I need to eliminate alternate rows of an array, like i have an array of 23847X1 and i need the odd rows and finally making it into 11924X1. It is in .mat file and i want the resultant in the .mat file as well.
Try yourMatrix(1:2:size(yourMatrix, 2)).
The 1:2:N selects all elements from 1 to N with step 2.
A more complete example:
> M=[1, 2, 3, 4, 5, 6, 7]
M =
1 2 3 4 5 6 7
> OddM = M(1:2:size(M, 2))
OddM =
1 3 5 7
To load / store data in data.mat, follow H.Muster's advice below:
load('data.mat'); x = x(1:2:end,:); save('data.mat', 'x')
Related
Im currently using micropython and it does not have the .zfill method.
What Im trying to get is to get the YYMMDDhhmmss of the UTC.
The time that it gives me for example is
t = (2019, 10, 11, 3, 40, 8, 686538, None)
I'm able to access the ones that I need by using t[:6]. Now the problem is with the single digit numbers, the 3 and 8. I was able to get it to show 1910113408, but I need to get 19101034008 I would need to get the zeroes before those 2. I used
t = "".join(map(str,t))
t = t[2:]
So my idea was to iterate over t and then check if the number is less than 10. If it is. I will add zeroes in front of it, replacing the number . And this is what I came up with.
t = (2019, 1, 1, 2, 40, 0)
t = list(t)
for i in t:
if t[i] < 10:
t[i] = 0+t[i]
t[i] = t[i]
print(t)
However, this gives me IndexError: list index out of range
Please help, I'm pretty new to coding/python.
When you use
for i in t:
i is not index, each item.
>>> for i in t:
... print(i)
...
2019
10
11
3
40
8
686538
None
If you want to use index, do like following:
>>> for i, v in enumerate(t):
... print("{} is {}".format(i,v))
...
0 is 2019
1 is 10
2 is 11
3 is 3
4 is 40
5 is 8
6 is 686538
7 is None
another way to create '191011034008'
>>> t = (2019, 10, 11, 3, 40, 8, 686538, None)
>>> "".join(map(lambda x: "%02d" % x, t[:6]))
'20191011034008'
>>> "".join(map(lambda x: "%02d" % x, t[:6]))[2:]
'191011034008'
note that:
%02d add leading zero when argument is lower than 10 otherwise (greater or equal 10) use itself. So year is still 4digit string.
This lambda does not expect that argument is None.
I tested this code at https://micropython.org/unicorn/
edited :
str.format method version:
"".join(map(lambda x: "{:02d}".format(x), t[:6]))[2:]
or
"".join(map(lambda x: "{0:02d}".format(x), t[:6]))[2:]
second example's 0 is parameter index.
You can use parameter index if you want to specify it (ex: position mismatch between format-string and params, want to write same parameter multiple times...and so on) .
>>> print("arg 0: {0}, arg 2: {2}, arg 1: {1}, arg 0 again: {0}".format(1, 11, 111))
arg 0: 1, arg 2: 111, arg 1: 11, arg 0 again: 1
I'd recommend you to use Python's string formatting syntax.
>> t = (2019, 10, 11, 3, 40, 8, 686538, None)
>> r = ("%d%02d%02d%02d%02d%02d" % t[:-2])[2:]
>> print(r)
191011034008
Let's see what's going on here:
%d means "display a number"
%2d means "display a number, at least 2 digits"
%02d means "display a number, at least 2 digits, pad with zeroes"
so we're feeding all the relevant numbers, padding them as needed, and cut the "20" out of "2019".
I have a table in MATLAB with attributes in the first three columns and data from the fourth column onwards. I was trying to sort the entire table based on the first three columns. However, one of the columns (Column C) contains months ('January', 'February' ...etc). The sortrows function would only let me choose 'ascend' or 'descend' but not a custom option to sort by month. Any help would be greatly appreciated. Below is the code I used.
