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train | ListCollectionFieldsResponse._from_dict | Initialize a ListCollectionFieldsResponse object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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args = {}
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return cls(**args) | def _from_dict(cls, _dict):
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train | ListCollectionFieldsResponse._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | ListCollectionsResponse._from_dict | Initialize a ListCollectionsResponse object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | ListCollectionsResponse._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | ListConfigurationsResponse._from_dict | Initialize a ListConfigurationsResponse object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | ListEnvironmentsResponse._from_dict | Initialize a ListEnvironmentsResponse object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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args = {}
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train | ListEnvironmentsResponse._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | LogQueryResponse._from_dict | Initialize a LogQueryResponse object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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args = {}
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train | LogQueryResponse._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | LogQueryResponseResult._from_dict | Initialize a LogQueryResponseResult object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | LogQueryResponseResultDocuments._from_dict | Initialize a LogQueryResponseResultDocuments object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | MetricAggregation._from_dict | Initialize a MetricAggregation object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | TrainingQuery._from_dict | Initialize a TrainingQuery object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | WordHeadingDetection._from_dict | Initialize a WordHeadingDetection object from a json dictionary. | ibm_watson/discovery_v1.py | def _from_dict(cls, _dict):
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train | WordStyle._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | XPathPatterns._to_dict | Return a json dictionary representing this model. | ibm_watson/discovery_v1.py | def _to_dict(self):
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train | ToneAnalyzerV3.tone | Analyze general tone.
Use the general purpose endpoint to analyze the tone of your input content. The
service analyzes the content for emotional and language tones. The method always
analyzes the tone of the full document; by default, it also analyzes the tone of
each individual sentenc... | ibm_watson/tone_analyzer_v3.py | def tone(self,
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tones=None,
content_language=None,
accept_language=None,
content_type=None,
**kwargs):
"""
Analyze general tone.
Use the general purpose endpoint to analyze the ... | def tone(self,
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Analyze general tone.
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train | ToneAnalyzerV3.tone_chat | Analyze customer engagement tone.
Use the customer engagement endpoint to analyze the tone of customer service and
customer support conversations. For each utterance of a conversation, the method
reports the most prevalent subset of the following seven tones: sad, frustrated,
satisfied,... | ibm_watson/tone_analyzer_v3.py | def tone_chat(self,
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Analyze customer engagement tone.
Use the customer engagement endpoint to analyze the tone of customer service and
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Analyze customer engagement tone.
Use the customer engagement endpoint to analyze the tone of customer service and
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train | DocumentAnalysis._from_dict | Initialize a DocumentAnalysis object from a json dictionary. | ibm_watson/tone_analyzer_v3.py | def _from_dict(cls, _dict):
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args = {}
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train | DocumentAnalysis._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
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train | SentenceAnalysis._from_dict | Initialize a SentenceAnalysis object from a json dictionary. | ibm_watson/tone_analyzer_v3.py | def _from_dict(cls, _dict):
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args = {}
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train | SentenceAnalysis._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
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train | ToneAnalysis._from_dict | Initialize a ToneAnalysis object from a json dictionary. | ibm_watson/tone_analyzer_v3.py | def _from_dict(cls, _dict):
"""Initialize a ToneAnalysis object from a json dictionary."""
args = {}
if 'document_tone' in _dict:
args['document_tone'] = DocumentAnalysis._from_dict(
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"""Initialize a ToneAnalysis object from a json dictionary."""
args = {}
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args['document_tone'] = DocumentAnalysis._from_dict(
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else:
raise ValueError(
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train | ToneAnalysis._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'document_tone') and self.document_tone is not None:
_dict['document_tone'] = self.document_tone._to_dict()
if hasattr(self, 'sentences_tone') and self.sentences_tone is no... | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
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_dict['document_tone'] = self.document_tone._to_dict()
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train | ToneCategory._from_dict | Initialize a ToneCategory object from a json dictionary. | ibm_watson/tone_analyzer_v3.py | def _from_dict(cls, _dict):
"""Initialize a ToneCategory object from a json dictionary."""
args = {}
if 'tones' in _dict:
args['tones'] = [
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]
else:
raise ValueError(
... | def _from_dict(cls, _dict):
"""Initialize a ToneCategory object from a json dictionary."""
args = {}
if 'tones' in _dict:
args['tones'] = [
ToneScore._from_dict(x) for x in (_dict.get('tones'))
]
else:
raise ValueError(
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train | ToneCategory._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'tones') and self.tones is not None:
_dict['tones'] = [x._to_dict() for x in self.tones]
if hasattr(self, 'category_id') and self.category_id is not None:
_dict... | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'tones') and self.tones is not None:
_dict['tones'] = [x._to_dict() for x in self.tones]
if hasattr(self, 'category_id') and self.category_id is not None:
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train | ToneChatScore._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
if hasattr(self, 'tone_id') and self.tone_id is not None:
_dict['tone_id'] = self.tone_id
... | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'score') and self.score is not None:
_dict['score'] = self.score
if hasattr(self, 'tone_id') and self.tone_id is not None:
_dict['tone_id'] = self.tone_id
... | [
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train | Utterance._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'user') and self.user is not None:
_dict['user'] = self.user
return _dict | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'user') and self.user is not None:
_dict['user'] = self.user
return _dict | [
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train | UtteranceAnalyses._from_dict | Initialize a UtteranceAnalyses object from a json dictionary. | ibm_watson/tone_analyzer_v3.py | def _from_dict(cls, _dict):
"""Initialize a UtteranceAnalyses object from a json dictionary."""
args = {}
if 'utterances_tone' in _dict:
args['utterances_tone'] = [
UtteranceAnalysis._from_dict(x)
for x in (_dict.get('utterances_tone'))
]
... | def _from_dict(cls, _dict):
"""Initialize a UtteranceAnalyses object from a json dictionary."""
args = {}
if 'utterances_tone' in _dict:
args['utterances_tone'] = [
UtteranceAnalysis._from_dict(x)
for x in (_dict.get('utterances_tone'))
]
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] | watson-developer-cloud/python-sdk | python | https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/tone_analyzer_v3.py#L955-L969 | [
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train | UtteranceAnalyses._to_dict | Return a json dictionary representing this model. | ibm_watson/tone_analyzer_v3.py | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'utterances_tone') and self.utterances_tone is not None:
_dict['utterances_tone'] = [
x._to_dict() for x in self.utterances_tone
]
... | def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'utterances_tone') and self.utterances_tone is not None:
_dict['utterances_tone'] = [
x._to_dict() for x in self.utterances_tone
]
... | [
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] | watson-developer-cloud/python-sdk | python | https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/tone_analyzer_v3.py#L971-L981 | [
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... | 4c2c9df4466fcde88975da9ecd834e6ba95eb353 |
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