def __init__(self, no_components=30, learning_rate=0.05,
                 alpha=0.75, max_count=100, max_loss=10.0,
                 random_state=None):
        """
        Parameters:
        - int no_components: number of latent dimensions
        - float learning_rate: learning rate for SGD estimation.
        - float alpha, float max_count: parameters for the
          weighting function (see the paper).
        - float max_loss: the maximum absolute value of calculated
                          gradient for any single co-occurrence pair.
                          Only try setting to a lower value if you
                          are experiencing problems with numerical
                          stability.
        - random_state: random statue used to intialize optimization
        """

        self.no_components = no_components
        self.learning_rate = float(learning_rate)
        self.alpha = float(alpha)
        self.max_count = float(max_count)
        self.max_loss = max_loss

        self.word_vectors = None
        self.word_biases = None

        self.vectors_sum_gradients = None
        self.biases_sum_gradients = None

        self.dictionary = None
        self.inverse_dictionary = None

        self.random_state = random_state
def __init__ ( self , no_components = 30 , learning_rate = 0.05 , alpha = 0.75 , max_count = 100 , max_loss = 10.0 , random_state = None ) : self . no_components = no_components self . learning_rate = float ( learning_rate ) self . alpha = float ( alpha ) self . max_count = float ( max_count ) self . max_loss = max_loss self . word_vectors = None self . word_biases = None self . vectors_sum_gradients = None self . biases_sum_gradients = None self . dictionary = None self . inverse_dictionary = None self . random_state = random_state
Parameters:
        - int no_components: number of latent dimensions
        - float learning_rate: learning rate for SGD estimation.
        - float alpha, float max_count: parameters for the
          weighting function (see the paper).
        - float max_loss: the maximum absolute value of calculated
                          gradient for any single co-occurrence pair.
                          Only try setting to a lower value if you
                          are experiencing problems with numerical
                          stability.
        - random_state: random statue used to intialize optimization