在2個字典值之間的平均絕對誤差百分比我的位置的字典,然後屬性值對,像這樣:計算蟒蛇
{"Russia":
{"/location/statistical_region/size_of_armed_forces": 65700.0,
"/location/statistical_region/gni_per_capita_in_ppp_dollars": 42530.0,
"/location/statistical_region/gdp_nominal": 1736050505050.0,
"/location/statistical_region/foreign_direct_investment_net_inflows": 8683048195.0,
"/location/statistical_region/life_expectancy": 80.929, ...
等等,對每一個國家。
,然後將含有單個陣列字典,數組中的每個值是3個鍵的字典:
{
"sentences": [
{
"location-value-pair": {
"Russia": 6.1
},
"parsedSentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in LOCATION_SLOT hit a historic post-Soviet period low of NUMBER_SLOT percent in 2011 , citing final data .",
"sentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in Russia hit a historic post-Soviet period low of 6.1 percent in 2011 , citing final data ."
},
{
"location-value-pair": {
"Russia": 8.8
},
"parsedSentence": "In 2010 , annual inflation in LOCATION_SLOT hit NUMBER_SLOT percent due to the summer drought , exceeding forecasts and equalling the figure for 2009 , the year of the global financial meltdown .",
"sentence": "In 2010 , annual inflation in Russia hit 8.8 percent due to the summer drought , exceeding forecasts and equalling the figure for 2009 , the year of the global financial meltdown ."
}, ...
我想要做的就是比較每個句子,每個位置和價值計算與第一個字典中的位置 - 值對匹配的最接近的匹配值,然後返回其對應的頂部統計屬性,並將其添加爲句子字典的新關鍵字。
例如:
句子1,我看到,我們正在尋找在俄羅斯和6.1的值。我想索引第一本字典,找到「俄羅斯」,並查看所有存在的值,例如65700.0,42530.0,1736050505050.0,8683048195.0。然後,我想找出每個屬性的平均絕對誤差,例如想着當
{
"location-value-pair": {
"Russia": 6.1
},
"predictedRegion": "/location/statistical_region/gni_in_ppp_dollars"
"meanabserror": 2%
"parsedSentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in LOCATION_SLOT hit a historic post-Soviet period low of NUMBER_SLOT percent in 2011 , citing final data .",
"sentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in Russia hit a historic post-Soviet period low of 6.1 percent in 2011 , citing final data ."
},
我的困惑:23%的size_of_armed_forces價值,爲gni_per_capita財產等的話,我想找到10%的最小的一個假設,並將其添加爲重點,以第二字典,所以寫這只是如何訪問另一個字典的鍵值作爲另一個字典的條件。我現在的想法是:
def predictRegion(sentenceArray,trueDict):
absPercentageErrors = {}
for location, property2value in trueDict.items():
print location
absPercentageErrors['location'] = {}
for property,trueValue in property2value.iteritems():
print property
absError = abs(sentenceArray['sentences']['location-value-pair'].key() - trueValue)
absPercentageErrors['location']['property'] = absError/numpy.abs(trueValue)
for index, dataTriples in enumerate(sentenceArray["sentences"]):
for location, trueValue in dataTriples['location-value-pair'].items():
print location
但是很明顯,我不能在此行中訪問sentenceArray['sentences']['location-value-pair'].key()
:absError = abs(sentenceArray['sentences']['location-value-pair'].key() - trueValue)
因爲它是循環之外。
我怎樣才能獲得從環指的是完全不同的變量如此重要呢?
您無緣*最小*在[最小,完整的,並且Verifable](http://stackoverflow.com/help/mcve)實施例部分。你發佈了這樣一本大字典,所有「俄羅斯」的價值都被切斷了,所以你不可能完全理解你想要做什麼。 –
我發佈的第一個字典是一個例子(只有一個國家),但我已經修改它是俄羅斯而不是加拿大,以便更清楚。 –
請進一步修改它,讓你展示**您使用您的示例中的實際數字。**你使用'[23,421,24,412]'但是我沒有看到任何地方的那些當然 –