Fix typos across multiple files

sts_jax/causal_impact/causal_impact.py

@@ -43,7 +43,7 @@ def plot(self) -> None:
         x = jnp.arange(self.time_series.shape[0])
         fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(9, 6), sharex=True, layout="constrained")
 
-        # Plot the original obvervation and the counterfactual predict
+        # Plot the original observation and the counterfactual predict
         ax1.plot(x, self.time_series, color="black", lw=2, label="Observation")
         ax1.plot(x, self.predict_point, linestyle="dashed", color="blue", lw=2, label="Prediction")
         ax1.fill_between(x, self.predict_interval[0], self.predict_interval[1], color="blue", alpha=0.2)
@@ -90,7 +90,7 @@ def print_summary(self) -> None:
             f'{f"[{pred_l.cumulative:.2f}, {pred_r.cumulative:.2f}]": ^{ncol3}}'
         )
 
-        # Summary statistics of the absolute post-invervention effect
+        # Summary statistics of the absolute post-intervention effect
         abs_e = self.summary["abs_effect"]
         abs_sd = self.summary["abs_effect_sd"]
         abs_l = self.summary["abs_effect_lower"]
@@ -202,7 +202,7 @@ def causal_impact(
     # Fit the STS model, sample from the past and forecast.
     # Model fitting
     params_posterior_samples, _ = sts_model.fit_hmc(num_samples, time_series_pre, covariates=covariates_pre, key=key1)
-    # Sample observations from the posterior predictive sample given paramters of the STS model.
+    # Sample observations from the posterior predictive sample given parameters of the STS model.
     posterior_sample_means, posterior_samples = sts_model.posterior_sample(
         params_posterior_samples, time_series_pre, covariates_pre, key=key2
     )
@@ -266,7 +266,7 @@ def causal_impact(
         average=jnp.std(forecast_samples.mean(axis=1)), cumulative=jnp.std(forecast_samples.sum(axis=1))
     )
 
-    # Summary statistics of the absolute post-invervention effect
+    # Summary statistics of the absolute post-intervention effect
     effect_means = time_series_pos - forecast_means
     effects = time_series_pos - forecast_samples
     summary["abs_effect"] = Stats(average=effect_means.mean(axis=0).mean(), cumulative=effect_means.mean(axis=0).sum())

sts_jax/structural_time_series/learning.py

@@ -49,10 +49,10 @@ def fit_vi(
     which is achieved by written KL(q || p) as expectation over standard normal distribution
     so a sample from q is obstained by
     s = z * exp(log_sigma_k) + mu_k,
-    where z is a sample from the standard multivarate normal distribtion.
+    where z is a sample from the standard multivariate normal distribution.
 
     Args:
-        sample_size (int): number of samples to be returned from the fitted approxiamtion q.
+        sample_size (int): number of samples to be returned from the fitted approximation q.
         M (int): number of fixed samples from q used in evaluation of ELBO.
 
     Returns:

sts_jax/structural_time_series/sts_components.py

@@ -21,27 +21,27 @@
 
 
 class ParamsSTSComponent(OrderedDict):
-    """A :class: 'OrderdedDict' with each item being an instance of :class: 'jax.DeviceArray'."""
+    """A :class: 'OrderedDict' with each item being an instance of :class: 'jax.DeviceArray'."""
 
     pass
 
 
 class ParamsSTS(OrderedDict):
-    """A :class: 'OrderdedDict' with each item being an instance of :class: 'OrderedDict'."""
+    """A :class: 'OrderedDict' with each item being an instance of :class: 'OrderedDict'."""
 
     pass
 
 
 class ParamPropertiesSTS(OrderedDict):
-    """A :class: 'OrderdedDict' with each item being an instance of :class: 'OrderedDict',
+    """A :class: 'OrderedDict' with each item being an instance of :class: 'OrderedDict',
     having the same pytree structure with 'ParamsSTS'.
     """
 
     pass
 
 
 class ParamPriorsSTS(OrderedDict):
-    """A :class: 'OrderdedDict' with each item being an instance of :class: 'OrderedDict',
+    """A :class: 'OrderedDict' with each item being an instance of :class: 'OrderedDict',
     having the same pytree structure with 'ParamsSTS'.
     """
 
@@ -81,7 +81,7 @@ class STSComponent(ABC):
         component at time step $t$.
     * :attr: 'obs_mat' returns the observation (emission) matrix $H$ for the latent component.
     * :attr: 'cov_select_mat' returns the selecting matrix $R$ that expands the nonsingular
-        covariance matrix $Q[t]$ in each time step into a (possibly singular) convarince
+        covariance matrix $Q[t]$ in each time step into a (possibly singular) covariance
         matrix of shape (dim_state, dim_state).
     * :attr: 'name' returns the unique name of the latent component.
     * :attr: 'dim_obs' returns the dimension of the observation in each step of the observed
@@ -130,7 +130,7 @@ def get_trans_mat(self, params: ParamsSTSComponent, t: int) -> Float[Array, "dim
         Args:
             params: parameters of the latent component, having the same tree structure with
             self.params.
-            t: time point at which the transition matrix is to be evaluted.
+            t: time point at which the transition matrix is to be evaluated.
 