sortrows(Table, {'Column A','Column B','Column C'} , {'ascend' , 'ascend' , '???' } )
As #AnonSubmitter85 suggested, the best thing you can do is to convert your month names to numeric values from 1 (January) to 12 (December) as follows:
c = {
7 1 'February';
1 0 'April';
2 1 'December';
2 1 'January';
5 1 'January';
};
t = cell2table(c,'VariableNames',{'ColumnA' 'ColumnB' 'ColumnC'});
t.ColumnC = month(datenum(t.ColumnC,'mmmm'));
This will facilitate the access to a standard sorting criterion for your ColumnC too (in this example, ascending):
t = sortrows(t,{'ColumnA' 'ColumnB' 'ColumnC'},{'ascend', 'ascend', 'ascend'});
If, for any reason that is unknown to us, you are forced to keep your months as literals, you can use a workaround that consists in sorting a clone of the table using the approach described above, and then applying to it the resulting indices:
c = {
7 1 'February';
1 0 'April';
2 1 'December';
2 1 'January';
5 1 'January';
};
t_original = cell2table(c,'VariableNames',{'ColumnA' 'ColumnB' 'ColumnC'});
t_clone = t_original;
t_clone.ColumnC = month(datenum(t_clone.ColumnC,'mmmm'));
[~,idx] = sortrows(t_clone,{'ColumnA' 'ColumnB' 'ColumnC'},{'ascend', 'ascend', 'ascend'});
t_original = t_original(idx,:);
I have a table looks like
Time ID Value1 Value2
1 a 1 4
2 a 2 3
3 a 5 9
1 b 6 2
2 b 4 2
3 b 9 1
4 b 2 5
1 c 4 7
2 c 2 0
Here is the tasks and requirements:
I want to set the column ID as the key, not the column Time, but I don't want to delete the column Time. Is there a way in Spark to set Primary Key?
The aggregation function is non-linear, which means you can not use "reduceByKey". All the data must be shuffled to one single node before calculation. For example, the aggregation function may looks like root N of the sum values, where N is the number of records (count) for each ID :
output = root(sum(value1), count(*)) + root(sum(value2), count(*))
To make it clear, for ID="a", the aggregated output value should be
output = root(1 + 2 + 5, 3) + root(4 + 3 + 9, 3)
the later 3 is because we have 3 record for a. For ID='b', it is:
output = root(6 + 4 + 9 + 2, 4) + root(2 + 2 + 1 + 5, 4)
The combination is non-linear. Therefore, in order to get correct results, all the data with the same "ID" must be in one executor.
I checked UDF or Aggregator in Spark 2.0. Based on my understanding, they all assume "linear combination"
Is there a way to handle such nonlinear combination calculation? Especially, taking the advantage of parallel computing with Spark?
Function you use doesn't require any special treatment. You can use plain SQL with join
import org.apache.spark.sql.Column
import org.apache.spark.sql.functions.{count, lit, sum, pow}
def root(l: Column, r: Column) = pow(l, lit(1) / r)
val out = root(sum($"value1"), count("*")) + root(sum($"value2"), count("*"))
df.groupBy("id").agg(out.alias("outcome")).join(df, Seq("id"))
or window functions:
import org.apache.spark.sql.expressions.Window
val w = Window.partitionBy("id")
val outw = root(sum($"value1").over(w), count("*").over(w)) +
root(sum($"value2").over(w), count("*").over(w))
df.withColumn("outcome", outw)
I am trying to find the odd numbers and a multiple of 7 between a 1 to 100 and append them into an array. I have got this far:
var results: [Int] = []
for n in 1...100 {
if n / 2 != 0 && 7 / 100 == 0 {
results.append(n)
}
}
Your conditions are incorrect. You want to use "modular arithmetic"
Odd numbers are not divisible by 2. To check this use:
if n % 2 != 0
The % is the mod function and it returns the remainder of the division (e.g. 5 / 2 is 2.5 but integers don't have decimals, so the integer result is 2 with a remainder of 1 and 5 / 2 => 2 and 5 % 2 => 1)
To check if it's divisible by 7, use the same principle:
if n % 7 == 0
The remainder is 0 if the dividend is divisible by the divisor. The complete if condition is:
if n % 2 != 0 && n % 7 == 0
You can also use n % 2 == 1 because the remainder is always 1. The result of any mod function, a % b, is always between 0 and b - 1.
Or, using the new function isMultiple(of:, that final condition would be:
if !n.isMultiple(of: 2) && n.isMultiple(of: 7)
Swift 5:
Since Swift 5 has been released, you could use isMultiple(of:) method.
In your case, you should check if it is not multiple of ... :
if !n.isMultiple(of: 2)
Swift 5 is coming with isMultiple(of:) method for integers , so you can try
let res = Array(1...100).filter { !$0.isMultiple(of:2) && $0.isMultiple(of:7) }
Here is an efficient and concise way of getting the odd multiples of 7 less than or equal to 100 :
let results: [Int] = Array(stride(from: 7, through: 100, by: 14))
You can also use the built-in filter to do an operation on only qualified members of an array. Here is how that'd go in your case for example
var result = Array(1...100).filter { (number) -> Bool in
return (number % 2 != 0 && number % 7 == 0)
}
print(result) // will print [7, 21, 35, 49, 63, 77, 91]
You can read more about filter in the doc but here is the basics: it goes through each element and collects elements that return true on the condition. So it filters the array and returns what you want
I've got a list of some integers, e.g. [1, 2, 3, 4, 5, 10]
And I've another integer (N). For example, N = 19.
