本文整理汇总了Python中matplotlib.pylab.scatter方法的典型用法代码示例。如果您正苦于以下问题:Python pylab.scatter方法的具体用法?Python pylab.scatter怎么用?Python pylab.scatter使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.pylab
的用法示例。
在下文中一共展示了pylab.scatter方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot(self, words, num_points=None):
if not num_points:
num_points = len(words)
embeddings = self.get_words_embeddings(words)
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
two_d_embeddings = tsne.fit_transform(embeddings[:num_points, :])
assert two_d_embeddings.shape[0] >= len(words), 'More labels than embeddings'
pylab.figure(figsize=(15, 15)) # in inches
for i, label in enumerate(words[:num_points]):
x, y = two_d_embeddings[i, :]
pylab.scatter(x, y)
pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pylab.show()
示例2: plot_clustering
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_clustering(x, y, title, mx=None, ymax=None, xmin=None, km=None):
pylab.figure(num=None, figsize=(8, 6))
if km:
pylab.scatter(x, y, s=50, c=km.predict(list(zip(x, y))))
else:
pylab.scatter(x, y, s=50)
pylab.title(title)
pylab.xlabel("Occurrence word 1")
pylab.ylabel("Occurrence word 2")
pylab.autoscale(tight=True)
pylab.ylim(ymin=0, ymax=1)
pylab.xlim(xmin=0, xmax=1)
pylab.grid(True, linestyle='-', color='0.75')
return pylab
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:19,代码来源:plot_kmeans_example.py
示例3: plot_stocks
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_stocks(self, freq=252):
"""Plots the Expected annual Returns over annual Volatility of
the stocks of the portfolio.
:Input:
:freq: ``int`` (default: ``252``), number of trading days, default
value corresponds to trading days in a year.
"""
# annual mean returns of all stocks
stock_returns = self.comp_mean_returns(freq=freq)
stock_volatility = self.comp_stock_volatility(freq=freq)
# adding stocks of the portfolio to the plot
# plot stocks individually:
plt.scatter(stock_volatility, stock_returns, marker="o", s=100, label="Stocks")
# adding text to stocks in plot:
for i, txt in enumerate(stock_returns.index):
plt.annotate(
txt,
(stock_volatility[i], stock_returns[i]),
xytext=(10, 0),
textcoords="offset points",
label=i,
)
plt.legend()
示例4: _plot_intensity
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def _plot_intensity(ax, coeffs, upper_bound, lower_bound):
n_coeffs = len(coeffs)
if n_coeffs > 1:
x = np.arange(n_coeffs)
ax.step(x, np.exp(coeffs), label="Estimated RI")
if upper_bound is not None and lower_bound is not None:
ax.fill_between(x, np.exp(lower_bound), np.exp(upper_bound),
alpha=.5, color='orange', step='pre',
label="95% boostrap CI")
elif n_coeffs == 1:
if upper_bound is not None and lower_bound is not None:
ax.errorbar(0, coeffs, yerr=(np.exp(lower_bound),
np.exp(upper_bound)), fmt='o',
ecolor='orange')
else:
ax.scatter([0], np.exp(coeffs), label="Estimated RI")
return ax
# Internals
示例5: tsne_plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def tsne_plot(xs, xt, xs_label, xt_label, subset=True, title=None, pname=None):
num_test=1000
import matplotlib.cm as cm
if subset:
combined_imgs = np.vstack([xs[0:num_test, :], xt[0:num_test, :]])
combined_labels = np.vstack([xs_label[0:num_test, :],xt_label[0:num_test, :]])
combined_labels = combined_labels.astype('int')
combined_domain = np.vstack([np.zeros((num_test,1)),np.ones((num_test,1))])
