from sklearn import datasets
from matplotlib import pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings("ignore") # 将警告设为为忽略
iris = datasets.load_iris(0)
iris
{'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2],
[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]]),
'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),
'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
'DESCR': 'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\'s paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...\n',
'feature_names': ['sepal length (cm)',
'sepal width (cm)',
'petal length (cm)',
'petal width (cm)']}
type(iris)
sklearn.utils.Bunch
iris.keys()
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])
# iris['DESCR']
print(iris.DESCR)
Iris Plants Database
====================
Notes
-----
Data Set Characteristics:
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris-Versicolour
- Iris-Virginica
:Summary Statistics:
============== ==== ==== ======= ===== ====================
Min Max Mean SD Class Correlation
============== ==== ==== ======= ===== ====================
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
============== ==== ==== ======= ===== ====================
:Missing Attribute Values: None
:Class Distribution: 33.3% for each of 3 classes.
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris
The famous Iris database, first used by Sir R.A Fisher
This is perhaps the best known database to be found in the
pattern recognition literature. Fisher's paper is a classic in the field and
is referenced frequently to this day. (See Duda & Hart, for example.) The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant. One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.
References
----------
- Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
Mathematical Statistics" (John Wiley, NY, 1950).
- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
on Information Theory, May 1972, 431-433.
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
- Many, many more ...
X.ndim
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-9-4fdb863cdb3a> in <module>()
----> 1 X.ndim
NameError: name 'X' is not defined
X.shape
X.size
iris.feature_names
y = iris.target
y
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
iris.target_names
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
# 绘制萼片维度
X = iris.data[:,:2]
X
array([[5.1, 3.5],
[4.9, 3. ],
[4.7, 3.2],
[4.6, 3.1],
[5. , 3.6],
[5.4, 3.9],
[4.6, 3.4],
[5. , 3.4],
[4.4, 2.9],
[4.9, 3.1],
[5.4, 3.7],
[4.8, 3.4],
[4.8, 3. ],
[4.3, 3. ],
[5.8, 4. ],
[5.7, 4.4],
[5.4, 3.9],
[5.1, 3.5],
[5.7, 3.8],
[5.1, 3.8],
[5.4, 3.4],
[5.1, 3.7],
[4.6, 3.6],
[5.1, 3.3],
[4.8, 3.4],
[5. , 3. ],
[5. , 3.4],
[5.2, 3.5],
[5.2, 3.4],
[4.7, 3.2],
[4.8, 3.1],
[5.4, 3.4],
[5.2, 4.1],
[5.5, 4.2],
[4.9, 3.1],
[5. , 3.2],
[5.5, 3.5],
[4.9, 3.1],
[4.4, 3. ],
[5.1, 3.4],
[5. , 3.5],
[4.5, 2.3],
[4.4, 3.2],
[5. , 3.5],
[5.1, 3.8],
[4.8, 3. ],
[5.1, 3.8],
[4.6, 3.2],
[5.3, 3.7],
[5. , 3.3],
[7. , 3.2],
[6.4, 3.2],
[6.9, 3.1],
[5.5, 2.3],
[6.5, 2.8],
[5.7, 2.8],
[6.3, 3.3],
[4.9, 2.4],
[6.6, 2.9],
[5.2, 2.7],
[5. , 2. ],
[5.9, 3. ],
[6. , 2.2],
[6.1, 2.9],
[5.6, 2.9],
[6.7, 3.1],
[5.6, 3. ],
[5.8, 2.7],
[6.2, 2.2],
[5.6, 2.5],
[5.9, 3.2],
[6.1, 2.8],
[6.3, 2.5],
[6.1, 2.8],
[6.4, 2.9],
[6.6, 3. ],
[6.8, 2.8],
[6.7, 3. ],
[6. , 2.9],
[5.7, 2.6],
[5.5, 2.4],
[5.5, 2.4],
[5.8, 2.7],
[6. , 2.7],
[5.4, 3. ],
[6. , 3.4],
[6.7, 3.1],
[6.3, 2.3],
