logical errors essay

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logical errors essay

python – What is the reason why this program runs slowly and memory errors occur with large amounts of data

I have a problem with the Rusul, the result is slow and the "memory error" is displayed for large amounts of data

def BCS (Eval_Func, m_i = 50, n = 50, minf = 0, dim = none, prog = false, alpha = 0.1, beta = 1.5, param = 0.2):

estim = Eval_Func (). evaluate

if dim == None:
dim = Eval_Func (). check_dimentions (dim)
pa = param
# flag = dr
gens = random_search (n, dim)
fit[float("-inf") if minf == 0 else float("inf") for _ in range(n)]
    pos =[0 for _ in range(n)]
    g_pos =[0]* dim
g_val = float ("- inf") if minf == 0 otherwise float ("inf")
gens_dict = {tuple ([0]* dim): float ("- inf") if minf == 0 otherwise float ("inf")}
when prog:
Miter = tqdm (range (m_i))
otherwise:
Miter = area (m_i)
for miter:
enumerate for i, g in (gens):
if tuple (g) in gens_dict:
score = gens_dict[tuple(g)]
            otherwise:
Score = estimate (g)
gens_dict[tuple(g)]= Score
if score> fit[i] if minf == 0 else score < fit[i]:
                fit[i]=score
                pos[i]=g

        maxfit,maxind=max(fit),fit.index(max(fit))
        minfit,minind=min(fit),fit.index(min(fit))
        if minf==0:
            if maxfit > g_val:
g_val = dc (maxfit)
g_pos = dc (gens[maxind])
otherwise:
if minfit <g_val:
g_val = dc (minfit)
g_pos = dc (gens[minind])

if pa < random.uniform(0,1):
            if minf==0:
                gens[minind]=[0 if 0.5>random.uniform (0,1) else 1 for _ in range (dim)]#rand_gen ()
fit[minind]= float ("- inf") if minf == 0 otherwise float ("inf")
otherwise:
gens[maxind]=[0 if 0.5>random.uniform(0,1) else 1 for _ in range(dim)]#rand_gen ()
fit[maxind]= float ("- inf") if minf == 0 otherwise float ("inf")


for g in gens:
for d in range (dim):
x = levy_flight (beta, g_pos[d],G[d],Alpha)
if at random.uniform (0,1) < sigmoid(x):
                    g[d]=1
                else:
                    g[d]=0
    return g_val,g_pos,g_pos.count(1)
class Evaluate:
    def __init__(self):
        self.train_l = tr_y
        self.train_d = tr_x
        self.test_l = te_y
        self.test_d=te_x
        self.K = 2
    def evaluate(self,gen):
        mask=np.array(gen) > 0
al_data = np.array ([al[mask] for al in self.train_d])
# al_test_data = np.array ([al[mask] for al in self.test_d])
kf = ms.KFold (n_splits = self.K, random_state = 42)
s = 0
for tr_ix, te_ix in kf.split (al_data):
s + = svm.SVR (kernel = "linear", gamma = 0.001, C = 1.0, epsilon = 0.2) .fit (al_data[tr_ix]self.train_l[tr_ix]) .score (al_data[te_ix]self.train_l[te_ix]) #. predict (al_test_data)
s / = self.K
returns
def check_dimentions (self, dim):
if dim == None:
return len (self.train_d[0])
otherwise:
dimming back
def test_score (gen, tr_x, tr_y, te_x, te_y):
mask = np.array (gen) == 1
al_data = np.array (tr_x[:,mask])
al_test_data = np.array (te_x[:,mask])
return np.mean (np.abs (([svm.SVR(kernel = 'linear', gamma=0.001, C=1.0, epsilon=0.2).fit(al_data,tr_y).score(al_test_data,te_y) for i in range(4)]))) * 100    

