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[Image](/uploads/documents/b/e/1/2/be120c9b4aa84615315e317f8b6bd115/p1_1.jpg)
Correctly Calculating min, max, and More: What Can Go Wrong?
 must be used with care, in even seemingly simple algorithms such as max and min.
• We will also discuss the use of operator < in other order-related algorithms, showing how easy China, all rights reserved
## The intuitive approach $ ② $
But those C++ templates ...
■ auto min (auto a, auto b) { return a < b ? a : b; }
■ auto max (auto a, auto b) { return b < a ? a :
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MAGED MICHAEL
& MICHAEL WONG
20
24
September 15 - 20
## Agenda
1. Improving C++20 Atomic Min/Max(P0493; Michael)
2. Hazard pointer extensions (P3135; Maged)
3. Pointer tagging (P3125; Maged) Michael), may be Parallel Algorithms (P2500)
C++26: Atomic Min/Max
# C++26: Improving C++20 Concurrency primitives atomic min/max (P0493)
## Atomic min/max motivation (P0493)
Long history - almost as old as operations, race conditions can occur, leading to data corruption and unpredictable behavior.
Atomic min/max operations provide a solution by ensuring that updates to the shared variable are performed atomically
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int x_min;
int x_max;
int y_min;
int y_max;
int x_max_min;
int x_max_max;
int y_min_max;
int y_max_min_max;
int x_max_min_max_min_max;
int x_max_max_min_max_max_min_max; x;
int y_min_max_min_max_max_max_min_max;
int y_max_min_max_max_max_max_max_min_max;
int x_max_max_min_max_max_max_max_min_max;
int y_max_min_max_max_max_max_max_min_max;
int x_max x_max_max_min_max_max_max_max_min_max;
int y_max_min_max_max_max_max_max_min_max;
int x_max_max_min_max_max_max_max_min_max;
int y_max_min_max_max_max_max_max_min_max;
int x_max_max_min_max_
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"min": 0,
"max": 0
}
{
"min": 0,
"max": 0
}
{
"min": 0,
"max": "min": 0,
"max": 0
}
{
"min": 0,
"max": 0
}
{
"min": 0,
"max": 0
}
{
"min": "min": 0,
"max": 0
}
{
"min": 0,
"max": 0
}
{
"min": 0,
"max": 0
}
{
"min":
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first make two reasonable assumptions:
1. We know that the value of x will lie between -10.0 ( $ x_{min} $ ) and 10.0 ( $ x_{max} $ ). Let us assume that this will always hold true. If not, the value of range. This means that all values of x are equally likely to lie in any part of the range from $ [x_{min}, x_{max}] $ , and there are no clusters of values in any part.
Now that we have the assumptions out see that floating-point $ x_{min} $ should map to 0, and $ x_{max} $ should map to $ 2^{b}-1 $ . How do we map the rest of the floating point values in between $ x_{min} $ and $ x_{max} $ to integer
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powerful Python data analysis toolkit, Release 0.13.1
min or max element respectively. Prior to 0.13.0 these would return the position of the min / max element.
(GH6214)
1.2.2 Prior Version Deprecations/Changes values=’MEAN_TEMP’)
df3 = pandas.concat([df2.min(), df2.mean(), df2.max()],
axis=1,keys=["Min Tem", "Mean Temp", "Max Temp"])
The resulting DataFrame is:
> df3
Min Tem
Mean Temp
Max Temp
MONTH
1
-53.336667 indexes during join operations (GH3877)
• Timestamp.min and Timestamp.max now represent valid Timestamp instances instead of the default date-
time.min and datetime.max (respectively), thanks @SleepingPills
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r>
min | returns the smaller of the given values (function template) | | ranges::min(C++20) | returns the smaller of the given values (niebloid) |
| min_element | returns t | returns the smallest element in a range (function template) |
| ranges::min_element(C++20) | returns the smallest element in a range (niebloid) |
| minmax(C++11) | returns /tr>
std::min_element
| Defined in header <algorithm> | template<class ForwardIt>ForwardIt min_element(ForwardIt first, ForwardIt 0 码力 |
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describe()
Out[38]:
B
A
1 count
1.000000
mean
4.000000
std
NaN
min
4.000000
25%
4.000000
50%
4.000000
75%
4.000000
...
...
5 mean
7.000000
std
1.414214
min
6.000000
25%
6.500000
50%
7.000000
75%
7.500000
max
8 B
0 count
2
1.000000
mean
1
4.000000
std
0
NaN
min
1
4.000000
25%
1
4.000000
50%
1
4.000000
75%
1
4.000000
...
..
...
1 mean
5
7.000000
std
0
1.414214
min
5
6.000000
25%
5
6.500000
50%
5
7.000000
75%
5 rolling-moment functions to dictate how to handle resampling; rolling_max() de-
faults to max, rolling_min() defaults to min, and all others default to mean (GH6297)
22
Chapter 1. What’s New
pandas: powerful Python 0 码力 |
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microseconds and nanoseconds separately.
# Timedelta accessor
In [9]: tds = Timedelta(’31 days 5 min 3 sec’)
In [10]: tds.minutes
Out[10]: 5L
In [11]: tds.seconds
Out[11]: 3L
# datetime.timedelta accessor Rolling/Expanding Moments improvements
• rolling_min(), rolling_max(), rolling_cov(), and rolling_corr() now return objects with
all NaN when len(arg) < min_periods <= window rather than raising. (This makes Series([10, 11, 12, 13])
In [15]: rolling_min(s, window=10, min_periods=5)
ValueError: min_periods (5) must be <= window (4)
New behavior
In [70]: rolling_min(s, window=10, min_periods=5)
Out[70]:
0
NaN
1
NaN
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indexes during join operations (GH3877)
• Timestamp.min and Timestamp.max now represent valid Timestamp instances instead of the default datetime.min and datetime.max (respectively), thanks @SleepingPills Series.
- deprecated the unique method, can be replicated by select_column(key, column).unique()
- min item size parameter to append will now automatically create data columns for passed keys
#### 1.2 return the calling object as before. A deprecation message has been added
• Groupby aggregations Max/Min no longer exclude non-numeric data (GH2700)
• Resampling an empty DataFrame now returns an empty DataFrame 0 码力 |
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min max and More[Image](/uploads/documents/b/e/1/2/be120c9b4aa84615315e317f8b6bd115/p1_1.jpg) Correctly Calculating min, max, and More: What Can Go Wrong?  must be used with care, in even seemingly simple algorithms such as max and min. • We will also discuss the use of operator < in other order-related algorithms, showing how easy China, all rights reserved ## The intuitive approach $ ② $ But those C++ templates ... ■ auto min (auto a, auto b) { return a < b ? a : b; } ■ auto max (auto a, auto b) { return b < a ? a :0 码力 | 8 页 | 2.48 MB | 1 年前3