfr_1 x β xfr_2 - 0 ≡ x 0 20 solve : 0 12.79 2 1 mat = Y1 X1 2 el α xfr_1 ( : 28.705 = 2 1 line i 1 X rows range p i el . : for data X Y p augment : 2 1 line x Oxyh x α xfr_1 0 x ≤ ( x X1 2 el ≤ ( & x β xfr_2 X1 2 el x ≤ ( x 100 ≤ ( & N/A cases : a 0 : b 100 : 1 2 mat Σ x Oxyh x a b int : 7708.5 = slide graph in else user note resume prescription:Pharmacist Oxyhemoglobin fit patient: JoBlo 17 1 line

Oxyhemoglobin fit to patient

data x Oxyh X1 2 el Y1 + 100 green 1 5 mat 3 1 sys

Σ 7708.5

x Oxyh x a X1 2 el int 145.6

data 00 0.78 0.46 1.94 1.37 2.33 2.28 3.1 3.2 3.88 4.57 4.65 5.94 5.43 7.31 5.81 8.22 6.59 9.59 7.36 11.42 8.53 14.15 8.53 14.15 12.79 26.94 16.28 40.18 19.38 53.42 23.64 66.67 31.01 79.91 40.7 89.04 50 93.61 62.02 95.89 72.87 96.8 79.07 97.26 92.64 97.72 242 mat :

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

X data 1 col : Y data 2 col : 12 mat support data . 12 black plot : 21 mat

x α xfr_1 α 4 el x α 3 el - Heaviside * α 2 el - α 1 el + α 1 el x α 3 el - (- α 1 el / exp * - α 2 el x α 3 el - (- α 2 el / exp * + α 2 el α 1 el - / * - :

x β xfr_2 β 4 el x β 3 el - Heaviside * β 2 el - β 1 el + β 1 el x β 3 el - (- β 1 el / exp * - β 2 el x β 3 el - (- β 2 el / exp * + β 2 el β 1 el - / * - :

X1 x α xfr_1 x β xfr_2 - 0 ≡ x 020 solve : 0 12.79 21 mat = Y1 X1 2 el α xfr_1 (: 28.705 = 21 line

α 12.5 11.423 09914 mat transpose : β 9 8.75 3.15 9714 mat transpose : 12 mat

piecewise from reconciliation wrt unique model

x α xfr_1 x β xfr_2 support X1 2 el Y1 + 50 black 1 5 mat 4 1 sys

The primitive low range

<p> Minimize Conjugate-Gradient </p>

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

init iter Minimize XY f φ β LS X XY 1 col : Y XY 2 col : n XY rows : k x β φ length 1 - : vx-> raw initial vector fit i 1 X length range vx i el X i el β f eval : for M-> Cholesky GradientMatrix j 1 k range i 1 n range M i j el X i el β φ j 1 + el eval : for for CholeskySolver LeastSquares Φ M transpose M * : d M transpose Y vx - (* : Δβ Φ 1 -^eval d * : b β Δβ + : 131 line : β 1 el init : i 1 : i iter ≤ β i 1 + el XY f φ β i el LS : i i 1 + : 21 line while β β iter el : 41 line β 31 line :

Vector Gradient ... Optimiz Symbolic
seemingly a scalar kernel  not 
recognized in program loop x β f n φ ct 1 : i 1 n range PD ct el x β f (β ct el diff : stack f(x,β) & PD V 1 el x β f : V i 1 + el PD i el : 21 line ct ct 1 + : 41 line for V 31 line :

Error stem plot for QuickPlot unicolor data Error stemX 1 data rows range : i 1 stemX rows range stem i el data i 1 el data i 2 el data i 1 el 022 mat : for i 1 stem rows 1 - range stem i el stem i el stem i 1 + (1 el 1 el 012 mat stack : for V stem 1 el : j 2 stem rows 1 - range V V stem j el stack : for V V data data rows row stack : 71 line :

L H N xd U 0 : dx H L - N / : i 1 N 1 + range U i el L dx - (dx i * + : for U 41 line :

Bar plot unicolor style, bar width user. data dt BarSize v data 2 col : t v bar t dt - 0 t dt - v t dt + v t dt + 042 mat : j 1 data rows range j 1 ≡ B data j 1 el data j 2 el bar : B B data j 1 el data j 2 el bar stack : if 11 line for B 41 line :

corr Pearson correlation χ X Mean : μ Y Mean : n XY rows : 13 mat X i el χ - (Y i el μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum Y i el μ - (2^(i 1 n sum * sqrt / X i el χ - (X i el β f eval μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum X i el β f eval μ - (2^(i 1 n sum * sqrt / / 31 line :

correlation between [X,Y data]

correlation ... [X,fitted model]

Absolute  residuals observer
fetches β from calculated δ j 1 XY rows range Δ j el Y j el X j el β f - eval : for X Δ augment 21 line :

SSD = "Sum Square Differences" SSD Y i el X i el β f - (2^(i 1 XY rows sum :

XY 00 0.78 0.46 1.94 1.37 2.33 2.28 3.1 3.2 3.88 4.57 4.65 5.94 5.43 7.31 5.81 8.22 6.59 9.59 7.36 11.42 8.53 14.15 122 mat :

X XY 1 col : Y XY 2 col : 12 mat data XY . 12 black plot : n XY rows : 31 line

Model function x β f β 1 el β 2 el β 3 el x β 4 el - (* - exp - exp * + :

<= ConjugateGradient Vector Fnct x β φ x β f 4 φ :

init 050 0.1 1014 mat transpose :

L 0 : H 10 : N 199 : 13 mat U L H N xd : 21 line

iter 7 :

algo solving for β β init iter Minimize :

assign/isolate .. β β :

corr 0.9995

Absolute  residuals δ j 1 XY rows range Δ j el Y j el X j el β f - eval : for X Δ augment 21 line :

FIT range, populate i 1 U rows range vy i el U i el β f eval : for U vy augment 31 line :

β 1.3666 - 41.0463 0.1488 8.3748 41 mat

0 β f 0.1 -

bar δ 0.05 BarSize :

data FIT 2 1 sys bar 0 2 1 sys