And then I will call Function BCS with this program

def fitureSelection (data, column):
Result = []
    n = len (column)
for i in the range (0,2):
X = data.loc[data['label']== i].iloc[:,0:n-2]
        y = data.loc[data['label']== i].iloc[:,-1]
        tr_x, te_x, tr_y, te_y = train_test_split (X, Y, test_size = 0.2, random_state = 42)
sc_X = StandardScaler ()
tr_x = sc_X.fit_transform (tr_x)
te_x = sc_X.fit_transform (te_x)
s, g, l = BCS (Eval_Func = Evaluate, n = 20, m_i = 200)
result.append ("". join (map (str, g)))
Return result

Result

['0110111011011111010100101111110100001111100111001001111101000001101011110111001111111110011',

'1100001101001111000010001010010010101001100100110011011111000111100111111100101111011111111']

I hope someone can help me achieve the high performance to run this program and get the result faster.

apache 2.4 – MultiPHP in XAMPP configuration causes 404 errors

I've followed this answer / guide to configure multiple PHP versions on a Windows server using XAMPP (unfortunately they can not be migrated to Linux yet).

Most of the steps were fine, but after completing the steps, a 403 error is now displayed.

This is my (flown) httpd-xampp file:

# I know it's php53, what's a shit - but just roll it
ScriptAlias ​​/ php53 "C: / xampp / php53"
Action Application / x-httpd-php53-cgi /php53/php-cgi.exe


    AllowOverride None
Options None
All denied require
    
        Need all granted
    



    
        
        
        
        SetHandler application / x-httpd-php53-cgi
    

and then my vhost


    
    
    
    Server name php5.local
DocumentRoot "C: / www / php5"
ErrorLog "logs / php5.local-error.log"
CustomLog "logs / php5.local-access.log" in common
        
        AllowOverride All
Need all granted
Options -Indexes + FollowSymLinks + Multiviews
    

I then restarted my Apache server after making the above changes, but when I go to php5.local, it only shows a 404 error – what's wrong with my script?

Removing the directory material to set the PHP version renders the index.php page I have (just an echo hello world) – so it has something to do with this CGI.

Differential Equations – Errors The expression can not be used as a part specification, simple case, and compound to fit multiple data

So, I have a very simple question that relates to a simple topic, but also directly to an appropriate problem.
I see that my question is related to it [this question] (Minimization with indexes, error: Part :: pkspec1: The expression can not be used as a part specification), but I can not beat the bridge between them.

The basic question is how to create a function that looks like this:

list = {{1, 2}, 2, {3, 4, 8, 12}, 4, {2, 2, 2, 2, 2}}
F[x_] = List[[x]]

And I would like to use it in the following context:

err[Vinitphi_?NumberQ, l_?NumberQ, nc_?NumberQ, tauC_?NumberQ, 
  tauG_?NumberQ, D0_?NumberQ, k_] =
With[{model = (Rad /. pfun)[Vinitphi, l, nc, tauC, tauG, D0]},
standard[rad[[k]][[2;;length[[2Length;;[[2;;Länge[[2;;Length[rad[[k]]], 2]]-
model / @ rad[[k]][[2;;length[[2Length;;[[2;;Länge[[2;;Length[rad[[k]]], 1]]]]fit = FindMinimum[{Sum[
    err[Vinitphi/2^(i - 1), l, nc, tauC, tauG, D0, i], {i, 1, 6, 1}].
5 * 10 ^ 7 <Vinitphi <9 * 10 ^ 7, 20 <1 <25, 0.05 <tauC <0.5,
0.2 <nc <0.8,
4 * 10 ^ (10) <D0 <2 * 10 ^ (11)}, {{Vinitphi, 6 * 10 ^ 7}, {nc, 0,5}, {tauC,
0,2}, {D0, 6 * 10 ^ (10)}}]

Where can I get the error?

Part :: pkspec1: The expression k can not be used as a part specification.

Could you please help?

