Untitled
unknown
plain_text
a year ago
10 kB
3
Indexable
Never
Мария Перебейнова, [21.05.2023 0:01]
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
def load_split():
diabet = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(diabet.data, diabet.target, random_state = 42, test_size=0.5)
return X_train, y_train
Мария Перебейнова, [21.05.2023 0:01]
from sklearn.linear_model import LinearRegression
def uniform(X_train,y_train):
ans = LinearRegression().fit(X_train, y_train)
return ans.coef_
def predict_model(model,X_test):
return model.predict(X_test)
Мария Перебейнова, [21.05.2023 0:01]
from sklearn.linear_model import LinearRegression
def uniform(X_train,y_train):
ans = LinearRegression().fit(X_train, y_train)
return ans.coef_
def predict_model(model,X_test):
return model.predict(X_test)
Мария Перебейнова, [21.05.2023 0:02]
from sklearn import datasets
from sklearn.model_selection import train_test_split
def load_split():
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, test_size=0.8, random_state=42)
return X_train[:15],y_train[:15]
Мария Перебейнова, [21.05.2023 0:02]
from sklearn.linear_model import LogisticRegression
def train_model(X_train,y_train):
ans = LogisticRegression(random_state = 42).fit(X_train, y_train)
return ans.coef_
def model_predict(X_test, model):
return model.predict(X_test)[:15]
Мария Перебейнова, [21.05.2023 0:02]
from sklearn.linear_model import LogisticRegression
def train_model(X_train,y_train):
ans = LogisticRegression(random_state = 42).fit(X_train, y_train)
return ans.coef_
def model_predict(X_test, model):
return model.predict(X_test)[:15]
Мария Перебейнова, [21.05.2023 0:02]
from sklearn.metrics import accuracy_score
def accuracy(y_test,y_pred):
return (y_test==y_pred).mean()
Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np
def uniform(X, Y):
scaler = StandardScaler()
scaled_data = scaler.fit_transform(X)
mean = np.mean(scaled_data)
variance = np.var(scaled_data)
mean_rounded = round(mean, 3)
variance_rounded = round(variance, 3)
return mean_rounded, variance_rounded
Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
def uniform(X, Y):
scaler = MinMaxScaler()
res = scaler.fit_transform(X,Y)
mo = res.mean()
ds = res.std()
mean = np.round(mo, 3)
variance = np.round(ds, 3)
return mean, variance
Мария Перебейнова, [21.05.2023 0:02]
from sklearn import preprocessing
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd
from sklearn.preprocessing import MaxAbsScaler
import numpy as np
def uniform(X, Y):
scaler = MaxAbsScaler()
res = scaler.fit_transform(X,Y)
mo = res.mean()
ds = res.std()
mean = np.round(mo, 3)
variance = np.round(ds, 3)
return mean, variance
Мария Перебейнова, [21.05.2023 0:03]
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing import RobustScaler
def uniform(X, Y):
scaler = RobustScaler()
res = scaler.fit_transform(X,Y)
mo = res.mean()
ds = res.std()
mean = np.round(mo, 3)
variance = np.round(ds, 3)
return mean, variance
Мария Перебейнова, [21.05.2023 0:03]
from sklearn.cluster import KMeans
def predict_model(X, y):
ans = KMeans(n_clusters = 3, random_state =42, n_init = "auto").fit(X,y)
return ans.cluster_centers_
Мария Перебейнова, [21.05.2023 0:03]
from sklearn import datasets
def load_split():
res = datasets.load_wine()
return res.data, res.target
Мария Перебейнова, [21.05.2023 0:04]
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from sklearn.datasets import load_iris
def load_data():
res = load_iris()
return preprocessing.StandardScaler().fit_transform(res.data)
Мария Перебейнова, [21.05.2023 0:04]
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from sklearn.datasets import load_iris
def predict_model(data):
dbscan = DBSCAN(eps=0.7, min_samples=2)
dbscan.fit(data)
labels = dbscan.labels_
return set(labels)
Мария Перебейнова, [21.05.2023 0:04]
from sklearn import datasets
def load_split():
