## What is the logic of the variables within the loops? [pendente]

Can a variable be different from itself, depending on how it is inserted into a code?

For example, if I want to add 3 values ​​given by the user in Visualg, I use the following code:

``````algorithm "about 3 values"
male
counter, number, sum: whole
home
counter <- 1
While (counter <= 3) Knife
Type ("Value", counter, ":")
sum <- sum + number // My question is here
counter <- counter + 1 // And here too
Finish while
to write
TypeL ("The sum of the 3 values ​​entered is", S, ".")
final algorithm
``````

In Python:

``````sum = 0
for c in rank (1, 4):
num = int (entry (f & # 39; Value {c}: & # 39;))
sum + = number # sum = sum + number
print (f & # 39;  nThe sum of the 3 values ​​entered is {sum}.)
``````

The variable sum and counter in Visualg and sum in Python, in this case could be considered different from themselves when they are assigned as variable = variable + "something".

What I considered is that at the beginning it is the space that the variable occupies, after which, with the arithmetic operator of addition it would be its value, that is, there would be a difference in the variable when placing it assigning a value to itself.
Is it correct to say that? Is there any difference in this case?

I can not imagine that they are the same, no matter how hard I try to connect the dots, I get confused when I think about it.

## Code Review: What techniques have you used to avoid loops in Mathematica?

It seems to me that the mathematics loop is very slow (compared to matlab). Because many people still use Mathematica to finish their work, there must be some techniques to avoid this problem.

Could you share your techniques of avoiding loops in math with beginners like me?

## hd cg

BlackHatKings: General discussion of PPC
Posted by: MervinROX
Time of publication: June 16, 2019 at 18:15.

## python – How to optimize a subset selection function that has nested loops, to choose the best combination of n choose k models?

Algorithm

``````Let den0 be the null model that does not contain predictors. This model simply predicts the sample mean of each observation.

For 𝑘 = 1,2, ...., 𝑛:
Set all models (𝑛  choose 𝑘) that contain exactly 𝑘 predictors.

Choose the best among these (𝑛 choose 𝑘) models, and call it 𝑀_𝑘.
Here the best is defined as having the smallest or smallest equivalent measure.

Select the best individual model among 𝑀0, 𝑀1, ..., 𝑀𝑛 using the cross validation prediction error, 𝐶𝑝, BIC, 𝑅2𝑎𝑑𝑗 or any other method.
``````

I searched for a tutorial and created a function based on your script. It is basically used so that you can select dependent variables that are a subset of a data frame. It works but it is very very slow.

How would you flatten a nested loop like this?

``````# Loop over all possible combinations of k characteristics
for k in the range (1, len (X.columns) + 1):
# Reviewing all possible combinations: from 11 choose k
for combo in itertools.combinations (X.columns, k):
# Store temporary results
temp_results = fit_linear_reg (X[list(combo)], Y)

``````

I tried to implement an enumeration version but it did not work. I'm not sure, how can I flatten the nested loop, so that I can append the results of a function to a list.

``````# This function takes in a subset of a data frame that represents independent
# variables (X) and one column for the dependent variable (Y). This function adjusts
# separate models for each possible combination of the k predictors (which is
# based on the length of the X column) and then select the best subset. the
# The resulting output is a data frame.

Def BestSubsetSelection (X, Y):
# number of predictors
k = len (X.columns)
# Stores the RSS from a linear regression model.
# Stores the square R of a linear regression model
R_squared_list = []
# Stores the characteristics for a given iteration.
feature_list = []
# Stores the number of functions used for a given iteration. This corresponds to the feature_list.
numb_features = []

# Loop over all possible combinations of k characteristics
for k in the range (1, len (X.columns) + 1):
# Reviewing all possible combinations: from 11 choose k
for combo in itertools.combinations (X.columns, k):
# Store temporary results
temp_results = fit_linear_reg (X[list(combo)], Y)

# Add R-Squared to the R-Squared list
R_squared_list.append (temp_results)

# Add functions / s to the list of functions
feature_list.append (combo)

# Add the number of functions to the list of function numbers
numb_features.append (len (combo))

df = pd.DataFrame ({
& # 39; No_of_Features & # 39 ;: numb_features,
& # 39; R-Squared & # 39 ;: R_squared_list,
& # 39; Features & # 39 ;: feature_list
})

# Find the best subsets for each number of features

df_min = df[df.groupby('No_of_Features')['RSS'].transform (min) == df['RSS']]# The highest value of R square
df_max = df[df.groupby('No_of_Features')['R-Squared'].transform (min) == df['R-Squared']]show (df_min)
show (df_max)

# Add columns to the data frame with the RSS and R-Squared values ​​of the best subset
df['max_R_Squared'] = df.groupby (& # 39; No_of_Features & # 39;)['R-Squared'].transform (max)

$$`` `$$
``````