from sklearn.manifold import TSNE
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=3000)
source_only_tsne = tsne.fit_transform(combined_imgs)
plt.figure(figsize=(15,15))
plt.scatter(source_only_tsne[:num_test,0], source_only_tsne[:num_test,1], c=combined_labels[:num_test].argmax(1),
s=50, alpha=0.5,marker='o', cmap=cm.jet, label='source')
plt.scatter(source_only_tsne[num_test:,0], source_only_tsne[num_test:,1], c=combined_labels[num_test:].argmax(1),
s=50, alpha=0.5,marker='+',cmap=cm.jet,label='target')
plt.axis('off')
plt.legend(loc='best')
plt.title(title)
if filesave:
plt.savefig(os.path.join(pname,title+'.png'),bbox_inches='tight', pad_inches = 0,
format='png')
else:
plt.savefig(title+'.png')
plt.close()
#%% source model
示例6: _plot_mi_func
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def _plot_mi_func(x, y):
mi = mutual_info(x, y)
title = "NI($X_1$, $X_2$) = %.3f" % mi
pylab.scatter(x, y)
pylab.title(title)
pylab.xlabel("$X_1$")
pylab.ylabel("$X_2$")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:10,代码来源:demo_mi.py
示例7: plot_optimal_portfolios
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_optimal_portfolios(self):
"""Plots markers of the optimised portfolios for
- minimum Volatility, and
- maximum Sharpe Ratio.
"""
# compute optimal portfolios
min_vol_weights = self.minimum_volatility(save_weights=False)
max_sharpe_weights = self.maximum_sharpe_ratio(save_weights=False)
# compute return and volatility for each portfolio
min_vol_vals = list(
annualised_portfolio_quantities(
min_vol_weights, self.mean_returns, self.cov_matrix, freq=self.freq
)
)[0:2]
min_vol_vals.reverse()
max_sharpe_vals = list(
annualised_portfolio_quantities(
max_sharpe_weights, self.mean_returns, self.cov_matrix, freq=self.freq
)
)[0:2]
max_sharpe_vals.reverse()
plt.scatter(
min_vol_vals[0],
min_vol_vals[1],
marker="X",
color="g",
s=150,
label="EF min Volatility",
)
plt.scatter(
max_sharpe_vals[0],
max_sharpe_vals[1],
marker="X",
color="r",
s=150,
label="EF max Sharpe Ratio",
)
plt.legend()
示例8: plot_learning_curves
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_learning_curves(self, hyperparameter):
if hyperparameter == "TV":
C = self.C_tv_history
elif hyperparameter == "Group L1":
C = self.C_group_l1_history
else:
raise ValueError("hyperparameter value should be either `TV` or"
" `Group L1`")
x = np.log10(C)
order = np.argsort(x)
m = np.array(self.kfold_mean_train_scores)[order]
sd = np.array(self.kfold_sd_train_scores)[order]
fig = plt.figure()
ax = plt.gca()
p1 = ax.plot(x[order], m)
p2 = ax.fill_between(x[order], m - sd, m + sd, alpha=.3)
min_point_train = np.min(m - sd)
m = np.array(self.kfold_mean_test_scores)[order]
sd = np.array(self.kfold_sd_test_scores)[order]
p3 = ax.plot(x[order], m)
p4 = ax.fill_between(x[order], m - sd, m + sd, alpha=.3)
min_point_test = np.min(m - sd)
min_point = min(min_point_train, min_point_test)
p5 = plt.scatter(np.log10(C), min_point * np.ones_like(C))
ax.legend([(p1[0], p2), (p3[0], p4), p5],
['train score', 'test score', 'tested hyperparameters'],
loc='lower right')
ax.set_title('Learning curves')
ax.set_xlabel('C %s (log scale)' % hyperparameter)
ax.set_ylabel('Loss')
return fig, ax
示例9: plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot(embeddings, labels):
assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
pylab.figure(figsize=(15,15)) # in inches
for i, label in enumerate(labels):
x, y = embeddings[i,:]
pylab.scatter(x, y)
pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pylab.show()
示例10: plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot(embeddings, labels):
assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
pylab.figure(figsize=(15, 15)) # in inches
for i, label in enumerate(labels):
x, y = embeddings[i, :]
pylab.scatter(x, y)
pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pylab.show()
示例11: plot_simple_demo_1
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_simple_demo_1():
pylab.clf()
fig = pylab.figure(num=None, figsize=(10, 4))
pylab.subplot(121)
title = "Original feature space"
pylab.title(title)
pylab.xlabel("$X_1$")
pylab.ylabel("$X_2$")
x1 = np.arange(0, 10, .2)
x2 = x1 + np.random.normal(scale=1, size=len(x1))
good = (x1 > 5) | (x2 > 5)
bad = ~good
x1g = x1[good]
x2g = x2[good]
pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")
x1b = x1[bad]
x2b = x2[bad]
pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")
pylab.grid(True)
pylab.subplot(122)
X = np.c_[(x1, x2)]
pca = decomposition.PCA(n_components=1)
Xtrans = pca.fit_transform(X)
Xg = Xtrans[good]
Xb = Xtrans[bad]
pylab.scatter(
Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
pylab.scatter(
Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
title = "Transformed feature space"
pylab.title(title)
pylab.xlabel("$X'$")
fig.axes[1].get_yaxis().set_visible(False)
print(pca.explained_variance_ratio_)
pylab.grid(True)
pylab.autoscale(tight=True)
filename = "pca_demo_1.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:54,代码来源:demo_pca.py
示例12: plot_simple_demo_2
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_simple_demo_2():
pylab.clf()
fig = pylab.figure(num=None, figsize=(10, 4))
pylab.subplot(121)
title = "Original feature space"
pylab.title(title)
pylab.xlabel("$X_1$")
pylab.ylabel("$X_2$")
x1 = np.arange(0, 10, .2)
x2 = x1 + np.random.normal(scale=1, size=len(x1))
good = x1 > x2
bad = ~good
x1g = x1[good]
x2g = x2[good]
pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")
x1b = x1[bad]
x2b = x2[bad]
pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")
pylab.grid(True)
pylab.subplot(122)
X = np.c_[(x1, x2)]
pca = decomposition.PCA(n_components=1)
Xtrans = pca.fit_transform(X)
Xg = Xtrans[good]
Xb = Xtrans[bad]
pylab.scatter(
Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
pylab.scatter(
Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
title = "Transformed feature space"
pylab.title(title)
pylab.xlabel("$X'$")
fig.axes[1].get_yaxis().set_visible(False)
print(pca.explained_variance_ratio_)
pylab.grid(True)
pylab.autoscale(tight=True)
filename = "pca_demo_2.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:54,代码来源:demo_pca.py
示例13: plot_simple_demo_lda
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plot_simple_demo_lda():
pylab.clf()
fig = pylab.figure(num=None, figsize=(10, 4))
pylab.subplot(121)
title = "Original feature space"
pylab.title(title)
pylab.xlabel("$X_1$")
pylab.ylabel("$X_2$")
good = x1 > x2
bad = ~good
x1g = x1[good]
x2g = x2[good]
pylab.scatter(x1g, x2g, edgecolor="blue", facecolor="blue")
x1b = x1[bad]
x2b = x2[bad]
pylab.scatter(x1b, x2b, edgecolor="red", facecolor="white")