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, test_size=0.5, random_state=42)
return X_train[:15], y_train[:15]
Мария Перебейнова, [21.05.2023 0:04]
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
def uniform(X_train, X_test, y_train, y_test):
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
return accuracy
Мария Перебейнова, [21.05.2023 0:05]
это был 4 раздел все задания попорядку
Мария Перебейнова, [21.05.2023 0:05]
сейчас ещё визуализуцию
Мария Перебейнова, [21.05.2023 0:06]
def solve():
fig = plt.figure(figsize=(10,9))
gs = fig.add_gridspec(3,3)
ax1 = fig.add_subplot(gs[:2, :1])
ax2 = fig.add_subplot(gs[:1, 1:])
ax3 = fig.add_subplot(gs[1:2, 1:2])
ax4 = fig.add_subplot(gs[1:2, 2:3])
ax5 = fig.add_subplot(gs[2:, :])
return fig
Мария Перебейнова, [21.05.2023 0:06]
def solve(ax, X, Y, color, title):
minX, maxX = min(X), max(100, max(X))
minY, maxY = min(Y), max(1000, max(Y))
ax.plot(X, Y, color=color)
ax.set_xlim([minX, maxX])
ax.set_ylim([minY, maxY])
ax.set_title(title)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xticks(X)
ax.set_yticks(Y)
ax.grid()
return ax
Мария Перебейнова, [21.05.2023 0:06]
Мария Перебейнова, [21.05.2023 0:06]
def solve(ax, x, y_list, color_list, label_list, title):
ax.set_title(title)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.plot(x, y_list[0], color=color_list[0], marker='s', markersize=5, label=label_list[0], linewidth=10)
ax.plot(x, y_list[1], color=color_list[1], marker='x',linestyle = "dashdot", label=label_list[1], alpha=0.5)
ax.plot(x, y_list[2], color=color_list[2], marker='D', linestyle='dashed', label=label_list[2], markeredgewidth=2, markeredgecolor='black')
ax.legend()
Мария Перебейнова, [21.05.2023 0:07]
Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, df, colors):
df.plot.line(ax=ax,color=colors, subplots = True)
Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x, y):
# Начерчиваем площадную диаграмму
ax.fill_between(x, y, color='green', alpha=0.5)
# Выделяем границы площадной диаграммы линией
ax.plot(x, y, color='black')
# Устанавливаем заголовок графика
ax.set_title('Площадная диаграмма')
# Устанавливаем подпись оси x
ax.set_xlabel('X')
# Устанавливаем подпись оси y
ax.set_ylabel('Y')
# Возвращаем объект типа данных AxesSubplot
return ax
Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x, y1, y2):
ax.plot(x,y1)
ax.plot(x,y2)
ax.fill_between(x,y1,y2, where = (x>2)&(x<5))
Мария Перебейнова, [21.05.2023 0:07]
Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, X, interval_length, colors):
min_value = min(X)
max_value = max(X)
intervals = np.arange(min_value, max_value + interval_length, interval_length)
heights = []
for i in range(len(intervals)):
if i == len(intervals) - 1:
count = len([x for x in X if intervals[i] <= x <= max_value])
else:
count = len([x for x in X if intervals[i] <= x < intervals[i + 1]])
heights.append(count)
mid_points = []
for i in range(len(intervals)):
if i == len(intervals) - 1:
mid_point = 9.0
else:
mid_point = (intervals[i] + intervals[i + 1]) / 2
mid_points.append(mid_point)
ax.bar(mid_points, heights, width=0.5, color=colors[:len(intervals)])
ax.set_xticks(mid_points)
Мария Перебейнова, [21.05.2023 0:07]
def solve(ax, x_centers, heights_left, heights_right):
ax.bar(x_centers-0.2, heights_left, width=0.4, color='orange')
ax.bar(x_centers+0.2, heights_right, width=0.4, color='blue')
ax.set_xticks(x_centers)
Мария Перебейнова, [21.05.2023 0:08]
def solve(ax, y_centers, heights_left, heights_right):
ax.barh(y_centers, heights_left, height=0.7,color = "orange")
ax.barh(y_centers, heights_right, left=heights_left ,height = 0.7, color = "blue")
ax.set_yticks(y_centers)
Мария Перебейнова, [21.05.2023 0:08]
def solve(ax, values, labels, colors):
ax.pie(values, labels=labels, colors=colors, startangle=90, wedgeprops={'linewidth': 2, 'edgecolor': 'black'})
ax.set_title('Pie Chart')
for text in ax.texts:
text.set_color('orange')