I'm writing ParametricNDSolve in the following appendix, which I'll take care of:

Cecum:

rad = {{{0., 117.705}, {3., 148.255}, {6., 176.81}, {9., 183.561}, {12.,
197.419}, {15., 210.672}, {18., 211.152}, {21., 209.889}, {24.
207.741}, {27., 204.352}, {30., 201.79}, {33., 199.976}, {36.,
199.04}, {39., 197.151}, {42., 197.584}, {45., 196.198}, {48.,
195.153}, {51., 195.711}, {54., 194.088}, {57., 193.304}, {60.,
192.474}, {63., 192.13}, {66., 192.877}, {69., 192.371}, {72.,
192.657}, {75., 190.984}, {78., 190.685}, {81., 190.449}, {84.,
189.83}, {87., 189.625}, {90., 194.855}, {93., 186.581}, {96.,
184,735}, {99., 184.586}, {102., 183.505}, {105., 181.531}, {108.,
179,925}, {111., 178,428}, {114., 176,164}, {117., 175,375}, {120.
174.782}, {123., 172.649}, {126., 170.454}, {129.,
168.357}, {132., 168.04}, {135., 167.26}, {138., 165.657}, {141.,
164.797}, {144., 163.705}, {147., 161.214}, {150., 160.5}, {153.,
159,353}, {156., 157.873}, {159., 157.225}}, {{0., 51.7792}, {3.,
80.825}, {6., 108.913}, {9., 121.147}, {12., 130.805}, {15.,
140, 562}, {18, 143, 615}, {21, 146, 513}, {24, 147, 63}, {27,
147,12}, {30, 146,693}, {33., 147,396}, {36., 148,256}, {39.
147.737}, {42., 148.685}, {45., 149.043}, {48., 147.814}, {51.,
148.776}, {54., 147.959}, {57., 147.775}, {60., 148.031}, {63.,
148,284}, {66, 148,334}, {69, 148,521}, {72, 148,974}, {75.
146,562}, {78., 145.734}, {81., 145.177}, {84., 145.588}, {87.,
144,949}, {90, 147, 035}, {93, 141, 755}, {96, 140, 841}, {99,
139.94}, {102., 138.25}, {105., 136.508}, {108., 135.52}, {111.
133.758}, {114., 132.694}, {117., 131.744}, {120., 131.208}, {123.,
130,292}, {126., 127.612}, {129., 126.981}, {132.,
127,035}, {135, 125,198}, {138, 123,557}, {141, 123,946}, {144,
120,738}, {147, 119,875}, {150, 118,828}, {153,
118.162}, {156., 117.363}, {159., 116.712}, {{0., 29.62}, {3.,
53.1414}, {6., 67.2233}, {9., 82.5676}, {12., 83.5019}, {15.,
92.3142}, {18., 98.9869}, {21., 102.557}, {24., 106.481}, {27.,
107.188}, {30., 107.637}, {33., 108.415}, {36., 109.622}, {39.,
110,593}, {42., 111.205}, {45., 111.396}, {48., 111.668}, {51.,
114.126}, {54., 113.3}, {57., 114.27}, {60., 114.849}, {63.,
110.808}, {66., 116.51}, {69., 118.796}, {72., 119.636}, {75.,
118.02}, {78., 116.026}, {81., 116.767}, {84., 116.994}, {87.,
119, 169}, {90, 121,246}, {93, 116,291}, {96, 117,296}, {99,
117.72}, {102., 115.814}, {105., 114.76}, {108., 114.853}, {111.,
113.886}, {114., 112.522}, {117., 112.109}, {120., 112.376}, {123.,
110,998}, {126., 109.708}, {129., 108.926}, {132.,
108,075}, {135., 107.182}, {138., 106.723}, {141., 106.562}, {144.,
103.807}, {147., 102.798}, {150., 102.333}, {153.,
