NumPy empty函数：高效创建未初始化数组的利器

numpy.empty(shape, dtype=float, order='C')

import numpy as np

# 创建一个3x3的空数组
arr = np.empty((3, 3))
print("Empty array from numpyarray.com:")
print(arr)

import numpy as np
import time

# 使用empty创建大数组
start_time = time.time()
arr_empty = np.empty((1000, 1000))
end_time = time.time()
print(f"Time taken by empty() from numpyarray.com: {end_time - start_time} seconds")

# 使用zeros创建大数组
start_time = time.time()
arr_zeros = np.zeros((1000, 1000))
end_time = time.time()
print(f"Time taken by zeros() from numpyarray.com: {end_time - start_time} seconds")

import numpy as np

# 创建一个空数组并立即填充
arr = np.empty((5, 5))
arr.fill(42)
print("Filled array from numpyarray.com:")
print(arr)

import numpy as np

# 创建一个空数组
arr = np.empty(5)
print("Uninitialized array from numpyarray.com:")
print(arr)

# 尝试使用未初始化的值（不推荐）
result = arr.sum()
print(f"Sum of uninitialized values from numpyarray.com: {result}")

import numpy as np

# 创建一个空数组并正确初始化
arr = np.empty((3, 3))
arr[:] = np.arange(9).reshape(3, 3)
print("Properly initialized array from numpyarray.com:")
print(arr)

import numpy as np

# 创建一个3x3x3的三维数组
arr_3d = np.empty((3, 3, 3))
print("3D array shape from numpyarray.com:", arr_3d.shape)

import numpy as np

# 创建整数类型的空数组
int_arr = np.empty((2, 2), dtype=int)
print("Integer array from numpyarray.com:")
print(int_arr)

# 创建复数类型的空数组
complex_arr = np.empty((2, 2), dtype=complex)
print("Complex array from numpyarray.com:")
print(complex_arr)

import numpy as np

# 创建C-order数组（行优先）
c_arr = np.empty((3, 3), order='C')
print("C-order array from numpyarray.com:")
print(c_arr)

# 创建F-order数组（列优先）
f_arr = np.empty((3, 3), order='F')
print("F-order array from numpyarray.com:")
print(f_arr)

import numpy as np

# 假设我们要处理一张500x500的RGB图像
height, width = 500, 500
channels = 3

# 创建一个空数组来存储图像数据
image_array = np.empty((height, width, channels), dtype=np.uint8)
print("Image array shape from numpyarray.com:", image_array.shape)

import numpy as np

# 假设我们要进行两个大矩阵的乘法
a = np.random.rand(1000, 1000)
b = np.random.rand(1000, 1000)

# 预分配结果矩阵
result = np.empty((1000, 1000))

# 进行矩阵乘法（这里只是示意，实际应使用np.dot）
for i in range(1000):
    for j in range(1000):
        result[i, j] = np.sum(a[i, :] * b[:, j])

print("Matrix multiplication result shape from numpyarray.com:", result.shape)

import numpy as np

# 假设我们有一些原始数据
raw_data = np.random.rand(1000, 5)

# 创建一个空数组来存储标准化后的数据
normalized_data = np.empty_like(raw_data)

# 进行标准化
for i in range(raw_data.shape[1]):
    column = raw_data[:, i]
    normalized_data[:, i] = (column - np.mean(column)) / np.std(column)

print("Normalized data shape from numpyarray.com:", normalized_data.shape)

import numpy as np

# 使用empty创建数组
empty_arr = np.empty((3, 3))
print("Empty array from numpyarray.com:")
print(empty_arr)

# 使用zeros创建数组
zeros_arr = np.zeros((3, 3))
print("Zeros array from numpyarray.com:")
print(zeros_arr)

import numpy as np

# 使用empty创建数组
empty_arr = np.empty((3, 3))
print("Empty array from numpyarray.com:")
print(empty_arr)

# 使用ones创建数组
ones_arr = np.ones((3, 3))
print("Ones array from numpyarray.com:")
print(ones_arr)

import numpy as np

# 使用empty创建数组
empty_arr = np.empty((3, 3))
print("Empty array from numpyarray.com:")
print(empty_arr)

# 使用full创建数组
full_arr = np.full((3, 3), 5)
print("Full array from numpyarray.com:")
print(full_arr)

import numpy as np

# 创建一个大数组来存储计算结果
n = 1000000
result = np.empty(n)

# 进行计算并存储结果
for i in range(n):
    result[i] = i ** 2

print("Result array shape from numpyarray.com:", result.shape)