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## Python 3.x – How to make loops in scrapy?

I'm scraping a website from the Dmoz site. I want to make a loop in functions because the `in loop` that I am using in each function, I will have to put again and again in each function. Although its functionality is the same. The second thing I want to solve is to make a loop in `performance response. follow` because if I scraped more pages, I'll have to write this again and again. Is there any way of my two problems. I tried several times but I failed.

``````                                # save and call another page
performance response.follow (self.editor, self.parse_editor, meta = {& # 39; items & # 39 ;: items})

# do your stuff on the second page
items = response.meta['items']
names = {& # 39; name1 & # 39 ;: & # 39; Headings & # 39 ;,
& # 39; name2 & # 39 ;: & # 39; paragraphs & # 39 ;,
& # 39; name3 & # 39 ;: & # 39; 3 projects & # 39 ;,
& # 39; name4 & # 39 ;: & # 39; About Dmoz & # 39 ;,
& # 39; name5 & # 39 ;: & # 39; languages ​​& # 39 ;,
& # 39; name6 & # 39 ;: & # 39; You can make a difference & # 39 ;,
& # 39; name7 & # 39 ;: & # 39; additional information & # 39;
}

finder = {& # 39; find1 & # 39 ;: & # 39; h2 :: text, #mainContent h1 :: text & # 39 ;,
& # 39; find2 & # 39 ;: & # 39; p :: text & # 39 ;,
& # 39; find3 & # 39 ;: & # 39; li ~ li + li b a :: text, li: nth-child (1) b a :: text & # 39 ;,
& # 39; find4 & # 39 ;: & # 39; .nav ul a :: text, li: nth-child (2) b a :: text & # 39 ;,
& # 39; find5 & # 39 ;: & # 39; .nav ~ .nav to :: text & # 39 ;,
& # 39; find6 & # 39 ;: & # 39; dd :: text, # about-submit :: text & # 39 ;,
& # 39; find7 & # 39 ;: & # 39; li :: text, # about-more-info to :: text & # 39;
}
for name, search in zip (names.values ​​(), finder.values ​​()):
articles[name] = response.css (find) .extract ()
performance items
``````

## performance – Delete nested for loops in python

I have the following work code, but it is too slow because I have to execute the same code for several files inside a folder. Please, help me avoid the nested loop to make it more efficient.

``````def create_nodes (self):
for child in self.tree.getroot ():
columnNames = child.attrib
events = [grandchild.attrib for grandchild in child]
return (columnNames, events)

def create_game_data (self):
columns = {}
for the column in self.create_nodes ().llaves ():
columns[column] = []
columnValues ​​= []
for event in self.create_nodes ():
columnValues.append (self.create_nodes ()[column]) if column in self.create_nodes ().keys () else columnValues.append ("NA")
columns[column] = columnValues
return columns
``````

## python: remove unnecessary loops when parsing the cable modem status table

I am writing a Python script that will be used to generate a line protocol to insert metrics into an InfluxDB. I am analyzing the status page of my cable modem. That part works. I receive the data and I can extract the table of information that I need.

The HTML for the table looks like this (yes, it has the commented lines)

The one underneath works, but feels inefficient. I feel that I have many loops and these feel unnecessary. Although I'm not sure how to be more efficient.