101.633}, {156., 100.395}, {159., 99.889}}, {{0., 79.5768}, {3.
86.0729}, {6., 101.334}, {9., 103.158}, {12., 104.818}, {15.,
104.534}, {18., 104.361}, {21., 105.568}, {24., 107.109}, {27.,
105,042}, {30., 105.165}, {33., 107.669}, {36., 108.182}, {39.
108,549}, {42., 109.208}, {45., 109.714}, {48., 110.098}, {51.,
110.481}, {54., 110.373}, {57., 110.563}, {60., 111.115}, {63.,
111.766}, {66., 112.415}, {69., 113.322}, {72., 113.272}, {75.,
113,95}, {78., 113,98}, {81., 114,017}, {84., 111,879}, {87.,
114.706}, {90., 112.125}, {93., 109.696}, {96., 112.481}, {99.,
109,528}, {102., 108.22}, {105., 108.112}, {108., 107.387}, {111.
106,369}, {114., 106.522}, {117., 105.678}, {120., 111.234}, {123.,
109,391}, {126., 104.95}, {129., 109.079}, {132., 109.363}, {135.,
100,807}, {138., 99.9696}, {141., 100.622}, {144., 99.789}, {147.,
98.5068}, {150., 99.6161}, {153., 97.4872}, {156.,
101.554}, {159., 101.406}, {{0., 30.4597}, {3., 35.889}, {6.
45.7724}, {9., 54.0641}, {12., 56.851}, {15., 59.1402}, {18.
61.0664}, {21., 63.1851}, {24., 65.2428}, {27., 66.6239}, {30.,
67.5882}, {33., 68.5353}, {36., 69.885}, {39., 71.1742}, {42.,
72.485}, {45., 73.2793}, {48., 74.2798}, {51., 74.7271}, {54.,
74.8248}, {57., 75.83}, {60., 76.1228}, {63., 77.5324}, {66.,
76.4005}, {69., 77.5578}, {72., 80.4519}, {75., 80.1548}, {78.,
80.1533}, {81., 79.2626}, {84., 79.6456}, {87., 79.5882}, {90.,
78,9125}, {93., 77.5023}, {96., 80.9046}, {99., 78.125}, {102.,
78.1087}, {105., 82.0064}, {108., 80.6066}, {111., 82.9245}, {114.,
83.6384}, {117., 82.0775}, {120., 81.1198}, {123., 75.248}, {126.,
78.1893}, {129., 72.6991}, {132., 72.683}, {135., 80.5139}, {138.,
83.442}, {141., 81.0871}, {144., 80.1472}, {147., 79.3543}, {150.,
79.0979}, {153., 79.0636}, {156., 78.3953}, {159.,
77.2895}}, {{0., 27.5731}, {3., 27.4456}, {6., 37.2589}, {9.,
40,9683}, {12, 43,2509}, {15, 44,3384}, {18, 46,5891}, {21.
48.0219}, {24., 49.6954}, {27., 51.1536}, {30., 51.7754}, {33.,
53.2019}, {36., 54.9082}, {39., 55.9732}, {42., 57.3092}, {45.,
58.6397}, {48., 58.9803}, {51., 58.6734}, {54., 60.6691}, {57.,
61.3107}, {60., 62.1459}, {63., 63.2534}, {66., 64.1169}, {69.,
64.7201}, {72., 65.4561}, {75., 65.7958}, {78., 65.9518}, {81.,
66,9163}, {84., 67.5038}, {87., 64.925}, {90., 69.8176}, {93.,
65.8334}, {96., 69.2665}, {99., 66.6777}, {102., 65.9855}, {105.,
69.4403}, {108., 70.2052}, {111., 69.9442}, {114., 70.7562}, {117.
70.3714}, {120., 63.5385}, {123., 62.8021}, {126.,
67.0399}, {129., 61.5096}, {132., 63.1028}, {135., 70.3602}, {138.,
70.2032}, {141., 69.4146}, {144., 68.2243}, {147.
67.9524}, {150., 67.8477}, {153., 68.2036}, {156., 68.2165}, {159.,
67.5863}}}