pylab.grid(True)
pylab.subplot(122)
X = np.c_[(x1, x2)]
lda_inst = lda.LDA(n_components=1)
Xtrans = lda_inst.fit_transform(X, good)
Xg = Xtrans[good]
Xb = Xtrans[bad]
pylab.scatter(
Xg[:, 0], np.zeros(len(Xg)), edgecolor="blue", facecolor="blue")
pylab.scatter(
Xb[:, 0], np.zeros(len(Xb)), edgecolor="red", facecolor="white")
title = "Transformed feature space"
pylab.title(title)
pylab.xlabel("$X'$")
fig.axes[1].get_yaxis().set_visible(False)
pylab.grid(True)
pylab.autoscale(tight=True)
filename = "lda_demo.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:49,代码来源:demo_pca.py
示例14: plotScatter
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def plotScatter(self, xList, yList, saveFigPath):
'''
根据特征数据 xList 及其类别 yList 绘制散点图,并将绘制出的
散点图保存在 saveFigPath 路径下。
:param xList: 样本特征
:param yList: 样本类别
:param saveFigPath: 保存散点图的路径
:return:
'''
# 判断特征是否大于等于二维
# 如果样本的特征大于等于 2
# 那么仅可视化前面 2 维度的数据
if len(xList[0]) >= 2:
x1List = map(lambda x: x[0], xList)
x2List = map(lambda x: x[1], xList)
else:
# 1 或 2 维数据都可视化为 2 维
x1List = x2List = map(lambda x: x[0], xList)
# 新建画布
scatterFig= plt.figure(saveFigPath)
# 预定义:颜色初始化
colorDict = {-1: 'm', 1: 'r', 2: 'b', 3: 'pink', 4: 'orange'}
# 绘制每个点
map(lambda idx: \
plt.scatter(x1List[idx], \
x2List[idx], \
marker='o', \
color=colorDict[yList[idx]], \
label=yList[idx]), \
xrange(len(x1List)))
# 给每种类别加上标注
# ySet = set(yList)
# map(lambda y: \
# plt.legend(str(y), \
# loc='best'), \
# ySet)
# 设定其他属性并保存图像后显示
plt.title(saveFigPath)
plt.xlabel(r'$x^1$')
plt.ylabel(r'$x^2$')
plt.grid(True)
plt.savefig(saveFigPath)
plt.show()
示例15: fwhm_vs_time_plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import scatter [as 别名]
def fwhm_vs_time_plot(self, extraction, data):
"""create fwhm plot"""
logging.info('create FWHM plot')
fwhm_filename = os.path.join(self.conf.diagnostics_path,
'.diagnostics',
'fwhm.'+self.conf.image_file_format)
frame_midtimes = np.array([frame['time'] for frame in extraction])
fwhm = [np.median(frame['catalog_data']['FWHM_IMAGE'])
for frame in extraction]
fwhm_sig = [np.std(frame['catalog_data']['FWHM_IMAGE'])
for frame in extraction]
fig, ax = plt.subplots()
ax.set_title('Median PSF FWHM per Frame')
ax.set_xlabel('Minutes after {:s} UT'.format(
Time(frame_midtimes.min(), format='jd',
out_subfmt='date_hm').iso))
ax.set_ylabel('Point Source FWHM (px)')
ax.scatter((frame_midtimes-frame_midtimes.min())*1440,
fwhm, marker='o',
color='black')
xrange = [plt.xlim()[0], plt.xlim()[1]]
ax.plot(xrange, [data['optimum_aprad']*2, data['optimum_aprad']*2],
color='blue')
ax.set_xlim(xrange)
ax.set_ylim([0, max([data['optimum_aprad']*2+1, max(fwhm)])])
ax.grid()
fig.savefig(fwhm_filename, dpi=self.conf.plot_dpi,
format=self.conf.image_file_format)
data['fwhm_filename'] = fwhm_filename
# create html map
if self.conf.individual_frame_pages:
data['fwhm_map'] = ""
for i in range(len(extraction)):
x, y = ax.transData.transform_point(
[((frame_midtimes-frame_midtimes.min())*1440)[i],
fwhm[i]])
filename = extraction[i]['fits_filename']
data['fwhm_map'] += (
'<area shape="circle" coords="{:.1f},{:.1f},{:.1f}" '
'href="{:s}#{:s}" alt="{:s}" title="{:s}">\n').format(
x, fig.bbox.height - y, 5,
os.path.join(self.conf.diagnostics_path,
'.diagnostics', filename+'.html'),
'',
filename, filename)
logging.info('FWHM plot created')