         Returns:
             transition matrix, $F[t]$, of the latent component at time step $t$
@@ -145,7 +145,7 @@ def get_trans_cov(self, params: ParamsSTSComponent, t: int) -> Float[Array, "ran
         Args:
             params: parameters of the latent component, having the same tree structure with
             self.params.
-            t: time point at which the transition matrix is to be evaluted.
+            t: time point at which the transition matrix is to be evaluated.
 
         Returns:
             nonsingular covariance matrix, $Q[t]$, of the latent component at time step $t$
@@ -162,7 +162,7 @@ def obs_mat(self) -> Float[Array, "dim_obs dim_state"]:
     @abstractmethod
     def cov_select_mat(self) -> Float[Array, "dim_state rank_state"]:
         r"""Returns the selecting matrix $R$ that expands the nonsingular covariance matrix
-            $Q[t]$ in each time step into a (possibly singular) convarince matrix of shape
+            $Q[t]$ in each time step into a (possibly singular) covariance matrix of shape
             (dim_state, dim_state).
 
         Returns:
@@ -230,11 +230,11 @@ def initialize_params(
     def get_reg_value(
         self, params: ParamsSTSComponent, covariates: Float[Array, "num_timesteps dim_covariates"]
     ) -> Float[Array, "num_timesteps dim_obs"]:
-        r"""Returns the sequence of values of the regression model evaluted at the
+        r"""Returns the sequence of values of the regression model evaluated at the
             given parameters and the sequence of covariates.
 
         Args:
-            params: parameters on which the regression model is to be evalueated.
+            params: parameters on which the regression model is to be evaluated.
             covariates: sequence of covariates at which the regression model is to be evaluated.
 
         Raises:
@@ -249,7 +249,7 @@ def get_reg_value(
 
 
 class LocalLinearTrend(STSComponent):
-    r"""The local linear trend component of the structual time series (STS) model
+    r"""The local linear trend component of the structural time series (STS) model
 
     The latent state has two parts $[level, slope]$, having dimension 2 * dim_obs.
     The dynamics is:
@@ -451,7 +451,7 @@ def cov_select_mat(self) -> Float[Array, "order*dim_obs dim_obs"]:
 class SeasonalDummy(STSComponent):
     r"""The (dummy) seasonal component of the structual time series (STS) model
 
-    Since at any step $t$ the seasonal effect has following constraint
+    Since at any step $t$ the seasonal effect has the following constraint
 
     $$sum_{j=1}^{num_seasons} s_{t-j} = 0 $$,
 
@@ -571,7 +571,7 @@ def cov_select_mat(self) -> Float[Array, "(num_seasons-1)*dim_obs dim_obs"]:
 
 
 class SeasonalTrig(STSComponent):
-    r"""The trigonometric seasonal component of the structual time series (STS) model.
+    r"""The trigonometric seasonal component of the structural time series (STS) model.
 
     The seasonal effect (random) of next time step takes the form:
 
@@ -589,7 +589,7 @@ class SeasonalTrig(STSComponent):
         distribution with mean zeros and a common covariance $drift_cov$ for all $j$ and $t$.
 
     The latent state corresponding to the seasonal component has dimension $(s-1) * dim_obs$.
-    If $s$ is odd, then $s-1 = 2 * (s-1)/2$, which means thare are $j = 1,...,(s-1)/2$ blocks.
+    If $s$ is odd, then $s-1 = 2 * (s-1)/2$, which means there are $j = 1,...,(s-1)/2$ blocks.
     If $s$ is even, then $s-1 = 2 * (s/2) - 1$, which means there are $j = 1,...(s/2)$ blocks,
     but we remove the last dimension in this case since this part does not play role in the
     observation.

sts_jax/structural_time_series/sts_ssm.py

@@ -43,7 +43,7 @@
 
 
 class StructuralTimeSeriesSSM(SSM):
-    """Formulate the structual time series(STS) model into a LinearSSM model,
+    """Formulate the structural time series(STS) model into a LinearSSM model,
     which always have block-diagonal dynamics covariance matrix and fixed transition matrices.
     """
 
@@ -67,7 +67,7 @@ def __init__(
                 parameters of one component.
             param_props: properties of parameters of the STS model, having same tree structure
                 with 'params', each leaf node is a instance of 'ParameterProperties' for the
-                parameter in the conrresponding leaf node of 'params'.
+                parameter in the corresponding leaf node of 'params'.
             param_priors: priors of parameters of the STS model, having same tree structure
                 with 'params', each leaf node is a prior distribution for the parameter in the
                 corresponding leaf node of 'params'.
@@ -143,7 +143,7 @@ def initial_distribution(self):
     def transition_distribution(self, state):
         """This is a must-have method of SSM.
         Not implemented here because tfp.distribution does not support multivariate normal
-        distribution with singular convariance matrix.
+        distribution with singular covariance matrix.
         """
         raise NotImplementedError