NumPy Zeros: np.zeros()でゼロで埋められた配列を作成する

import numpy as np
 
# 5つのゼロの1D配列を作成
a = np.zeros(5)
print(a)        # [0. 0. 0. 0. 0.]
print(a.dtype)  # float64 (default)
print(a.shape)  # (5,)

import numpy as np
 
# 3行、4列
matrix = np.zeros((3, 4))
print(matrix)
# [[0. 0. 0. 0.]
#  [0. 0. 0. 0.]
#  [0. 0. 0. 0.]]
print(matrix.shape)  # (3, 4)

import numpy as np
 
# 2層、3行、4列
tensor = np.zeros((2, 3, 4))
print(tensor.shape)  # (2, 3, 4)
print(tensor.size)   # 24 elements total

import numpy as np
 
# 整数のゼロ
int_zeros = np.zeros(5, dtype=int)
print(int_zeros)       # [0 0 0 0 0]
print(int_zeros.dtype) # int64
 
# ブールのゼロ（False）
bool_zeros = np.zeros(5, dtype=bool)
print(bool_zeros)  # [False False False False False]
 
# 複素数のゼロ
complex_zeros = np.zeros(3, dtype=complex)
print(complex_zeros)  # [0.+0.j 0.+0.j 0.+0.j]
 
# 32ビット浮動小数点（メモリ節約）
small_zeros = np.zeros((1000, 1000), dtype=np.float32)
print(f"Memory: {small_zeros.nbytes / 1024 / 1024:.1f} MB")  # ~3.8 MB vs 7.6 MB for float64

import numpy as np
 
original = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
 
# 同じ形状とdtype
zeros_copy = np.zeros_like(original)
print(zeros_copy)
# [[0. 0. 0.]
#  [0. 0. 0.]]
print(zeros_copy.dtype)  # float32
print(zeros_copy.shape)  # (2, 3)
 
# dtypeを上書き
int_copy = np.zeros_like(original, dtype=int)
print(int_copy.dtype)  # int64

import numpy as np
 
# 1の配列
print(np.ones((2, 3)))
# [[1. 1. 1.]
#  [1. 1. 1.]]
 
# Full（カスタム値）
print(np.full((2, 3), 7))
# [[7 7 7]
#  [7 7 7]]
 
# Empty（未初期化 -- より高速だがゴミが含まれる）
empty = np.empty((2, 3))  # Contents unpredictable
 
# 単位行列
print(np.eye(3))
# [[1. 0. 0.]
#  [0. 1. 0.]
#  [0. 0. 1.]]

import numpy as np
 
# パフォーマンスのための事前割り当て（繰り返しの連結を回避）
n_iterations = 1000
results = np.zeros(n_iterations)
 
for i in range(n_iterations):
    results[i] = np.random.randn() ** 2
 
print(f"Mean: {results.mean():.4f}")
print(f"Std:  {results.std():.4f}")

import numpy as np
 
def confusion_matrix(y_true, y_pred, n_classes):
    """Build a confusion matrix from predictions."""
    cm = np.zeros((n_classes, n_classes), dtype=int)
    for true, pred in zip(y_true, y_pred):
        cm[true][pred] += 1
    return cm
 
y_true = [0, 1, 2, 0, 1, 2, 0, 0, 1, 2]
y_pred = [0, 1, 1, 0, 2, 2, 0, 1, 1, 0]
 
cm = confusion_matrix(y_true, y_pred, 3)
print(cm)
# [[3 1 0]
#  [0 2 1]
#  [1 0 2]]

import numpy as np
 
signal = np.array([1, 2, 3, 4, 5])
 
# ゼロで長さ16まで埋める（FFTのための2の冪乗）
padded = np.zeros(16)
padded[:len(signal)] = signal
print(padded)
# [1. 2. 3. 4. 5. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 
# np.padを使用（より柔軟）
padded2 = np.pad(signal, (0, 11), mode='constant', constant_values=0)
print(padded2)

import numpy as np
 
# 元の3x3画像
image = np.array([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])
 
# 1ピクセルのゼロパディングを追加
padded = np.zeros((5, 5), dtype=image.dtype)
padded[1:-1, 1:-1] = image
print(padded)
# [[0 0 0 0 0]
#  [0 1 2 3 0]
#  [0 4 5 6 0]
#  [0 7 8 9 0]
#  [0 0 0 0 0]]

import numpy as np
 
# ヒストグラムにカウントを累積
data = np.random.randint(0, 10, size=1000)
bins = np.zeros(10, dtype=int)
 
for value in data:
    bins[value] += 1
 
print("Histogram:")
for i, count in enumerate(bins):
    bar = '#' * (count // 5)
    print(f"  {i}: {bar} ({count})")