Explicit @jit signatures can use a number of types. Here are some common ones:
void is the return type of functions returning nothing (which actually return None when called from Python) intp and uintp are pointer-sized integers (signed and unsigned, respectively) intc and uintc are equivalent to C int and unsigned int integer types int8, uint8, int16, uint16, int32, uint32, int64, uint64 are fixed-width integers of the corresponding bit width (signed and unsigned) float32 and float64 are single- and double-precision floating-point numbers, respectively complex64 and complex128 are single- and double-precision complex numbers, respectively array types can be specified by indexing any numeric type, e.g. float32[:] for a one-dimensional single-precision array or int8[:,:] for a two-dimensional array of 8-bit integers.
@vectorize([int32(int32, int32),
int64(int64, int64),
float32(float32, float32),
float64(float64, float64)])
def f(x, y):
return x + y
如果给定的类型正确：
>>> a = np.arange(6)
>>> f(a, a)
array([ 0, 2, 4, 6, 8, 10])
>>> a = np.linspace(0, 1, 6)
>>> f(a, a)
array([ 0. , 0.4, 0.8, 1.2, 1.6, 2. ])
如果提供了不支持的类型：
>>> a = np.linspace(0, 1+1j, 6)
>>> f(a, a)
Traceback (most recent call last):
File "", line 1, in
TypeError: ufunc 'ufunc' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
vectorizer与jit装饰器的差别：numpy的ufunc自动加载其他特性，例如：reduction, accumulation or broadcasting:
import numpy as np
from numba import jitclass # import the decorator
from numba import int32, float32 # import the types
spec = [
('value', int32), # a simple scalar field
('array', float32[:]), # an array field
]
@jitclass(spec)
class Bag(object):
def __init__(self, value):
self.value = value
self.array = np.zeros(value, dtype=np.float32)
@property
def size(self):
return self.array.size
def increment(self, val):
for i in range(self.size):
self.array[i] = val
return self.array
@njit
def ident_np(x):
return np.cos(x) ** 2 + np.sin(x) ** 2
@njit
def ident_loops(x):
r = np.empty_like(x)
n = len(x)
for i in range(n):
r[i] = np.cos(x[i]) ** 2 + np.sin(x[i]) ** 2
return r
Function Name
@njit
Execution time
ident_np
No
0.581s
ident_np
Yes
0.659s
ident_loops
No
25.2s
ident_loops
Yes
0.670s
有时候不那么严格的规定数据将会带来性能的提升，此时，恶意使用fastmath关键字参数：
@njit(fastmath=False)
def do_sum(A):
acc = 0.
# without fastmath, this loop must accumulate in strict order
for x in A:
acc += np.sqrt(x)
return acc
@njit(fastmath=True)
def do_sum_fast(A):
acc = 0.
# with fastmath, the reduction can be vectorized as floating point
# reassociation is permitted.
for x in A:
acc += np.sqrt(x)
return acc
In [1]: from numba import jit
In [2]: @jit(nopython=True)
...: def f(x):
...: if x > 10:
...: return (1,)
...: else:
...: return 1
...:
尝试执行这个函数，就会得到以下的错误：
In [3]: f(10)
TypingError: Failed at nopython (nopython frontend)
Can't unify return type from the following types: tuple(int64 x 1), int64
Return of: IR name '$8.2', type '(int64 x 1)', location:
File "", line 4:
def f(x):
if x > 10:
return (1,)
^
Return of: IR name '$12.2', type 'int64', location:
File "", line 6:
def f(x):
else:
return 1
错误信息： “Can’t unify return type from the following types: tuple(int64 x 1), int64” 可以理解为： “Numba cannot find a type that can safely represent a 1-tuple of integer and an integer”.
def make_f(g):
# Note: a new f() is compiled each time make_f() is called!
@jit(nopython=True)
def f(x):
return g(x) + g(-x)
return f
f = make_f(my_g_function)
result = f(1)
1 svn回退版本
1)在window中选择log,根据想要回退的内容,选择revert this version或revert chanages from this version
两者的区别:
revert this version:表示回退到当前版本(该版本后的版本全部作废)
revert chanages from this versio
<!--javascript取当月最后一天-->
<script language=javascript>
var current = new Date();
var year = current.getYear();
var month = current.getMonth();
showMonthLastDay(year, mont
public class MyStack {
private long[] arr;
private int top;
public MyStack() {
arr = new long[10];
top = -1;
}
public MyStack(int maxsize) {
arr = new long[maxsize];
top
Binary search needs an ordered array so that it can use array indexing to dramatically reduce the number of compares required for each search, using the classic and venerable binary search algori