[5.6, 3. ],
[5.5, 2.5],
[5.5, 2.6],
[6.1, 3. ],
[5.8, 2.6],
[5. , 2.3],
[5.6, 2.7],
[5.7, 3. ],
[5.7, 2.9],
[6.2, 2.9],
[5.1, 2.5],
[5.7, 2.8],
[6.3, 3.3],
[5.8, 2.7],
[7.1, 3. ],
[6.3, 2.9],
[6.5, 3. ],
[7.6, 3. ],
[4.9, 2.5],
[7.3, 2.9],
[6.7, 2.5],
[7.2, 3.6],
[6.5, 3.2],
[6.4, 2.7],
[6.8, 3. ],
[5.7, 2.5],
[5.8, 2.8],
[6.4, 3.2],
[6.5, 3. ],
[7.7, 3.8],
[7.7, 2.6],
[6. , 2.2],
[6.9, 3.2],
[5.6, 2.8],
[7.7, 2.8],
[6.3, 2.7],
[6.7, 3.3],
[7.2, 3.2],
[6.2, 2.8],
[6.1, 3. ],
[6.4, 2.8],
[7.2, 3. ],
[7.4, 2.8],
[7.9, 3.8],
[6.4, 2.8],
[6.3, 2.8],
[6.1, 2.6],
[7.7, 3. ],
[6.3, 3.4],
[6.4, 3.1],
[6. , 3. ],
[6.9, 3.1],
[6.7, 3.1],
[6.9, 3.1],
[5.8, 2.7],
[6.8, 3.2],
[6.7, 3.3],
[6.7, 3. ],
[6.3, 2.5],
[6.5, 3. ],
[6.2, 3.4],
[5.9, 3. ]])
# X[y==0][:,0] # setosa 第一列
X[y==0,0]
array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. ])
# 绘制萼片散点图
plt.scatter(X[y==0, 0],X[y==0, 1],color='r',marker = '+' ) # setosa
plt.scatter(X[y==1, 0],X[y==1, 1],color='b',marker = 'x') # versicolor
plt.scatter(X[y==2, 0],X[y==2, 1],color='g',marker = 'o') # 'virginica
plt.show()
output_17_0.png
# 绘制花瓣维度
X = iris.data[:,2:]
X
array([[1.4, 0.2],
[1.4, 0.2],
[1.3, 0.2],
[1.5, 0.2],
[1.4, 0.2],
[1.7, 0.4],
[1.4, 0.3],
[1.5, 0.2],
[1.4, 0.2],
[1.5, 0.1],
[1.5, 0.2],
[1.6, 0.2],
[1.4, 0.1],
[1.1, 0.1],
[1.2, 0.2],
[1.5, 0.4],
[1.3, 0.4],
[1.4, 0.3],
[1.7, 0.3],
[1.5, 0.3],
[1.7, 0.2],
[1.5, 0.4],
[1. , 0.2],
[1.7, 0.5],
[1.9, 0.2],
[1.6, 0.2],
[1.6, 0.4],
[1.5, 0.2],
[1.4, 0.2],
[1.6, 0.2],
[1.6, 0.2],
[1.5, 0.4],
[1.5, 0.1],
[1.4, 0.2],
[1.5, 0.1],
[1.2, 0.2],
[1.3, 0.2],
[1.5, 0.1],
[1.3, 0.2],
[1.5, 0.2],
[1.3, 0.3],
[1.3, 0.3],
[1.3, 0.2],
[1.6, 0.6],
[1.9, 0.4],
[1.4, 0.3],
[1.6, 0.2],
[1.4, 0.2],
[1.5, 0.2],
[1.4, 0.2],
[4.7, 1.4],
[4.5, 1.5],
[4.9, 1.5],
[4. , 1.3],
[4.6, 1.5],
[4.5, 1.3],
[4.7, 1.6],
[3.3, 1. ],
[4.6, 1.3],
[3.9, 1.4],
[3.5, 1. ],
[4.2, 1.5],
[4. , 1. ],
[4.7, 1.4],
[3.6, 1.3],
[4.4, 1.4],
[4.5, 1.5],
[4.1, 1. ],
[4.5, 1.5],
[3.9, 1.1],
[4.8, 1.8],
[4. , 1.3],
[4.9, 1.5],
[4.7, 1.2],
[4.3, 1.3],
[4.4, 1.4],
[4.8, 1.4],
[5. , 1.7],
[4.5, 1.5],
[3.5, 1. ],
[3.8, 1.1],
[3.7, 1. ],
[3.9, 1.2],
[5.1, 1.6],
[4.5, 1.5],
[4.5, 1.6],
[4.7, 1.5],
[4.4, 1.3],
[4.1, 1.3],
[4. , 1.3],
[4.4, 1.2],
[4.6, 1.4],
[4. , 1.2],
[3.3, 1. ],
[4.2, 1.3],
[4.2, 1.2],
[4.2, 1.3],
[4.3, 1.3],
[3. , 1.1],
[4.1, 1.3],
[6. , 2.5],
[5.1, 1.9],
[5.9, 2.1],
[5.6, 1.8],
[5.8, 2.2],
[6.6, 2.1],
[4.5, 1.7],
[6.3, 1.8],
[5.8, 1.8],
[6.1, 2.5],
[5.1, 2. ],
[5.3, 1.9],
[5.5, 2.1],
[5. , 2. ],
[5.1, 2.4],
[5.3, 2.3],
[5.5, 1.8],
[6.7, 2.2],
[6.9, 2.3],
[5. , 1.5],
[5.7, 2.3],
[4.9, 2. ],
[6.7, 2. ],
[4.9, 1.8],
[5.7, 2.1],
[6. , 1.8],
[4.8, 1.8],
[4.9, 1.8],
[5.6, 2.1],
[5.8, 1.6],
[6.1, 1.9],
[6.4, 2. ],
[5.6, 2.2],
[5.1, 1.5],
[5.6, 1.4],
[6.1, 2.3],
[5.6, 2.4],
[5.5, 1.8],
[4.8, 1.8],
[5.4, 2.1],
[5.6, 2.4],
[5.1, 2.3],
[5.1, 1.9],
[5.9, 2.3],
[5.7, 2.5],
[5.2, 2.3],
[5. , 1.9],
[5.2, 2. ],
[5.4, 2.3],
[5.1, 1.8]])
X[y==0,0]
array([1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4,
1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1. , 1.7, 1.9, 1.6,
1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.5, 1.3,
1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4])
# 绘制花瓣散点图
plt.scatter(X[y==0, 0],X[y==0, 1],color='r',marker = '+' ) # setosa
plt.scatter(X[y==1, 0],X[y==1, 1],color='b',marker = 'x') # versicolor
plt.scatter(X[y==2, 0],X[y==2, 1],color='g',marker = 'o') # 'virginica
plt.show()
output_20_0.png