pfun = ParametricNDSolve[{Derivative[1][V][
     t]    == - ((
D0 (E ^ (- ((L + tv) ^ 2 / (4 D0t))) (-L + tv) Vinitphi) / ((D0t) ^ (
3/2) * 4 * Sqrt[Pi]* phi0)) + (
2 Vphi
2 (1 - Vphi
phic) ^ 2 tauC V
1/3)) - (-nc V
4 l L nL0 [Pi] csch[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
2 V
2 ^ (2/3) l)](-E ^ (- (a Vphi
l ^ 2 Sinh[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
1/3)) (V
E ^ (- (a Vphi
cosh[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
V
2/3))) / (-L Coth[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
2 V
2 ^ (2/3) l)]+ ((3 / [Pi]) ^ (1/3)
coth[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
V
TauG, V[eps] == 10,
derivative[1][Vphi][
     t]    == - ((D0 (E ^ (- ((L + tv) ^ 2 / (4 D0 t)))) (-L + tv) Vinitphi) / (
4 * Sqrt[Pi]* phi0 (D0 t) ^ (3/2))) + ((
VPHI
4 l L nL0 [Pi] csch[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
2 V
2 ^ (2/3) l)](-E ^ (- (a Vphi
l ^ 2 Sinh[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
1/3)) (V
E ^ (- (a Vphi
cosh[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
V
2/3))) / (-L Coth[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
2 V
2 ^ (2/3) l)]+ ((3 / [Pi]) ^ (1/3)
coth[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi[((E^(((aVphi
V
V
Rad & # 39;
wheel[eps] == 3/4 Pi}, {V, Vphi, Rad}, {t, eps, 170}, {Vinitphi, l,
nc, tauC, tauG, D0},
Method -> {"EquationSimplification" -> "Residual"}]

You can check if my initial guess is not bad:
Enter image description here

Find primes, memory errors

I tried writing Sieve for the first time and came up with that code

def sie (num):
pay = set (range (3, num, 2))
for i in range (3, num // 2, 2):
num_set = set (range (2 * i, num, i))
numbers = numbers - num_set
Return list (sorted ((2, * numbers)))

The problem is this num> 10 ** 6 The time to create primes increases.

Even when I tried num = 10 ** 8 My computer stopped working, made unpleasant sounds, and I had to restart it.

I think the problem is that I'm dealing with sets. For large numbers (for example in num = 10 ** 8 In this case, the set can not be created because my computer can not process as much information and therefore stops it.

Is there a way to solve this memory or time problem with my code or should another algorithm be used?

Stops dropdown box errors in Google Sheets

So I'm trying to use the drop-down menus to restrict people's input, but there's a formula that lets those cells be populated with information that people do not want to enter. This is fine with the formula, but the problem is that an error message will be displayed when triggered, as this input option is not included in the drowning list. Is it possible to either stop the error from occurring or solve another problem so as not to display the red triangle in the corner of the cell?
Thank you in advance.

bitcoin core – How to fix "Errors with non-obligatory script check flag (script evaluated without errors, but finished with wrong / empty topmost stack element)"?

I get: 2019-06-14T01: 16: 35Z ERROR: ConnectBlock (): CheckInputs failed on c2de0a7014853898bde9319cb347225d4ee6cef9025dc56773b0b57010060d48 with non-mandatory script check flag (script evaluated without error, but terminated with an incorrect end) (Code 64)

  • before that, the knot made progress. The necklace was in line with the normal one UpdateTip
  • then constant errors 2019-06-14T01: 16: 54Z ERROR: AcceptBlockHeader: Block 0000000000000000000104e5800f4ea259c5ab92a18e15320d8879d6c9e09e5b are marked as invalid. No UpdateTip Events for days.

bitcoind --version Bitcoin Core Daemon Version v0.18.99.0-257f750cd