質問編集履歴
5
文法修正
test
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test
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@@ -69,9 +69,11 @@
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x_combined = X_train.iloc[:, [0,1]].values
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y_combined = y_train.values
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-
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+
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d = {'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}
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y = np.array(list(map(lambda i : d[i], y_combined)))
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+
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+
model.fit(x_combined, y)
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print(np.unique(np.array(y)))
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4
書式改善
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test
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@@ -14,8 +14,13 @@
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### 発生している問題・エラーメッセージ
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```
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+
plot_decision_regions( x_combined, y, clf=model)と書いている行で
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+
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-
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can only concatenate str (not "int") to str
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+
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というエラーが出ています。
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```
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+
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### 該当のソースコード
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@@ -31,38 +36,42 @@
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df = pd.read_csv("iris.csv")
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df.head()
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38
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-
X = df[["SepalLength","SepalWidth","PetalLength","PetalWidth"]]
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X = df.loc[:, ["SepalLength","SepalWidth","PetalLength","PetalWidth"]]
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-
y = df["Name"]
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y = df.loc[:, "Name"]
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+
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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model = svm.SVC()
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model.fit(X_train,y_train)
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X_train.shape, X_test.shape
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40
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-
data_train = pd.DataFrame(X_train)
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#data_train = pd.DataFrame(X_train)
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-
data_train["Name"] = y_train
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#data_train["Name"] = y_train
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-
sns.pairplot(data_train, hue='Name', palette="husl")
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#sns.pairplot(data_train, hue='Name', palette="husl")
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-
X_train = data_train[["SepalLength","SepalWidth","PetalLength","PetalWidth"]].values
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#X_train = data_train[["SepalLength","SepalWidth","PetalLength","PetalWidth"]].values
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-
y_train = data_train["Name"].values
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#y_train = data_train["Name"].values
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-
from sklearn import svm,metrics
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-
model = svm.SVC()
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-
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-
pre =
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pre = model.predict(X_test)
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ac_score = metrics.accuracy_score(y_test,pre)
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print("正解率=", ac_score)
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+
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#ここからデータ可視化
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import matplotlib.pyplot as plt
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from mlxtend.plotting import plot_decision_regions
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%matplotlib inline
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-
x_combined = X_t
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x_combined = X_train.iloc[:, [0,1]].values
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-
y_combined = y_t
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y_combined = y_train.values
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model.fit(x_combined, y_combined)
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d = {'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}
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y = list(map(lambda i : d[i], y_combined))
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y = np.array(list(map(lambda i : d[i], y_combined)))
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print(np.unique(np.array(y)))
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3
文法の追加
test
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test
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@@ -61,10 +61,13 @@
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y_combined = y_test.values
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model.fit(x_combined, y_combined)
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d = {'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}
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y = list(map(lambda i : d[i], y_combined))
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print(np.unique(np.array(y)))
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fig = plt.figure(figsize=(13,8))
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plot_decision_regions( x_combined, y
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plot_decision_regions( x_combined, y, clf=model)
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plt.show()
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```
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2
誤字修正
test
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test
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@@ -20,17 +20,6 @@
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### 該当のソースコード
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```python
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import numpy as np
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import pandas as pd
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import seaborn as sns
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sns.set_style("whitegrid")
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import matplotlib.pyplot as plt
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%matplotlib inline
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from sklearn.model_selection import train_test_split, cross_validate
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df = pd.read_csv("iris.csv")
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df.head()
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-
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import numpy as np
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import pandas as pd
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import seaborn as sns
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1
誤字修正
test
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test
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@@ -75,7 +75,7 @@
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fig = plt.figure(figsize=(13,8))
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plot_decision_regions( x_combined,
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+
plot_decision_regions( x_combined, y_combined, clf=model)
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plt.show()
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```
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