import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

data = pd.read_csv(r"C:\Users\28747\Desktop\pca\iris.csv")
print(data)
data = (np.array(data))
pca_data = data
(r, c) = np.shape(data)
new_data = data
meanval = np.mean(data, axis=0)  # 计算原始数据中每一列的均值，axis=0按列取均值
for i in range(0, r):
    for j in range(0, c):
        new_data[i][j] -= meanval[j]
data = np.cov(data, rowvar=False)  # 计算协方差矩阵,rowvar=0每一列为一个特征。
vals, vecs = np.linalg.eig(data)  # 特征值：vals，特征向量：vecs
index = np.argsort(vals)  # 将特征值按从大到小排序，index保留的是对应原featValue中的下标
index = np.flipud(index)
new_dimension = int(input("new dimension:"))
n_featVec = vecs[index, 0:new_dimension]  # 注意上面矩阵的列向量才是特征向量
lowData = np.dot(new_data, n_featVec)
print(lowData)
print("##################################################")
from sklearn import decomposition

pca = decomposition.PCA()
pca.fit(pca_data)
pca.n_components = new_dimension
X_reduced = pca.fit_transform(pca_data)
print(X_reduced)
if new_dimension == 2:
    plt.scatter(lowData[:, 0], lowData[:, 1],label="my pca")
    plt.title("compare")
    plt.xlabel("first component")
    plt.ylabel("second component")
    plt.scatter(X_reduced[:, 0], X_reduced[:, 1],label="sklearn's pca")
    plt.xlabel("first component")
    plt.ylabel("second component")
    plt.legend(loc="lower left")
    plt.show()

150    4  setosa  versicolor  virginica
0    5.1  3.5     1.4         0.2          0
1    4.9  3.0     1.4         0.2          0
2    4.7  3.2     1.3         0.2          0
3    4.6  3.1     1.5         0.2          0
4    5.0  3.6     1.4         0.2          0
..   ...  ...     ...         ...        ...
145  6.7  3.0     5.2         2.3          2
146  6.3  2.5     5.0         1.9          2
147  6.5  3.0     5.2         2.0          2
148  6.2  3.4     5.4         2.3          2
149  5.9  3.0     5.1         1.8          2

[150 rows x 5 columns]
new dimension:2
[[ 2.86541481e+00  2.96294592e-01]
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##################################################
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