def knn(ipaddress="localhost", port="9999"):
'''
用D3.JS展示Knn算法的运行结果
:return:
'''
testNum, errorRate, errorCount, classifierData, realData = knn1.displayData(
'data/edx_knn.csv')
x = np.linspace(0, testNum, testNum)
df = pandas.DataFrame({
'x': x,
'y': classifierData[:testNum],
'z': realData[:testNum],
})
print "testNummber = %d \n" % testNum, "error rate : %f \n" % (
errorCount / float(testNum)), "error count：%d \n" % errorCount
webbrowser.open_new_tab("http://%s:%s/%s.html" %
(ipaddress, port, "disply_knn"))
with d3py.PandasFigure(df,
'disply_knn',
width=20000,
height=200,
port=int(port)) as fig:
fig += d3py.geoms.Line('x', 'y', stroke='BlueViolet')
fig += d3py.geoms.Line('x', 'z', stroke='DeepPink')
fig += d3py.xAxis('x', label="test number")
fig += d3py.yAxis('y', label="test label")
fig.show()
def getLineImg(dish,filename):
conn = pymysql.connect(host='127.0.0.1', port=3306, user='******', passwd='123456', db='biafinal_db')
cur = conn.cursor()
cmd = "select rating,review_count from restaurant_info "
print cmd
cur.execute(cmd)
print(cur.description)
ratings = []
reviews = []
for r in cur:
#print (r[2])
ratings.append(float(r[0]))
reviews.append(float(r[1]))
print ratings ,reviews
#d3py
T = 5*np.pi
x = np.asarray(ratings)
a = 0.05
y = np.exp(-a*x) * np.sin(x)
df = pandas.DataFrame({
'x': x,
'y': y
})
with d3py.PandasFigure(df, 'd3py_line', width=600, height=200) as fig:
fig += d3py.geoms.Line('x', 'y', stroke='BlueViolet')
fig += d3py.xAxis('x')
fig += d3py.yAxis('y')
fig.show(False,filename)
def getD3(self, xlabel="item", ylabel="count"):
df = pd.DataFrame({xlabel:[],ylabel:[]})
p = d3py.PandasFigure(df)
p += d3py.Bar(x = xlabel, y=ylabel)
# p += d3py.Line(x = xlabel, y=ylabel)
p += d3py.xAxis(x = xlabel)
p += d3py.yAxis(y = ylabel)
p.update()
p.js.merge(p.js_geoms)
d3 = {}
d3['js'] = p.js
d3['css'] = "%s\n%s"%(p.css, p.css_geoms)
return d3
def getD3(self, xlabel="item", ylabel="count"):
df = pd.DataFrame({xlabel: [], ylabel: []})
p = d3py.PandasFigure(df)
p += d3py.Bar(x=xlabel, y=ylabel)
# p += d3py.Line(x = xlabel, y=ylabel)
p += d3py.xAxis(x=xlabel)
p += d3py.yAxis(y=ylabel)
p.update()
p.js.merge(p.js_geoms)
d3 = {}
d3['js'] = p.js
d3['css'] = "%s\n%s" % (p.css, p.css_geoms)
return d3
def knn(ipaddress = "localhost",port = "9999"):
'''
用D3.JS展示Knn算法的运行结果
:return:
'''
testNum,errorRate, errorCount, classifierData, realData = knn1.displayData(
'data/edx_knn.csv');
x = np.linspace(0,testNum,testNum)
df = pandas.DataFrame({
'x' : x,
'y' : classifierData[:testNum],
'z' : realData[:testNum],
})
print "testNummber = %d \n" % testNum, "error rate : %f \n" % (errorCount/float(testNum)), "error count：%d \n" % errorCount
webbrowser.open_new_tab("http://%s:%s/%s.html" % (ipaddress, port, "disply_knn"))
with d3py.PandasFigure(df, 'disply_knn', width=20000, height=200, port = int(port)) as fig:
fig += d3py.geoms.Line('x', 'y', stroke='BlueViolet')
fig += d3py.geoms.Line('x', 'z', stroke='DeepPink')
fig += d3py.xAxis('x', label="test number")
fig += d3py.yAxis('y', label="test label")
fig.show()
def getScatterImg(dish,filename):
conn = pymysql.connect(host='127.0.0.1', port=3306, user='******', passwd='123456', db='biafinal_db')
cur = conn.cursor()
cmd = "select rest_name,rating,review_count from restaurant_info where rest_id in"\
"(" \
"select rest_id from rest_category where cate_id in "\
"(SELECT cate_id FROM biafinal_db.category where cate_name like '{0}%')"\
")".format(dish)
