Python statistics.mode() Examples

The following are 30 code examples of statistics.mode(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module statistics , or try the search function .
Example #1
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #2
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #3
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #4
Source File: ShotgunEnsembleClassifier.py    From SFA_Python with GNU General Public License v3.0 6 votes vote down vote up 
def predictEnsemble(self, models, testSamples):
        uniqueLabels = np.unique(testSamples["Labels"])
        pred_labels = []
        final_labels = [None for _ in range(testSamples["Samples"])]
        for i, model in enumerate(models):
            correct, labels = self.predict(model, testSamples)
            pred_labels.append(labels)

        for i in range(testSamples["Samples"]):
            ensemble_labels = [pred_labels[j][i] for j in range(len(pred_labels))]
            try:
                final_labels[i] = mode(ensemble_labels)
            except: #Guess if there is no favorite
                final_labels[i] = random.choice(uniqueLabels)

        final_correct = sum([final_labels[i] == testSamples[i].label for i in range(testSamples["Samples"])])
        return final_correct, final_labels
Example #5
Source File: average_mode.py    From Python with MIT License 6 votes vote down vote up 
def mode(input_list):  # Defining function "mode."
    """This function returns the mode(Mode as in the measures of
    central tendency) of the input data.

    The input list may contain any Datastructure or any Datatype.

    >>> input_list = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2]
    >>> mode(input_list)
    2
    >>> input_list = [2, 3, 4, 5, 3, 4, 2, 5, 2, 2, 4, 2, 2, 2]
    >>> mode(input_list) == statistics.mode(input_list)
    True
    """
    # Copying input_list to check with the index number later.
    check_list = input_list.copy()
    result = list()  # Empty list to store the counts of elements in input_list
    for x in input_list:
        result.append(input_list.count(x))
        input_list.remove(x)
        y = max(result)  # Gets the maximum value in the result list.
        # Returns the value with the maximum number of repetitions.
        return check_list[result.index(y)]
Example #6
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #7
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #8
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #9
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #10
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #11
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #12
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #13
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #14
Source File: analyze_mod.py    From nlp-services with MIT License 6 votes vote down vote up 
def confidence(self, features):
        """ Calculate the confidence of the result
        :param features:
        :return: confidence
        """

        votes = []
        for c in self._classifiers:
            v = c.classify(features)
            votes.append(v)

        total_of_winner_votes = votes.count(mode(votes))
        conf = total_of_winner_votes / len(votes)
        return conf


# Fetching word features
Example #15
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #16
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #17
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #18
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #19
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #20
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #21
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #22
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #23
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #24
Source File: analyze.py    From TTS with Mozilla Public License 2.0 6 votes vote down vote up 
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data
Example #25
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }
Example #26
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }
Example #27
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }
Example #28
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }
Example #29
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }
Example #30
Source File: analyze.py    From TTS with Mozilla Public License 2.0 5 votes vote down vote up 
def get_data_points(meta_data):
    x = [char_cnt for char_cnt in meta_data]
    y_avg = [meta_data[d]['mean'] for d in meta_data]
    y_mode = [meta_data[d]['mode'] for d in meta_data]
    y_median = [meta_data[d]['median'] for d in meta_data]
    y_std = [meta_data[d]['std'] for d in meta_data]
    y_num_samples = [len(meta_data[d]['data']) for d in meta_data]
    return {
        "x": x,
        "y_avg": y_avg,
        "y_mode": y_mode,
        "y_median": y_median,
        "y_std": y_std,
        "y_num_samples": y_num_samples
    }