AI1. 鸢尾花分类_1
描述
请编写代码实现train_and_predict功能，实现能够根据四个特征对三种类型的鸢尾花进行分类。
train_and_predict函数接收三个参数:
train_input_features—二维NumPy数组，其中每个元素都是一个数组，它包含:萼片长度、萼片宽度、花瓣长度和花瓣宽度。
train_outputs—一维NumPy数组，其中每个元素都是一个数字，表示在train_input_features的同一行中描述的鸢尾花种类。0表示鸢尾setosa，1表示versicolor，2代表Iris virginica。
prediction_features—二维NumPy数组，其中每个元素都是一个数组，包含:萼片长度、萼片宽度、花瓣长度和花瓣宽度。
该函数使用train_input_features作为输入数据，使用train_outputs作为预期结果来训练分类器。请使用训练过的分类器来预测prediction_features的标签，并将它们作为可迭代对象返回(如list或numpy.ndarray)。结果中的第n个位置是prediction_features参数的第n行。
Python 3 解法, 执行用时: 1102ms, 内存消耗: 524288KB, 提交时间: 2022-06-23
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
def train_and_predict(train_input_features, train_outputs, prediction_features):
clf = GaussianNB()
clf.fit(train_input_features, train_outputs)
y_pred = clf.predict(prediction_features)
return y_pred
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
test_size=0.3, random_state=0)
y_pred = train_and_predict(X_train, y_train, X_test)
if y_pred is not None:
print(metrics.accuracy_score(y_test, y_pred))
Python 3 解法, 执行用时: 1108ms, 内存消耗: 524288KB, 提交时间: 2022-06-24
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
def train_and_predict(train_input_features, train_outputs, prediction_features):
#code start here
clf = GaussianNB()
clf.fit(train_input_features, train_outputs)
y_pred = clf.predict(prediction_features)
return y_pred
#code end here
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
test_size=0.3, random_state=0)
y_pred = train_and_predict(X_train, y_train, X_test)
if y_pred is not None:
#code start here
print(metrics.accuracy_score(y_test, y_pred))
#code end here
Python 3 解法, 执行用时: 1119ms, 内存消耗: 524288KB, 提交时间: 2022-07-05
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
def train_and_predict(train_input_features, train_outputs, prediction_features):
clf = GaussianNB()
clf.fit(train_input_features, train_outputs)
y_pred = clf.predict(prediction_features)
return y_pred
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
test_size=0.3, random_state=0)
y_pred = train_and_predict(X_train, y_train, X_test)
if y_pred is not None:
print(metrics.accuracy_score(y_test, y_pred))
Python 3 解法, 执行用时: 1138ms, 内存消耗: 524288KB, 提交时间: 2022-07-17
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
def train_and_predict(train_input_features, train_outputs, prediction_features):
#code start here
knn = GaussianNB()
knn.fit(train_input_features, train_outputs)
return knn.predict(prediction_features)
#code end here
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
test_size=0.3, random_state=0)
y_pred = train_and_predict(X_train, y_train, X_test)
if y_pred is not None:
#code start here
print(metrics.accuracy_score(y_test,y_pred))
#code end here
Python 3 解法, 执行用时: 1140ms, 内存消耗: 524288KB, 提交时间: 2022-07-02
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
def train_and_predict(train_input_features, train_outputs, prediction_features):
#code start here
gnb = GaussianNB()
gnb.fit(train_input_features, train_outputs)
return gnb.predict(prediction_features)
#code end here
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
test_size=0.3, random_state=0)
y_pred = train_and_predict(X_train, y_train, X_test)
if y_pred is not None:
#code start here
r = 0
for i in range(0, len(y_pred)):
if y_pred[i] == y_test[i]:
r = r + 1
print(r / len(y_pred))
#code end here