print cmd
cur.execute(cmd)
print(cur.description)
ratings = []
reviews = []
for r in cur:
#print (r[2])
ratings.append(r[1])
reviews.append(r[2])
print ratings ,reviews
#d3py
df = pandas.DataFrame({
'd1': ratings,
'd2': reviews
})
with d3py.PandasFigure(df, "example scatter plot using d3py", width=400, height=400) as fig:
fig += d3py.Point("d1", "d2", fill="DodgerBlue")
fig += d3py.xAxis('d1', label="ratio")
fig += d3py.yAxis('d2', label="review count")
fig.show(False,filename)
cur.close()
conn.close()
return filename
from datetime import datetime
import d3py
import pandas
dates = [
datetime(2006, 1, 26, 11, 17, 32),
datetime(2006, 2, 22, 1, 37, 33),
datetime(2006, 3, 16, 1, 30, 11),
datetime(2006, 3, 26, 19, 16, 43),
datetime(2006, 4, 10, 20, 49, 38),
datetime(2006, 4, 25, 22, 5, 23),
datetime(2006, 5, 7, 3, 8, 9)
]
values = [
0, 3.1622776601683795, 6.9039350469423209, 9.9039350469423209,
12.732362171688511, 16.196463786826264, 20.886879546649695
]
df = pandas.DataFrame({'x': dates, 'y': values})
with d3py.Figure(df, 'd3py_line', width=600, height=200) as fig:
fig += d3py.geoms.Line('x', 'y')
fig += d3py.geoms.Point('x', 'y', fill='BlueViolet')
fig += d3py.xAxis('x')
fig += d3py.yAxis('y')
fig.show()
import numpy as np
import d3py
import pandas
T = 5*np.pi
x = np.linspace(-T,T,100)
a = 0.05
y = np.exp(-a*x) * np.sin(x)
df = pandas.DataFrame({
'x' : x,
'y' : y
})
with d3py.Figure(df, 'd3py_line', width=600, height=200) as fig:
fig += d3py.geoms.Line('x', 'y')
fig += d3py.geoms.Point('x', 'y', fill='BlueViolet')
fig += d3py.xAxis('x')
fig += d3py.yAxis('y')
fig.show()
import numpy as np
import pandas
import d3py
n = 400
df = pandas.DataFrame({
'd1': np.arange(0,n),
'd2': np.random.normal(0, 1, n)
})
with d3py.PandasFigure(df, "example scatter plot using d3py", width=400, height=400) as fig:
fig += d3py.Point("d1", "d2", fill="DodgerBlue")
fig += d3py.xAxis('d1', label="Random")
fig += d3py.yAxis('d2', label="Also random")
fig.show()
import pandas
import d3py
import numpy as np
x = np.random.gamma(1, size=100)
count, bins = np.histogram(x)
df = pandas.DataFrame({"count": count, "apple_type": bins[:-1]})
with d3py.Figure(df) as p:
p += d3py.Bar(x="apple_type", y="count", fill="MediumAquamarine")
p += d3py.xAxis(x="apple_type", label="Apple Type")
p += d3py.yAxis(y="count", label="Number")
p.show()
import numpy as np
import pandas
import d3py
n = 400
df = pandas.DataFrame({'d1': np.arange(0, n), 'd2': np.random.normal(0, 1, n)})
with d3py.PandasFigure(df,
"example scatter plot using d3py",
width=400,
height=400) as fig:
fig += d3py.Point("d1", "d2", fill="DodgerBlue")
fig += d3py.xAxis('d1', label="Random")
fig += d3py.yAxis('d2', label="Also random")
fig.show()
import pandas
import d3py
import numpy as np
x = np.random.gamma(1, size=100)
count, bins = np.histogram(x)
df = pandas.DataFrame({
"count" : count,
"apple_type" : bins[:-1]
})
with d3py.Figure(df) as p:
p += d3py.Bar(x="apple_type", y = "count", fill = "MediumAquamarine")
p += d3py.xAxis(x="apple_type", label="Apple Type")
p += d3py.yAxis(y="count", label="Number")
p.show()
import numpy as np
import d3py
import pandas
T = 5 * np.pi
x = np.linspace(-T, T, 100)
a = 0.05
y = np.exp(-a * x) * np.sin(x)
df = pandas.DataFrame({"x": x, "y": y})
with d3py.PandasFigure(df, "d3py_line", width=600, height=200) as fig:
fig += d3py.geoms.Line("x", "y", stroke="BlueViolet")
fig += d3py.xAxis("x")
fig += d3py.yAxis("y")
fig.show()