I want to check if my integer can be represented as a sum of any amount of numbers in my list:
19 = 10 + 5 + 4
or
19 = 10 + 4 + 3 + 2
Every number from the list can be used only once. N can raise up to 2 thousand or more. Size of the list can reach 200 integers.
Is there a good way to solve this problem?
4 years and a half later, this question is answered by Jonathan.
I want to post two implementations (bruteforce and Jonathan's) in Python and their performance comparison.
def check_sum_bruteforce(numbers, n):
# This bruteforce approach can be improved (for some cases) by
# returning True as soon as the needed sum is found;
sums = []
for number in numbers:
for sum_ in sums[:]:
sums.append(sum_ + number)
sums.append(number)
return n in sums
def check_sum_optimized(numbers, n):
sums1, sums2 = [], []
numbers1 = numbers[:len(numbers) // 2]
numbers2 = numbers[len(numbers) // 2:]
for sums, numbers_ in ((sums1, numbers1), (sums2, numbers2)):
for number in numbers_:
for sum_ in sums[:]:
sums.append(sum_ + number)
sums.append(number)
for sum_ in sums1:
if n - sum_ in sums2:
return True
return False
assert check_sum_bruteforce([1, 2, 3, 4, 5, 10], 19)
assert check_sum_optimized([1, 2, 3, 4, 5, 10], 19)
import timeit
print(
"Bruteforce approach (10000 times):",
timeit.timeit(
'check_sum_bruteforce([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 200)',
number=10000,
globals=globals()
)
)
print(
"Optimized approach by Jonathan (10000 times):",
timeit.timeit(
'check_sum_optimized([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 200)',
number=10000,
globals=globals()
)
)
Output (the float numbers are seconds):
Bruteforce approach (10000 times): 1.830944365834205
Optimized approach by Jonathan (10000 times): 0.34162875449254027
The brute force approach requires generating 2^(array_size)-1 subsets to be summed and compared against target N.
The run time can be dramatically improved by simply splitting the problem in two. Store, in sets, all of the possible sums for one half of the array and the other half separately. It can now be determined by checking for every number n in one set if the complementN-n exists in the other set.
This optimization brings the complexity down to approximately: 2^(array_size/2)-1+2^(array_size/2)-1=2^(array_size/2 + 1)-2
Half of the original.
Here is a c++ implementation using this idea.
#include <bits/stdc++.h>
using namespace std;
bool sum_search(vector<int> myarray, int N) {
//values for splitting the array in two
int right=myarray.size()-1,middle=(myarray.size()-1)/2;
set<int> all_possible_sums1,all_possible_sums2;
//iterate over the first half of the array
for(int i=0;i<middle;i++) {
//buffer set that will hold new possible sums
set<int> buffer_set;
//every value currently in the set is used to make new possible sums
for(set<int>::iterator set_iterator=all_possible_sums1.begin();set_iterator!=all_possible_sums1.end();set_iterator++)
buffer_set.insert(myarray[i]+*set_iterator);
all_possible_sums1.insert(myarray[i]);
//transfer buffer into the main set
for(set<int>::iterator set_iterator=buffer_set.begin();set_iterator!=buffer_set.end();set_iterator++)
all_possible_sums1.insert(*set_iterator);
}
//iterator over the second half of the array
for(int i=middle;i<right+1;i++) {
set<int> buffer_set;
for(set<int>::iterator set_iterator=all_possible_sums2.begin();set_iterator!=all_possible_sums2.end();set_iterator++)
buffer_set.insert(myarray[i]+*set_iterator);
all_possible_sums2.insert(myarray[i]);
for(set<int>::iterator set_iterator=buffer_set.begin();set_iterator!=buffer_set.end();set_iterator++)
all_possible_sums2.insert(*set_iterator);
}
//for every element in the first set, check if the the second set has the complemenent to make N
for(set<int>::iterator set_iterator=all_possible_sums1.begin();set_iterator!=all_possible_sums1.end();set_iterator++)
if(all_possible_sums2.find(N-*set_iterator)!=all_possible_sums2.end())
return true;
return false;
}
Ugly and brute force approach:
a = [1, 2, 3, 4, 5, 10]
b = []
a.size.times do |c|
b << a.combination(c).select{|d| d.reduce(&:+) == 19 }
end
puts b.flatten(1).inspect