The only change between this and my real code is that I have moved the HTML to pastebin to reduce the length of this publication. That pastebin is here and the HTML is not written or editable by me. It is generated in the cable modem. As I can not see the results of my cable modem executing the code I use to extract it, I hope this is good enough.

``````from bs4 import BeautifulSoup
import requests

results_url = "https://pastebin.com/raw/bLZLFzy6"
content = requests.get (results_url) .text

measure = "modem"
hostname = "home-modem"

def strip_uom (data):
"" "Unit of measure of band

Some of our fields come with a unit of measurement. Tear that and keep it alone
The value of the field.
""
uom = ["dB", "Hz", "dBmV", "Ksym/sec"]
data set = []
for d in the data:
yes d.split ("")[-1] in uom:
dataset.append (d.split (""))
plus:
dataset.append (d)
return data

soup = BeautifulSoup (content, & # 39; html.parser & # 39;)
dstable = soup.find (& # 39; table & # 39 ;, {& # 39; id & # 39 ;: & # 39; dsTable & # 39;})

# Pull the headings of the downstream table
# We want to make these labels friendly, so make them lowercase and eliminate spaces
dstable_tags = [td.get_text().lower().replace(" ", "_") for td in dstable.tr.find_all('td')]

# Extract the rest of the data for each row in the table; associate it with the correct label; Strip UoM
downstream_data = []
for the row in dstable.find_all (& # 39; tr & # 39;)[1:]:
column_values ​​= [col.get_text() for col in row.find_all('td')]
downstream_data.append (dict (zip (dstable_tags, strip_uom (column_values))))

# Print line protocol lines for the inputs.exec plugin of telegraphy to manage
for the data in downstream_data:
line_protocol_line = f "{measurement}, hostname = {hostname}"
fields = []
For key, value in data.items ():
# Check if our value is a number. If not, surround it with quotes.
# Do not use the float () value, since some numbers are returned as
# valid integers
try:
_ = float (value)
fields.append (f & # 39; {key} = {value} & # 39;)
except ValueError:
fields.append (f & # 39; {key} = "{value}" & # 39;)
fieldset = ",". join (fields)
line_protocol_line = line_protocol_line + f ", channel = {data['channel']} {fieldset} "
print (line_protocol_line)
``````

Finally, the output generated by this script is as follows:

``````modem, hostname = netgear-cm400, channel = 1 channel = 1, lock_status = "Blocked", modulation = "QAM 256", channel_id = 121, frequency = 585000000, power = 5.6, snr = 37.0, correctable = 19443, not correctable = 11263
modem, hostname = netgear-cm400, channel = 2 channel = 2, lock_status = "Blocked", modulation = "QAM 256", channel_id = 6, frequency = 591000000, power = 5.7, snr = 37.0, correctable = 19531, uncorrectables = 9512
modem, hostname = netgear-cm400, channel = 3 channel = 3, lock_status = "Blocked", modulation = "QAM 256", channel_id = 7, frequency = 597000000, power = 5.7, snr = 36.8, correctable = 17457, not correctable = 9736
modem, hostname = netgear-cm400, channel = 4 channel = 4, lock_status = "Blocked", modulation = "QAM 256", channel_id = 8, frequency = 603000000, power = 5.9, snr = 37.0, correctable = 12750, not correctable = 11156
modem, hostname = netgear-cm400, channel = 5 channel = 5, lock_status = "Blocked", modulation = "QAM 256", channel_id = 122, frequency = 609000000, power = 2.6, snr = 36.3, correctable = 1855538, not correctable = 18388
modem, hostname = netgear-cm400, channel = 6 channel = 6, lock_status = "Blocked", modulation = "QAM 256", channel_id = 10, frequency = 615000000, power = 2.6, snr = 37.0, correctable = 846194, not correctable = 14615
modem, hostname = netgear-cm400, channel = 7 channel = 7, lock_status = "Blocked", modulation = "QAM 256", channel_id = 11, frequency = 621000000, power = 2.6, snr = 37.6, correctable = 281431, not correctable = 13998
modem, hostname = netgear-cm400, channel = 8 channel = 8, lock_status = "Blocked", modulation = "QAM 256", channel_id = 12, frequency = 627000000, power = 2.4, snr = 36.1, correctable = 78059, not correctable = 13695
``````

I have loops and comprehension lists to get the labels, associate data with labels for each row and generate the line protocol lines. I have two additional understandings in those and a loop to see if I need to delete an included unit of measure. Can I eliminate some of these to be more efficient?

