[ab initio mathematics] No. 206 section optimization method and experimental design preliminary

plot summary:
[Machine Xiao Wei] in the [engineer Ah Wei] escorted into the [nine turn Elixir] seventh turn of the cultivation.
This study is to be [preferred method and experimental design preliminary].

Drama Start:

Star Calendar May 08, 2016 16:07:12, the Milky Way Galaxy Earles the Chinese Empire Jiangnan Line province.

[Engineer Ah Wei] is working with [machine Xiao Wei] to study [optimization method and experimental design preliminary].

<span style= "FONT-SIZE:18PX;" >>>> a = 1+1-2*math.cos (108/180*math.pi);>>> a2.618033988749895>>> d = a**0.5;>> > d1.618033988749895>>> d* (d-1);1.0000000000000002</span>

<span style= "FONT-SIZE:18PX;" >>>> [1, 0.5, 0.6666666666666666, 0.6000000000000001, 0.625, 0.6153846153846154, 0.6190476190476191, 0.6176470588235294, 0.6181818181818182, 0.6179775280898876, 0.6180555555555556] #分数法系数阵列def fractionmethod ():    array = [];    w = 1;    Array.append (w);        For I in range:        w = 1/(1+w);        Array.append (w);    Print (array);</span>

<span style= "FONT-SIZE:18PX;" >>>> [1, 1, 2, 3, 5, 8,, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765]21 55def tmp (): 
    #生成斐波那契数列    fib = [];    For I in range:        fib.append (Fibonacci (i));    Print (FIB);            #试验范围    bound_low =;    Bound_high =;    Index_low = Index_high = 1;        For I in range (len):        if (Fib[i] <= bound_low):            index_low = i;        if (Fib[i] > Bound_low): Break            ;    For I in range (len):        if (Fib[i] >= bound_high):            index_high = i;            break;    Print (Fib[index_low], Fib[index_high]); Test points: 21, 55, 34, [21, 34] to [0, 13], middle take [8, 5], so is taken, 26</span>

<span style= "FONT-SIZE:18PX;" >1  --912--843--794---765-- > 767----------  91def tmp ():   for I in range (1, 10):       print (i, '---', i*i+10* (10-i));     </span>

What kinds of saturated orthogonal tables are there?

<span style= "FONT-SIZE:18PX;" >>>> l_4.0 (2^3) l_8.0 (2^7) l_16.0 (2^15) l_32.0 (2^31) l_9.0 (3^4) l_27.0 (3^13) l_16.0 (4^5) l_64.0 (4^21) L_ 25.0 (5^6) l_125.0 (5^31) l_36.0 (6^7) #水平N正交表def orthogonaltable (level, Factor):    #饱和型正交表    count = 0;    #q = (t^n-1)/(t-1)    count = Math.log (((LEVEL-1) *factor) +1)/math.log (level);    Return count;def tmp ():    #水平    for I in range (2, 7):        #因素        for J in range (2,):            result = orthogonaltable ( I, j);            If ABS (int (result)-result) <1e-6:                print (' l_{0} ({1}^{2}) '. Format (Round (I**result, 3), I, J));</span>

<span style= "FONT-SIZE:18PX;" >>>> [[15.0, 18.0, 13.5], [14.5, 11.5, 16.0]][0.5, 6.5, 2.5]def tmp (): Level    = 2;    Factor = 3;        Array = [        #前三列为因素, the last column is the data value obtained from the experiment [        1,1,1,17],        [1,2,2,13],        [2,1,2,19],        [2,2,1,10]        ];    size = len (array);    K_q = [([0]*factor) for J in Range];    For k in range: For I in        Range (factor):            sum_ = 0;            For j in Range (size):                if (array[j][i] = = k+1):                    sum_ + = Array[j][factor];            K_q[k][i] = Sum_/level;    Print (k_q);           R = [0]*factor;    For I in Range (factor):        subarray = [];        For j in range:            subarray.append (K_q[j][i]);        Max_ = max (subarray);        min_ = min (subarray);        R[i] = Max_-min_;    Print (R);</span>

<span style= "FONT-SIZE:18PX;" >>>> [[1.5, 2.25, 2.0], [2.75, 2.0, 2.25]][1.25, 0.25, 0.25]def tmp (): Level    = 2;    Factor = 3;        Array = [        #前三列为因素, the last column is the data value obtained from the experiment [        1,1,1,1.5],        [1,2,2,1.5],        [2,1,2,3.0],        [2,2,1,2.5]        ];    size = len (array);    K_q = [([0]*factor) for J in Range];    For k in range: For I in        Range (factor):            sum_ = 0;            For j in Range (size):                if (array[j][i] = = k+1):                    sum_ + = Array[j][factor];            K_q[k][i] = Sum_/level;    Print (k_q);           R = [0]*factor;    For I in Range (factor):        subarray = [];        For j in range:            subarray.append (K_q[j][i]);        Max_ = max (subarray);        min_ = min (subarray);        R[i] = Max_-min_;    Print (R);</span>

<span style= "FONT-SIZE:18PX;" >>>> [[433.667, 456.167, 439.833], [438.833, 423.667, 418.167], [424.667, 417.333, 439.167]][14.166, 38.834,    21.666]def tmp (): level = 3;        Factor = 3; Array = [#前三列为因素, the last column is the data value obtained from the experiment [1,1,1,463.5], [1,2,2,409.0], [1,3,3,428.5], [2,1,2,451                 .5], [2,2,3,435.5], [2,3,1,429.5], [3,1,3,453.5], [3,2,1,426.5], [3,3,2,394.0]    ];    size = len (array);    K_q = [([0]*factor) for J in Range];            For k in range: For I in Range (factor): Sum_ = 0;            For j in Range (size): if (array[j][i] = = k+1): Sum_ + = Array[j][factor];    K_q[k][i] = Round (sum_/level, 3);           Print (K_Q);    R = [0]*factor;        For I in Range (factor): Subarray = [];        For j in Range: Subarray.append (K_q[j][i]);        Max_ = max (subarray);        min_ = min (subarray); R[i] = Round (Max_-min_, 3); Print (R);</span>

>>> [[72.0, 83.5, 80.0], [84.5, 73.0, 76.5]][12.5, 10.5, 3.5]def TMP2 (): Level    = 2;    Factor = 3;        Array = [        #前三列为因素, the last column is the data value obtained from the experiment [        1,1,1,79],        [1,2,2,65],        [2,1,2,88],        [2,2,1,81]        ];    size = len (array);    K_q = [([0]*factor) for J in Range];    For k in range: For I in        Range (factor):            sum_ = 0;            For j in Range (size):                if (array[j][i] = = k+1):                    sum_ + = Array[j][factor];            K_q[k][i] = Sum_/level;    Print (k_q);           R = [0]*factor;    For I in Range (factor):        subarray = [];        For j in range:            subarray.append (K_q[j][i]);        Max_ = max (subarray);        min_ = min (subarray);        R[i] = Max_-min_;    Print (R);

Each row in these orthogonal tables is exactly what the law is, and Xiao Wei has not figured it out, let it go.

The end of this section, to know how to funeral, please see tell.

[ab initio mathematics] No. 206 section optimization method and experimental design preliminary