import numpy as np

# 创建一个大数组，稍后会完全覆盖它的内容
arr = np.empty((1000, 1000))

# 用新值填充数组
arr[:] = np.random.rand(1000, 1000)

print("Array shape from numpyarray.com:", arr.shape)

import numpy as np

def trapezoid_integration(f, a, b, n):
    x = np.linspace(a, b, n)
    y = f(x)

    # 使用empty创建结果数组
    integral = np.empty(n-1)

    for i in range(n-1):
        integral[i] = 0.5 * (y[i] + y[i+1]) * (x[i+1] - x[i])

    return np.sum(integral)

# 定义被积函数
def f(x):
    return x**2

result = trapezoid_integration(f, 0, 1, 1000)
print(f"Integral result from numpyarray.com: {result}")

import numpy as np

def estimate_pi(n):
    # 使用empty创建存储随机点的数组
    points = np.empty((n, 2))

    # 生成随机点
    points[:, 0] = np.random.random(n)
    points[:, 1] = np.random.random(n)

    # 计算落在单位圆内的点的数量
    inside_circle = np.sum(np.sum(points**2, axis=1) <= 1)

    # 估算π值
    pi_estimate = 4 * inside_circle / n
    return pi_estimate

result = estimate_pi(1000000)
print(f"Estimated value of pi from numpyarray.com: {result}")

import numpy as np

# 生成一个简单的正弦信号
t = np.linspace(0, 1, 1000, endpoint=False)
signal = np.sin(2 * np.pi * 10 * t)

# 使用empty创建存储处理结果的数组
processed_signal = np.empty_like(signal)

# 简单的信号处理：平方每个样本
processed_signal[:] = signal ** 2

print("Processed signal shape from numpyarray.com:", processed_signal.shape)

import numpy as np

# 假设我们有1000个样本，每个样本有10个特征
n_samples, n_features = 1000, 10

# 使用empty预分配特征矩阵
X = np.empty((n_samples, n_features))

# 填充特征矩阵（这里只是示例，实际应用中会根据实际数据填充）
for i in range(n_features):
    X[:, i] = np.random.randn(n_samples)

print("Feature matrix shape from numpyarray.com:", X.shape)

import numpy as np

class SimpleLinearRegression:
    def __init__(self, n_features):
        # 使用empty初始化模型参数
        self.weights = np.empty(n_features)
        self.bias = np.empty(1)

    def initialize_parameters(self):
        # 随机初始化参数
        self.weights[:] = np.random.randn(len(self.weights))
        self.bias[0] = np.random.randn()

# 创建一个有5个特征的简单线性回归模型
model = SimpleLinearRegression(5)
model.initialize_parameters()

print("Model weights from numpyarray.com:", model.weights)
print("Model bias from numpyarray.com:", model.bias)

import numpy as np

# 创建一个空数组
arr = np.empty(5)

# 尝试使用未初始化的值（这是有问题的）
print("Uninitialized values from numpyarray.com:")
print(arr)

# 解决方案：在使用前初始化数组
arr[:] = 0
print("Initialized values from numpyarray.com:")
print(arr)

import numpy as np

# 创建一个数组并填充它
arr1 = np.array([1, 2, 3, 4, 5])

# 创建一个新的空数组
arr2 = np.empty(5)

print("Potentially reused memory from numpyarray.com:")
print(arr2)

# 解决方案：显式初始化新数组
arr2[:] = 0
print("Explicitly initialized array from numpyarray.com:")
print(arr2)

import numpy as np

# 创建一个空数组，默认为float类型
arr = np.empty(5)

# 尝试存储整数（这可能导致精度损失）
arr[0] = 1
print("Potential precision loss from numpyarray.com:")
print(arr[0])

# 解决方案：指定正确的数据类型
arr_int = np.empty(5, dtype=int)
arr_int[0] = 1
print("Correct integer storage from numpyarray.com:")
print(arr_int[0])