I am running all this code in Python 3.6.7.

## Python: update of personalized steps of Scipy basinhopping and restricted loops

I'm looking for the global minimum of a given function and trying to use its gradient (here just like Jacobin) to guide the step counter. However, my `X` It is the solution and so is my gradient. I am also trying to recover as quickly as possible the first `X` for which `f (x) <1`, therefore I am using a restriction.

• How can I update the `X` Entrance and the Jacobin?
• My `f (x) <1` It is not very effective, so is there any alternative to fulfill my requirement?

This is my code (more or less):

``````MyBounds class (object):
def __init __ (self, xmax =[2*np.pi, 2*np.pi, 2*np.pi, 2*np.pi, 1.2, 1.2, 1.2, 1.2], xmin =[0, 0, 0, 0, 0, 0, 0, 0] ):
self.xmax = np.array (xmax)
self.xmin = np.array (xmin)

def __call __ (self, ** kwargs):
x = kwargs["x_new"]
tmax = bool (np.all (x <= self.xmax))
tmin = bool(np.all(x >= self.xmin))
returns tmax and tmin

MyTakeStep class (object):
def __init __ (self, stepsize = 1):
self.stepsize = stepize

def compute_step (self, jacobi_matrix, x, i):
yes jacobi_matrix[i] < 0: r = np.random.uniform(0,      2*np.pi-x[i])
elif jacobi_matrix[i] >    0: r = np.random.uniform (0-x[i], 0)
else: r = 0
go back

def __call __ (self, x):
print ("ENTERING FROM CALL")
print ("THIS IS X:", x)
jacobi_matrix = jacobian (x)
print ("x:", x)
print ("jacobi:", jacobi_matrix)
X + = self.compute_step (jacobi_matrix, x, 0)
X + = self.compute_step (jacobi_matrix, x, 1)
X + = self.compute_step (jacobi_matrix, x, 2)
X + = self.compute_step (jacobi_matrix, x, 3)
X + = self.compute_step (jacobi_matrix, x, 4)
X + = self.compute_step (jacobi_matrix, x, 5)
X + = self.compute_step (jacobi_matrix, x, 6)
X + = self.compute_step (jacobi_matrix, x, 7)
print ("newx:", x)
return x

def f (x):
# components of the objective function
result = g1
result + = g2
result + = g3
return result

def jacobian (x):
print ("input_list in Jacobi:", x)

# define full derivatives
dG_dphi = dg1_dphi + dg2_dphi + dg3_dphi
dG_dr = dg1_dr + dg2_dr + dg3_dr

def callback (x, f, accept):
print ("x:% 65s | f:% 5s | accept:% 5s"% (str ([round(e,3) for e in x]), str (round (f, 3)), accept))

def hopping_solver (min_f, min_x, input_excitation):
# define limits
mybounds = MyBounds ()
mytakestep = MyTakeStep ()
comb = [deg2rad(phi) for phi in  input_excitation[:4]]+ input_excitation[4:]
print ("comb:", comb)
min_f = 10
tol = 0
Cons = {& # 39; type & # 39 ;: & # 39; ineq & # 39 ;, & # 39; fun & # 39 ;: lambda x: 1-f (x)}
k = {"method": & # 39; Nelder-Mead & # 39 ;, & # 39; restrictions & # 39 ;: cons, & # 39; jac & # 39 ;: jacobian, & tollet & # 39 ;: tol }
optimal_c = optimize.basinhopping (f,
x0 = comb,
Niter = 1000000,
T = 8,
step size = 1,
minimizer_kwargs = k,
take_step = mytakestep,
accept_test = mybounds,
callback = callback,
interval = 100000,
disp = true
niter_success = None)
print (optimum_c)
min_x, min_f = optimal_c['x'], optimum_c['fun']
comb = min_x